(intro music) >> Good afternoon guys and gals. Welcome back to The Strip, Las Vegas. It's "theCUBE" live day four of our coverage of "AWS re:Invent". Lisa Martin, Dave Vellante. Dave, we've had some awesome conversations the last four days. I can't believe how many people are still here. The AWS ecosystem seems stronger than ever. >> Yeah, last year we really noted the ecosystem, you know, coming out of the isolation economy 'cause everybody had this old pent up demand to get together and the ecosystem, even last year, we were like, "Wow." This year's like 10x wow. >> It really is 10x wow, it feels that way. We're going to have a 10x wow conversation next. We're bringing back DataStax to "theCUBE". Please welcome Thomas Bean, it's CMO. Thomas welcome to "theCUBE". >> Thanks, thanks a lot, thanks for having me. >> Great to have you, talk to us about what's going on at DataStax, it's been a little while since we talked to you guys. >> Indeed, so DataStax, we are the realtime data company and we've always been involved in technology such as "Apache Cassandra". We actually created to support and take this, this great technology to the market. And now we're taking it, combining it with other technologies such as "Apache Pulse" for streaming to provide a realtime data cloud. Which helps our users, our customers build applications faster and help them scale without limits. So it's all about mobilizing all of this information that is going to drive the application going to create the awesome experience, when you have a customer waiting behind their mobile phone, when you need a decision to take place immediately to, that's the kind of data that we, that we provide in the cloud on any cloud, but especially with, with AWS and providing the performance that technologies like "Apache Cassandra" are known for but also with market leading unit economics. So really empowering customers to operate at speed and scale. >> Speaking of customers, nobody wants less data slower. And one of the things I think we learned in the in the pan, during the pandemic was that access to realtime data isn't nice to have anymore for any business. It is table stakes, it's competitive advantage. There's somebody right behind in the rear view mirror ready to take over. How has the business model of DataStax maybe evolved in the last couple of years with the fact that realtime data is so critical? >> Realtime data has been around for some time but it used to be really niches. You needed a lot of, a lot of people a lot of funding actually to, to implement these, these applications. So we've adapted to really democratize it, made super easy to access. Not only to start developing but also scaling. So this is why we've taken these great technologies made them serverless cloud native on the cloud so that developers could really start easily and scale. So that be on project products could be taken to the, to the market. And in terms of customers, the patterns is we've seen enterprise customers, you were talking about the pandemic, the Home Depot as an example was able to deliver curbside pickup delivery in 30 days because they were already using DataStax and could adapt their business model with a real time application that combines you were just driving by and you would get the delivery of what exactly you ordered without having to go into the the store. So they shifted their whole business model. But we also see a real strong trend about customer experiences and increasingly a lot of tech companies coming because scale means success to them and building on, on our, on our stack to, to build our applications. >> So Lisa, it's interesting. DataStax and "theCUBE" were started the same year, 2010, and that's when it was the beginning of the ascendancy of the big data era. But of course back then there was, I mean very little cloud. I mean most of it was on-prem. And so data stacks had, you know, had obviously you mentioned a number of things that you had to do to become cloud friendly. >> Thomas: Yes. >> You know, a lot of companies didn't make it, make it through. You guys just raised a bunch of dough as well last summer. And so that's been quite a transformation both architecturally, you know, bringing the customers through. I presume part of that was because you had such a great open source community, but also you have a unique value problem. Maybe you could sort of describe that a little. >> Absolutely, so the, I'll start with the open source community where we see a lot of traction at the, at the moment. We were always very involved with, with the "Apache Cassandra". But what we're seeing right now with "Apache Cassandra" is, is a lot of traction, gaining momentum. We actually, we, the open source community just won an award, did an AMA, had a, a vote from their readers about the top open source projects and "Apache Cassandra" and "Apache Pulse" are part of the top three, which is, which is great. We also run a, in collaboration with the Apache Project, the, a series of events around the, around the globe called "Cassandra Days" where we had tremendous attendance. We, some of them, we had to change venue twice because there were more people coming. A lot of students, a lot of the big users of Cassandra like Apple, Netflix who spoke at these, at these events. So we see this momentum actually picking up and that's why we're also super excited that the Linux Foundation is running the Cassandra Summit in in March in San Jose. Super happy to bring that even back with the rest of the, of the community and we have big announcements to come. "Apache Cassandra" will, will see its next version with major advances such as the support of asset transactions, which is going to make it even more suitable to more use cases. So we're bringing that scale to more applications. So a lot of momentum in terms of, in terms of the, the open source projects. And to your point about the value proposition we take this great momentum to which we contribute a lot. It's not only about taking, it's about giving as well. >> Dave: Big committers, I mean... >> Exactly big contributors. And we also have a lot of expertise, we worked with all of the members of the community, many of them being our customers. So going to the cloud, indeed there was architectural work making Cassandra cloud native putting it on Kubernetes, having the right APIs for developers to, to easily develop on top of it. But also becoming a cloud company, building customer success, our own platform engineering. We, it's interesting because actually we became like our partners in a community. We now operate Cassandra in the cloud so that all of our customers can benefit from all the power of Cassandra but really efficiently, super rapidly, and also with a, the leading unit economies as I mentioned. >> How will the, the asset compliance affect your, you know, new markets, new use cases, you know, expand your TAM, can you explain that? >> I think it will, more applications will be able to tap into the power of, of "NoSQL". Today we see a lot on the customer experience as IOT, gaming platform, a lot of SaaS companies. But now with the ability to have transactions at the database level, we can, beyond providing information, we can go even deeper into the logic of the, of the application. So it makes Cassandra and therefore Astra which is our cloud service an even more suitable database we can address, address more even in terms of the transaction that the application itself will, will support. >> What are some of the business benefits that Cassandra delivers to customers in terms of business outcomes helping businesses really transform? >> So Cassandra brings skill when you have millions of customers, when you have million of data points to go through to serve each of the customers. One of my favorite example is Priceline, who runs entirely on our cloud service. You may see one offer, but it's actually everything they know about you and everything they have to offer matched while you are refreshing your page. This is the kind of power that Cassandra provide. But the thing to say about "Apache Cassandra", it used to be also a database that was a bit hard to manage and hard to develop with. This is why as part of the cloud, we wanted to change these aspects, provide developers the API they like and need and what the application need. Making it super simple to operate and, and, and super affordable, also cost effective to, to run. So the the value to your point, it's time to market. You go faster, you don't have to worry when you choose the right database you're not going to, going to have to change horse in the middle of the river, like sixth month down the line. And you know, you have the guarantee that you're going to get the performance and also the best, the best TCO which matters a lot. I think your previous person talking was addressing it. That's also important especially in the, in a current context. >> As a managed service, you're saying, that's the enabler there, right? >> Thomas: Exactly. >> Dave: That is the model today. I mean, you have to really provide that for customers. They don't want to mess with, you know, all the plumbing, right? I mean... >> Absolutely, I don't think people want to manage databases anymore, we do that very well. We take SLAs and such and even at the developer level what they want is an API so they get all the power. All of of this powered by Cassandra, but now they get it as a, and it's as simple as using as, as an API. >> How about the ecosystem? You mentioned the show in in San Jose in March and the Linux Foundation is, is hosting that, is that correct? >> Yes, absolutely. >> And what is it, Cassandra? >> Cassandra Summit. >> Dave: Cassandra Summit >> Yep. >> What's the ecosystem like today in Cassandra, can you just sort of describe that? >> Around Cassandra, you have actually the big hyperscalers. You have also a few other companies that are supporting Cassandra like technologies. And what's interesting, and that's been a, a something we've worked on but also the "Apache Project" has worked on. Working on a lot of the adjacent technologies, the data pipelines, all of the DevOps solutions to make sure that you can actually put Cassandra as part of your way to build these products and, and build these, these applications. So the, the ecosystem keeps on, keeps on growing and actually the, the Cassandra community keeps on opening the database so that it's, it's really easy to have it connect to the rest of the, the rest environment. And we benefit from all of this in our Astra cloud service. >> So things like machine learning, governance tools that's what you would expect in the ecosystem forming around it, right? So we'll see that in March. >> Machine learning is especially a very interesting use case. We see more and more of it. We recently did a, a nice video with one of our customers called Unifour who does exactly this using also our abstract cloud service. What they provide is they analyze videos of sales calls and they help actually the sellers telling them, "Okay here's what happened here was the customer sentiment". Because they have proof that the better the sentiment is, the shorter the sell cycle is going to be. So they teach the, the sellers on how to say the right things, how to control the thing. This is machine learning applied on video. Cassandra provides I think 200 data points per second that feeds this machine learning. And we see more and more of these use cases, realtime use cases. It happens on the fly when you are on your phone, when you have a, a fraud maybe to detect and to prevent. So it is going to be more and more and we see more and more of these integration at the open source level with technologies like even "Feast" project like "Apache Feast". But also in the, in, in the partners that we're working with integrating our Cassandra and our cloud service with. >> Where are customer conversations these days, given that every company has to be a data company. They have to be able to, to democratize data, allow access to it deep into the, into the organizations. Not just IT or the data organization anymore. But are you finding that the conversations are rising up the, up the stack? Is this, is this a a C-suite priority? Is this a board level conversation? >> So that's an excellent question. We actually ran a survey this summer called "The State of the Database" where we, we asked these tech leaders, okay what's top of mind for you? And real time actually was, was really one of the top priorities. And they explained for the one that who call themselves digital leaders that for 71% of them they could correlate directly the use of realtime data, the quality of their experience or their decision making with revenue. And that's really where the discussion is. And I think it's something we can relate to as users. We don't want the, I mean if the Starbucks apps take seconds to to respond there will be a riot over there. So that's, that's something we can feel. But it really, now it's tangible in, in business terms and now then they take a look at their data strategy, are we equipped? Very often they will see, yeah, we have pockets of realtime data, but we're not really able to leverage it. >> Lisa: Yeah. >> For ML use cases, et cetera. So that's a big trend that we're seeing on one end. On the other end, what we're seeing, and it's one of the things we discussed a lot at the event is that yeah cost is important. Growth at all, at all cost does not exist. So we see a lot of push on moving a lot of the workloads to the cloud to make them scale but at the best the best cost. And we also see some organizations where like, okay let's not let a good crisis go to waste and let's accelerate our innovation not at all costs. So that we see also a lot of new projects being being pushed but reasonable, starting small and, and growing and all of this fueled by, by realtime data, so interesting. >> The other big topic amongst the, the customer community is security. >> Yep. >> I presume it's coming up a lot. What's the conversation like with DataStax? >> That's a topic we've been working on intensely since the creation of Astra less than two years ago. And we keep on reinforcing as any, any cloud provider not only our own abilities in terms of making sure that customers can manage their own keys, et cetera. But also integrating to the rest of the, of the ecosystem when some, a lot of our customers are running on AWS, how do we integrate with PrivateLink and such? We fit exactly into their security environment on AWS and they use exactly the same management tool. Because this is also what used to cost a lot in the cloud services. How much do you have to do to wire them and, and manage. And there are indeed compliance and governance challenges. So that's why making sure that it's fully connected that they have full transparency on what's happening is, is a big part of the evolution. It's always, security is always something you're working on but it's, it's a major topic for us. >> Yep, we talk about that on pretty much every event. Security, which we could dive into, but we're out of time. Last question for you. >> Thomas: Yes. >> We're talking before we went live, we're both big Formula One fans. Say DataStax has the opportunity to sponsor a team and you get the whole side pod to, to put like a phrase about DataStax on the side pod of this F1 car. (laughter) Like a billboard, what does it say? >> Billboard, because an F1 car goes pretty fast, it will be hard to, be hard to read but, "Twice the performance at half the cost, try Astra a cloud service." >> Drop the mike. Awesome, Thomas, thanks so much for joining us. >> Thank for having me. >> Pleasure having you guys on the program. For our guest, Thomas Bean and Dave Vellante, I'm Lisa Martin and you're watching "theCUBE" live from day four of our coverage. "theCUBE", the leader in live tech coverage. (outro music)

>>Individuals create developers, translate ideas to code, to create great applications and great applications. Touch everyone. A Docker. We know that collaboration is key to your innovation sharing ideas, working together. Launching the most secure applications. Docker is with you wherever your team innovates, whether it be robots or autonomous cars, we're doing research to save lives during a pandemic, revolutionizing, how to buy and sell goods online, or even going into the unknown frontiers of space. Docker is launching innovation everywhere. Join us on the journey to build, share, run the future. >>Hello and welcome to Docker con 2021. We're incredibly excited to have more than 80,000 of you join us today from all over the world. As it was last year, this year at DockerCon is 100% virtual and 100% free. So as to enable as many community members as possible to join us now, 100%. Virtual is also an acknowledgement of the continuing global pandemic in particular, the ongoing tragedies in India and Brazil, the Docker community is a global one. And on behalf of all Dr. Khan attendees, we are donating $10,000 to UNICEF support efforts to fight the virus in those countries. Now, even in those regions of the world where the pandemic is being brought under control, virtual first is the new normal. It's been a challenging transition. This includes our team here at Docker. And we know from talking with many of you that you and your developer teams are challenged by this as well. So to help application development teams better collaborate and ship faster, we've been working on some powerful new features and we thought it would be fun to start off with a demo of those. How about it? Want to have a look? All right. Then no further delay. I'd like to introduce Youi Cal and Ben, gosh, over to you and Ben >>Morning, Ben, thanks for jumping on real quick. >>Have you seen the email from Scott? The one about updates and the docs landing page Smith, the doc combat and more prominence. >>Yeah. I've got something working on my local machine. I haven't committed anything yet. I was thinking we could try, um, that new Docker dev environments feature. >>Yeah, that's cool. So if you hit the share button, what I should do is it will take all of your code and the dependencies and the image you're basing it on and wrap that up as one image for me. And I can then just monitor all my machines that have been one click, like, and then have it side by side, along with the changes I've been looking at as well, because I was also having a bit of a look and then I can really see how it differs to what I'm doing. Maybe I can combine it to do the best of both worlds. >>Sounds good. Uh, let me get that over to you, >>Wilson. Yeah. If you pay with the image name, I'll get that started up. >>All right. Sen send it over >>Cheesy. Okay, great. Let's have a quick look at what you he was doing then. So I've been messing around similar to do with the batter. I've got movie at the top here and I think it looks pretty cool. Let's just grab that image from you. Pick out that started on a dev environment. What this is doing. It's just going to grab the image down, which you can take all of the code, the dependencies only get brunches working on and I'll get that opened up in my idea. Ready to use. It's a here close. We can see our environment as my Molly image, just coming down there and I've got my new idea. >>We'll load this up and it'll just connect to my dev environment. There we go. It's connected to the container. So we're working all in the container here and now give it a moment. What we'll do is we'll see what changes you've been making as well on the code. So it's like she's been working on a landing page as well, and it looks like she's been changing the banner as well. So let's get this running. Let's see what she's actually doing and how it looks. We'll set up our checklist and then we'll see how that works. >>Great. So that's now rolling. So let's just have a look at what you use doing what changes she had made. Compare those to mine just jumped back into my dev container UI, see that I've got both of those running side by side with my changes and news changes. Okay. So she's put Molly up there rather than mobi or somebody had the same idea. So I think in a way I can make us both happy. So if we just jumped back into what we'll do, just add Molly and Moby and here I'll save that. And what we can see is, cause I'm just working within the container rather than having to do sort of rebuild of everything or serve, or just reload my content. No, that's straight the page. So what I can then do is I can come up with my browser here. Once that's all refreshed, refresh the page once hopefully, maybe twice, we should then be able to see your refresh it or should be able to see that we get Malia mobi come up. So there we go, got Molly mobi. So what we'll do now is we'll describe that state. It sends us our image and then we'll just create one of those to share with URI or share. And we'll get a link for that. I guess we'll send that back over to you. >>So I've had a look at what you were doing and I'm actually going to change. I think that might work for both of us. I wondered if you could take a look at it. If I send it over. >>Sounds good. Let me grab the link. >>Yeah, it's a dev environment link again. So if you just open that back in the doc dashboard, it should be able to open up the code that I've changed and then just run it in the same way you normally do. And that shouldn't interrupt what you're already working on because there'll be able to run side by side with your other brunch. You already got, >>Got it. Got it. Loading here. Well, that's great. It's Molly and movie together. I love it. I think we should ship it. >>Awesome. I guess it's chip it and get on with the rest of.com. Wasn't that cool. Thank you Joey. Thanks Ben. Everyone we'll have more of this later in the keynote. So stay tuned. Let's say earlier, we've all been challenged by this past year, whether the COVID pandemic, the complete evaporation of customer demand in many industries, unemployment or business bankruptcies, we all been touched in some way. And yet, even to miss these tragedies last year, we saw multiple sources of hope and inspiration. For example, in response to COVID we saw global communities, including the tech community rapidly innovate solutions for analyzing the spread of the virus, sequencing its genes and visualizing infection rates. In fact, if all in teams collaborating on solutions for COVID have created more than 1,400 publicly shareable images on Docker hub. As another example, we all witnessed the historic landing and exploration of Mars by the perseverance Rover and its ingenuity drone. >>Now what's common in these examples, these innovative and ambitious accomplishments were made possible not by any single individual, but by teams of individuals collaborating together. The power of teams is why we've made development teams central to Docker's mission to build tools and content development teams love to help them get their ideas from code to cloud as quickly as possible. One of the frictions we've seen that can slow down to them in teams is that the path from code to cloud can be a confusing one, riddle with multiple point products, tools, and images that need to be integrated and maintained an automated pipeline in order for teams to be productive. That's why a year and a half ago we refocused Docker on helping development teams make sense of all this specifically, our goal is to provide development teams with the trusted content, the sharing capabilities and the pipeline integrations with best of breed third-party tools to help teams ship faster in short, to provide a collaborative application development platform. >>Everything a team needs to build. Sharon run create applications. Now, as I noted earlier, it's been a challenging year for everyone on our planet and has been similar for us here at Docker. Our team had to adapt to working from home local lockdowns caused by the pandemic and other challenges. And despite all this together with our community and ecosystem partners, we accomplished many exciting milestones. For example, in open source together with the community and our partners, we open sourced or made major contributions to many projects, including OCI distribution and the composed plugins building on these open source projects. We had powerful new capabilities to the Docker product, both free and subscription. For example, support for WSL two and apple, Silicon and Docker, desktop and vulnerability scanning audit logs and image management and Docker hub. >>And finally delivering an easy to use well-integrated development experience with best of breed tools and content is only possible through close collaboration with our ecosystem partners. For example, this last year we had over 100 commercialized fees, join our Docker verified publisher program and over 200 open source projects, join our Docker sponsored open source program. As a result of these efforts, we've seen some exciting growth in the Docker community in the 12 months since last year's Docker con for example, the number of registered developers grew 80% to over 8 million. These developers created many new images increasing the total by 56% to almost 11 million. And the images in all these repositories were pulled by more than 13 million monthly active IP addresses totaling 13 billion pulls a month. Now while the growth is exciting by Docker, we're even more excited about the stories we hear from you and your development teams about how you're using Docker and its impact on your businesses. For example, cancer researchers and their bioinformatics development team at the Washington university school of medicine needed a way to quickly analyze their clinical trial results and then share the models, the data and the analysis with other researchers they use Docker because it gives them the ease of use choice of pipeline tools and speed of sharing so critical to their research. And most importantly to the lives of their patients stay tuned for another powerful customer story later in the keynote from Matt fall, VP of engineering at Oracle insights. >>So with this last year behind us, what's next for Docker, but challenge you this last year of force changes in how development teams work, but we felt for years to come. And what we've learned in our discussions with you will have long lasting impact on our product roadmap. One of the biggest takeaways from those discussions that you and your development team want to be quicker to adapt, to changes in your environment so you can ship faster. So what is DACA doing to help with this first trusted content to own the teams that can focus their energies on what is unique to their businesses and spend as little time as possible on undifferentiated work are able to adapt more quickly and ship faster in order to do so. They need to be able to trust other components that make up their app together with our partners. >>Docker is doubling down and providing development teams with trusted content and the tools they need to use it in their applications. Second, remote collaboration on a development team, asking a coworker to take a look at your code used to be as easy as swiveling their chair around, but given what's happened in the last year, that's no longer the case. So as you even been hinted in the demo at the beginning, you'll see us deliver more capabilities for remote collaboration within a development team. And we're enabling development team to quickly adapt to any team configuration all on prem hybrid, all work from home, helping them remain productive and focused on shipping third ecosystem integrations, those development teams that can quickly take advantage of innovations throughout the ecosystem. Instead of getting locked into a single monolithic pipeline, there'll be the ones able to deliver amps, which impact their businesses faster. >>So together with our ecosystem partners, we are investing in more integrations with best of breed tools, right? Integrated automated app pipelines. Furthermore, we'll be writing more public API APIs and SDKs to enable ecosystem partners and development teams to roll their own integrations. We'll be sharing more details about remote collaboration and ecosystem integrations. Later in the keynote, I'd like to take a moment to share with Docker and our partners are doing for trusted content, providing development teams, access to content. They can trust, allows them to focus their coding efforts on what's unique and differentiated to that end Docker and our partners are bringing more and more trusted content to Docker hub Docker official images are 160 images of popular upstream open source projects that serve as foundational building blocks for any application. These include operating systems, programming, languages, databases, and more. Furthermore, these are updated patch scan and certified frequently. So I said, no image is older than 30 days. >>Docker verified publisher images are published by more than 100 commercialized feeds. The image Rebos are explicitly designated verify. So the developers searching for components for their app know that the ISV is actively maintaining the image. Docker sponsored open source projects announced late last year features images for more than 200 open source communities. Docker sponsors these communities through providing free storage and networking resources and offering their community members unrestricted access repos for businesses allow businesses to update and share their apps privately within their organizations using role-based access control and user authentication. No, and finally, public repos for communities enable community projects to be freely shared with anonymous and authenticated users alike. >>And for all these different types of content, we provide services for both development teams and ISP, for example, vulnerability scanning and digital signing for enhanced security search and filtering for discoverability packaging and updating services and analytics about how these products are being used. All this trusted content, we make available to develop teams for them directly to discover poll and integrate into their applications. Our goal is to meet development teams where they live. So for those organizations that prefer to manage their internal distribution of trusted content, we've collaborated with leading container registry partners. We announced our partnership with J frog late last year. And today we're very pleased to announce our partnerships with Amazon and Miranda's for providing an integrated seamless experience for joint for our joint customers. Lastly, the container images themselves and this end to end flow are built on open industry standards, which provided all the teams with flexibility and choice trusted content enables development teams to rapidly build. >>As I let them focus on their unique differentiated features and use trusted building blocks for the rest. We'll be talking more about trusted content as well as remote collaboration and ecosystem integrations later in the keynote. Now ecosystem partners are not only integral to the Docker experience for development teams. They're also integral to a great DockerCon experience, but please join me in thanking our Dr. Kent on sponsors and checking out their talks throughout the day. I also want to thank some others first up Docker team. Like all of you this last year has been extremely challenging for us, but the Docker team rose to the challenge and worked together to continue shipping great product, the Docker community of captains, community leaders, and contributors with your welcoming newcomers, enthusiasm for Docker and open exchanges of best practices and ideas talker, wouldn't be Docker without you. And finally, our development team customers. >>You trust us to help you build apps. Your businesses rely on. We don't take that trust for granted. Thank you. In closing, we often hear about the tenant's developer capable of great individual feeds that can transform project. But I wonder if we, as an industry have perhaps gotten this wrong by putting so much emphasis on weight, on the individual as discussed at the beginning, great accomplishments like innovative responses to COVID-19 like landing on Mars are more often the results of individuals collaborating together as a team, which is why our mission here at Docker is delivered tools and content developers love to help their team succeed and become 10 X teams. Thanks again for joining us, we look forward to having a great DockerCon with you today, as well as a great year ahead of us. Thanks and be well. >>Hi, I'm Dana Lawson, VP of engineering here at get hub. And my job is to enable this rich interconnected community of builders and makers to build even more and hopefully have a great time doing it in order to enable the best platform for developers, which I know is something we are all passionate about. We need to partner across the ecosystem to ensure that developers can have a great experience across get hub and all the tools that they want to use. No matter what they are. My team works to build the tools and relationships to make that possible. I am so excited to join Scott on this virtual stage to talk about increasing developer velocity. So let's dive in now, I know this may be hard for some of you to believe, but as a former CIS admin, some 21 years ago, working on sense spark workstations, we've come such a long way for random scripts and desperate systems that we've stitched together to this whole inclusive developer workflow experience being a CIS admin. >>Then you were just one piece of the siloed experience, but I didn't want to just push code to production. So I created scripts that did it for me. I taught myself how to code. I was the model lazy CIS admin that got dangerous and having pushed a little too far. I realized that working in production and building features is really a team sport that we had the opportunity, all of us to be customer obsessed today. As developers, we can go beyond the traditional dev ops mindset. We can really focus on adding value to the customer experience by ensuring that we have work that contributes to increasing uptime via and SLS all while being agile and productive. We get there. When we move from a pass the Baton system to now having an interconnected developer workflow that increases velocity in every part of the cycle, we get to work better and smarter. >>And honestly, in a way that is so much more enjoyable because we automate away all the mundane and manual and boring tasks. So we get to focus on what really matters shipping, the things that humans get to use and love. Docker has been a big part of enabling this transformation. 10, 20 years ago, we had Tomcat containers, which are not Docker containers. And for y'all hearing this the first time go Google it. But that was the way we built our applications. We had to segment them on the server and give them resources. Today. We have Docker containers, these little mini Oasys and Docker images. You can do it multiple times in an orchestrated manner with the power of actions enabled and Docker. It's just so incredible what you can do. And by the way, I'm showing you actions in Docker, which I hope you use because both are great and free for open source. >>But the key takeaway is really the workflow and the automation, which you certainly can do with other tools. Okay, I'm going to show you just how easy this is, because believe me, if this is something I can learn and do anybody out there can, and in this demo, I'll show you about the basic components needed to create and use a package, Docker container actions. And like I said, you won't believe how awesome the combination of Docker and actions is because you can enable your workflow to do no matter what you're trying to do in this super baby example. We're so small. You could take like 10 seconds. Like I am here creating an action due to a simple task, like pushing a message to your logs. And the cool thing is you can use it on any the bit on this one. Like I said, we're going to use push. >>You can do, uh, even to order a pizza every time you roll into production, if you wanted, but at get hub, that'd be a lot of pizzas. And the funny thing is somebody out there is actually tried this and written that action. If you haven't used Docker and actions together, check out the docs on either get hub or Docker to get you started. And a huge shout out to all those doc writers out there. I built this demo today using those instructions. And if I can do it, I know you can too, but enough yapping let's get started to save some time. And since a lot of us are Docker and get hub nerds, I've already created a repo with a Docker file. So we're going to skip that step. Next. I'm going to create an action's Yammel file. And if you don't Yammer, you know, actions, the metadata defines my important log stuff to capture and the input and my time out per parameter to pass and puts to the Docker container, get up a build image from your Docker file and run the commands in a new container. >>Using the Sigma image. The cool thing is, is you can use any Docker image in any language for your actions. It doesn't matter if it's go or whatever in today's I'm going to use a shell script and an input variable to print my important log stuff to file. And like I said, you know me, I love me some. So let's see this action in a workflow. When an action is in a private repo, like the one I demonstrating today, the action can only be used in workflows in the same repository, but public actions can be used by workflows in any repository. So unfortunately you won't get access to the super awesome action, but don't worry in the Guild marketplace, there are over 8,000 actions available, especially the most important one, that pizza action. So go try it out. Now you can do this in a couple of ways, whether you're doing it in your preferred ID or for today's demo, I'm just going to use the gooey. I'm going to navigate to my actions tab as I've done here. And I'm going to in my workflow, select new work, hello, probably load some workflows to Claire to get you started, but I'm using the one I've copied. Like I said, the lazy developer I am in. I'm going to replace it with my action. >>That's it. So now we're going to go and we're going to start our commitment new file. Now, if we go over to our actions tab, we can see the workflow in progress in my repository. I just click the actions tab. And because they wrote the actions on push, we can watch the visualization under jobs and click the job to see the important stuff we're logging in the input stamp in the printed log. And we'll just wait for this to run. Hello, Mona and boom. Just like that. It runs automatically within our action. We told it to go run as soon as the files updated because we're doing it on push merge. That's right. Folks in just a few minutes, I built an action that writes an entry to a log file every time I push. So I don't have to do it manually. In essence, with automation, you can be kind to your future self and save time and effort to focus on what really matters. >>Imagine what I could do with even a little more time, probably order all y'all pieces. That is the power of the interconnected workflow. And it's amazing. And I hope you all go try it out, but why do we care about all of that? Just like in the demo, I took a manual task with both tape, which both takes time and it's easy to forget and automated it. So I don't have to think about it. And it's executed every time consistently. That means less time for me to worry about my human errors and mistakes, and more time to focus on actually building the cool stuff that people want. Obviously, automation, developer productivity, but what is even more important to me is the developer happiness tools like BS, code actions, Docker, Heroku, and many others reduce manual work, which allows us to focus on building things that are awesome. >>And to get into that wonderful state that we call flow. According to research by UC Irvine in Humboldt university in Germany, it takes an average of 23 minutes to enter optimal creative state. What we call the flow or to reenter it after distraction like your dog on your office store. So staying in flow is so critical to developer productivity and as a developer, it just feels good to be cranking away at something with deep focus. I certainly know that I love that feeling intuitive collaboration and automation features we built in to get hub help developer, Sam flow, allowing you and your team to do so much more, to bring the benefits of automation into perspective in our annual October's report by Dr. Nicole, Forsgren. One of my buddies here at get hub, took a look at the developer productivity in the stork year. You know what we found? >>We found that public GitHub repositories that use the Automational pull requests, merge those pull requests. 1.2 times faster. And the number of pooled merged pull requests increased by 1.3 times, that is 34% more poor requests merged. And other words, automation can con can dramatically increase, but the speed and quantity of work completed in any role, just like an open source development, you'll work more efficiently with greater impact when you invest the bulk of your time in the work that adds the most value and eliminate or outsource the rest because you don't need to do it, make the machines by elaborate by leveraging automation in their workflows teams, minimize manual work and reclaim that time for innovation and maintain that state of flow with development and collaboration. More importantly, their work is more enjoyable because they're not wasting the time doing the things that the machines or robots can do for them. >>And I remember what I said at the beginning. Many of us want to be efficient, heck even lazy. So why would I spend my time doing something I can automate? Now you can read more about this research behind the art behind this at October set, get hub.com, which also includes a lot of other cool info about the open source ecosystem and how it's evolving. Speaking of the open source ecosystem we at get hub are so honored to be the home of more than 65 million developers who build software together for everywhere across the globe. Today, we're seeing software development taking shape as the world's largest team sport, where development teams collaborate, build and ship products. It's no longer a solo effort like it was for me. You don't have to take my word for it. Check out this globe. This globe shows real data. Every speck of light you see here represents a contribution to an open source project, somewhere on earth. >>These arts reach across continents, cultures, and other divides. It's distributed collaboration at its finest. 20 years ago, we had no concept of dev ops, SecOps and lots, or the new ops that are going to be happening. But today's development and ops teams are connected like ever before. This is only going to continue to evolve at a rapid pace, especially as we continue to empower the next hundred million developers, automation helps us focus on what's important and to greatly accelerate innovation. Just this past year, we saw some of the most groundbreaking technological advancements and achievements I'll say ever, including critical COVID-19 vaccine trials, as well as the first power flight on Mars. This past month, these breakthroughs were only possible because of the interconnected collaborative open source communities on get hub and the amazing tools and workflows that empower us all to create and innovate. Let's continue building, integrating, and automating. So we collectively can give developers the experience. They deserve all of the automation and beautiful eye UIs that we can muster so they can continue to build the things that truly do change the world. Thank you again for having me today, Dr. Khan, it has been a pleasure to be here with all you nerds. >>Hello. I'm Justin. Komack lovely to see you here. Talking to developers, their world is getting much more complex. Developers are being asked to do everything security ops on goal data analysis, all being put on the rockers. Software's eating the world. Of course, and this all make sense in that view, but they need help. One team. I told you it's shifted all our.net apps to run on Linux from windows, but their developers found the complexity of Docker files based on the Linux shell scripts really difficult has helped make these things easier for your teams. Your ones collaborate more in a virtual world, but you've asked us to make this simpler and more lightweight. You, the developers have asked for a paved road experience. You want things to just work with a simple options to be there, but it's not just the paved road. You also want to be able to go off-road and do interesting and different things. >>Use different components, experiments, innovate as well. We'll always offer you both those choices at different times. Different developers want different things. It may shift for ones the other paved road or off road. Sometimes you want reliability, dependability in the zone for day to day work, but sometimes you have to do something new, incorporate new things in your pipeline, build applications for new places. Then you knew those off-road abilities too. So you can really get under the hood and go and build something weird and wonderful and amazing. That gives you new options. Talk as an independent choice. We don't own the roads. We're not pushing you into any technology choices because we own them. We're really supporting and driving open standards, such as ISEI working opensource with the CNCF. We want to help you get your applications from your laptops, the clouds, and beyond, even into space. >>Let's talk about the key focus areas, that frame, what DACA is doing going forward. These are simplicity, sharing, flexibility, trusted content and care supply chain compared to building where the underlying kernel primitives like namespaces and Seagraves the original Docker CLI was just amazing Docker engine. It's a magical experience for everyone. It really brought those innovations and put them in a world where anyone would use that, but that's not enough. We need to continue to innovate. And it was trying to get more done faster all the time. And there's a lot more we can do. We're here to take complexity away from deeply complicated underlying things and give developers tools that are just amazing and magical. One of the area we haven't done enough and make things magical enough that we're really planning around now is that, you know, Docker images, uh, they're the key parts of your application, but you know, how do I do something with an image? How do I, where do I attach volumes with this image? What's the API. Whereas the SDK for this image, how do I find an example or docs in an API driven world? Every bit of software should have an API and an API description. And our vision is that every container should have this API description and the ability for you to understand how to use it. And it's all a seamless thing from, you know, from your code to the cloud local and remote, you can, you can use containers in this amazing and exciting way. >>One thing I really noticed in the last year is that companies that started off remote fast have constant collaboration. They have zoom calls, apron all day terminals, shattering that always working together. Other teams are really trying to learn how to do this style because they didn't start like that. We used to walk around to other people's desks or share services on the local office network. And it's very difficult to do that anymore. You want sharing to be really simple, lightweight, and informal. Let me try your container or just maybe let's collaborate on this together. Um, you know, fast collaboration on the analysts, fast iteration, fast working together, and he wants to share more. You want to share how to develop environments, not just an image. And we all work by seeing something someone else in our team is doing saying, how can I do that too? I can, I want to make that sharing really, really easy. Ben's going to talk about this more in the interest of one minute. >>We know how you're excited by apple. Silicon and gravis are not excited because there's a new architecture, but excited because it's faster, cooler, cheaper, better, and offers new possibilities. The M one support was the most asked for thing on our public roadmap, EFA, and we listened and share that we see really exciting possibilities, usership arm applications, all the way from desktop to production. We know that you all use different clouds and different bases have deployed to, um, you know, we work with AWS and Azure and Google and more, um, and we want to help you ship on prime as well. And we know that you use huge number of languages and the containers help build applications that use different languages for different parts of the application or for different applications, right? You can choose the best tool. You have JavaScript hat or everywhere go. And re-ask Python for data and ML, perhaps getting excited about WebAssembly after hearing about a cube con, you know, there's all sorts of things. >>So we need to make that as easier. We've been running the whole month of Python on the blog, and we're doing a month of JavaScript because we had one specific support about how do I best put this language into production of that language into production. That detail is important for you. GPS have been difficult to use. We've added GPS suppose in desktop for windows, but we know there's a lot more to do to make the, how multi architecture, multi hardware, multi accelerator world work better and also securely. Um, so there's a lot more work to do to support you in all these things you want to do. >>How do we start building a tenor has applications, but it turns out we're using existing images as components. I couldn't assist survey earlier this year, almost half of container image usage was public images rather than private images. And this is growing rapidly. Almost all software has open source components and maybe 85% of the average application is open source code. And what you're doing is taking whole container images as modules in your application. And this was always the model with Docker compose. And it's a model that you're already et cetera, writing you trust Docker, official images. We know that they might go to 25% of poles on Docker hub and Docker hub provides you the widest choice and the best support that trusted content. We're talking to people about how to make this more helpful. We know, for example, that winter 69 four is just showing us as support, but the image doesn't yet tell you that we're working with canonical to improve messaging from specific images about left lifecycle and support. >>We know that you need more images, regularly updated free of vulnerabilities, easy to use and discover, and Donnie and Marie neuro, going to talk about that more this last year, the solar winds attack has been in the, in the news. A lot, the software you're using and trusting could be compromised and might be all over your organization. We need to reduce the risk of using vital open-source components. We're seeing more software supply chain attacks being targeted as the supply chain, because it's often an easier place to attack and production software. We need to be able to use this external code safely. We need to, everyone needs to start from trusted sources like photography images. They need to scan for known vulnerabilities using Docker scan that we built in partnership with sneak and lost DockerCon last year, we need just keep updating base images and dependencies, and we'll, we're going to help you have the control and understanding about your images that you need to do this. >>And there's more, we're also working on the nursery V2 project in the CNCF to revamp container signings, or you can tell way or software comes from we're working on tooling to make updates easier, and to help you understand and manage all the principals carrier you're using security is a growing concern for all of us. It's really important. And we're going to help you work with security. We can't achieve all our dreams, whether that's space travel or amazing developer products ever see without deep partnerships with our community to cloud is RA and the cloud providers aware most of you ship your occasion production and simple routes that take your work and deploy it easily. Reliably and securely are really important. Just get into production simply and easily and securely. And we've done a bunch of work on that. And, um, but we know there's more to do. >>The CNCF on the open source cloud native community are an amazing ecosystem of creators and lovely people creating an amazing strong community and supporting a huge amount of innovation has its roots in the container ecosystem and his dreams beyond that much of the innovation is focused around operate experience so far, but developer experience is really a growing concern in that community as well. And we're really excited to work on that. We also uses appraiser tool. Then we know you do, and we know that you want it to be easier to use in your environment. We just shifted Docker hub to work on, um, Kubernetes fully. And, um, we're also using many of the other projects are Argo from atheists. We're spending a lot of time working with Microsoft, Amazon right now on getting natural UV to ready to ship in the next few. That's a really detailed piece of collaboration we've been working on for a long term. Long time is really important for our community as the scarcity of the container containers and, um, getting content for you, working together makes us stronger. Our community is made up of all of you have. Um, it's always amazing to be reminded of that as a huge open source community that we already proud to work with. It's an amazing amount of innovation that you're all creating and where perhaps it, what with you and share with you as well. Thank you very much. And thank you for being here. >>Really excited to talk to you today and share more about what Docker is doing to help make you faster, make your team faster and turn your application delivery into something that makes you a 10 X team. What we're hearing from you, the developers using Docker everyday fits across three common themes that we hear consistently over and over. We hear that your time is super important. It's critical, and you want to move faster. You want your tools to get out of your way, and instead to enable you to accelerate and focus on the things you want to be doing. And part of that is that finding great content, great application components that you can incorporate into your apps to move faster is really hard. It's hard to discover. It's hard to find high quality content that you can trust that, you know, passes your test and your configuration needs. >>And it's hard to create good content as well. And you're looking for more safety, more guardrails to help guide you along that way so that you can focus on creating value for your company. Secondly, you're telling us that it's a really far to collaborate effectively with your team and you want to do more, to work more effectively together to help your tools become more and more seamless to help you stay in sync, both with yourself across all of your development environments, as well as with your teammates so that you can more effectively collaborate together. Review each other's work, maintain things and keep them in sync. And finally, you want your applications to run consistently in every single environment, whether that's your local development environment, a cloud-based development environment, your CGI pipeline, or the cloud for production, and you want that micro service to provide that consistent experience everywhere you go so that you have similar tools, similar environments, and you don't need to worry about things getting in your way, but instead things make it easy for you to focus on what you wanna do and what Docker is doing to help solve all of these problems for you and your colleagues is creating a collaborative app dev platform. >>And this collaborative application development platform consists of multiple different pieces. I'm not going to walk through all of them today, but the overall view is that we're providing all the tooling you need from the development environment, to the container images, to the collaboration services, to the pipelines and integrations that enable you to focus on making your applications amazing and changing the world. If we start zooming on a one of those aspects, collaboration we hear from developers regularly is that they're challenged in synchronizing their own setups across environments. They want to be able to duplicate the setup of their teammates. Look, then they can easily get up and running with the same applications, the same tooling, the same version of the same libraries, the same frameworks. And they want to know if their applications are good before they're ready to share them in an official space. >>They want to collaborate on things before they're done, rather than feeling like they have to officially published something before they can effectively share it with others to work on it, to solve this. We're thrilled today to announce Docker, dev environments, Docker, dev environments, transform how your team collaborates. They make creating, sharing standardized development environments. As simple as a Docker poll, they make it easy to review your colleagues work without affecting your own work. And they increase the reproducibility of your own work and decreased production issues in doing so because you've got consistent environments all the way through. Now, I'm going to pass it off to our principal product manager, Ben Gotch to walk you through more detail on Docker dev environments. >>Hi, I'm Ben. I work as a principal program manager at DACA. One of the areas that doc has been looking at to see what's hard today for developers is sharing changes that you make from the inner loop where the inner loop is a better development, where you write code, test it, build it, run it, and ultimately get feedback on those changes before you merge them and try and actually ship them out to production. Most amount of us build this flow and get there still leaves a lot of challenges. People need to jump between branches to look at each other's work. Independence. Dependencies can be different when you're doing that and doing this in this new hybrid wall of work. Isn't any easier either the ability to just save someone, Hey, come and check this out. It's become much harder. People can't come and sit down at your desk or take your laptop away for 10 minutes to just grab and look at what you're doing. >>A lot of the reason that development is hard when you're remote, is that looking at changes and what's going on requires more than just code requires all the dependencies and everything you've got set up and that complete context of your development environment, to understand what you're doing and solving this in a remote first world is hard. We wanted to look at how we could make this better. Let's do that in a way that let you keep working the way you do today. Didn't want you to have to use a browser. We didn't want you to have to use a new idea. And we wanted to do this in a way that was application centric. We wanted to let you work with all the rest of the application already using C for all the services and all those dependencies you need as part of that. And with that, we're excited to talk more about docket developer environments, dev environments are new part of the Docker experience that makes it easier you to get started with your whole inner leap, working inside a container, then able to share and collaborate more than just the code. >>We want it to enable you to share your whole modern development environment, your whole setup from DACA, with your team on any operating system, we'll be launching a limited beta of dev environments in the coming month. And a GA dev environments will be ID agnostic and supporting composts. This means you'll be able to use an extend your existing composed files to create your own development environment in whatever idea, working in dev environments designed to be local. First, they work with Docker desktop and say your existing ID, and let you share that whole inner loop, that whole development context, all of your teammates in just one collect. This means if you want to get feedback on the working progress change or the PR it's as simple as opening another idea instance, and looking at what your team is working on because we're using compose. You can just extend your existing oppose file when you're already working with, to actually create this whole application and have it all working in the context of the rest of the services. >>So it's actually the whole environment you're working with module one service that doesn't really understand what it's doing alone. And with that, let's jump into a quick demo. So you can see here, two dev environments up and running. First one here is the same container dev environment. So if I want to go into that, let's see what's going on in the various code button here. If that one open, I can get straight into my application to start making changes inside that dev container. And I've got all my dependencies in here, so I can just run that straight in that second application I have here is one that's opened up in compose, and I can see that I've also got my backend, my front end and my database. So I've got all my services running here. So if I want, I can open one or more of these in a dev environment, meaning that that container has the context that dev environment has the context of the whole application. >>So I can get back into and connect to all the other services that I need to test this application properly, all of them, one unit. And then when I've made my changes and I'm ready to share, I can hit my share button type in the refund them on to share that too. And then give that image to someone to get going, pick that up and just start working with that code and all my dependencies, simple as putting an image, looking ahead, we're going to be expanding development environments, more of your dependencies for the whole developer worst space. We want to look at backing up and letting you share your volumes to make data science and database setups more repeatable and going. I'm still all of this under a single workspace for your team containing images, your dev environments, your volumes, and more we've really want to allow you to create a fully portable Linux development environment. >>So everyone you're working with on any operating system, as I said, our MVP we're coming next month. And that was for vs code using their dev container primitive and more support for other ideas. We'll follow to find out more about what's happening and what's coming up next in the future of this. And to actually get a bit of a deeper dive in the experience. Can we check out the talk I'm doing with Georgie and girl later on today? Thank you, Ben, amazing story about how Docker is helping to make developer teams more collaborative. Now I'd like to talk more about applications while the dev environment is like the workbench around what you're building. The application itself has all the different components, libraries, and frameworks, and other code that make up the application itself. And we hear developers saying all the time things like, how do they know if their images are good? >>How do they know if they're secure? How do they know if they're minimal? How do they make great images and great Docker files and how do they keep their images secure? And up-to-date on every one of those ties into how do I create more trust? How do I know that I'm building high quality applications to enable you to do this even more effectively than today? We are pleased to announce the DACA verified polisher program. This broadens trusted content by extending beyond Docker official images, to give you more and more trusted building blocks that you can incorporate into your applications. It gives you confidence that you're getting what you expect because Docker verifies every single one of these publishers to make sure they are who they say they are. This improves our secure supply chain story. And finally it simplifies your discovery of the best building blocks by making it easy for you to find things that you know, you can trust so that you can incorporate them into your applications and move on and on the right. You can see some examples of the publishers that are involved in Docker, official images and our Docker verified publisher program. Now I'm pleased to introduce you to marina. Kubicki our senior product manager who will walk you through more about what we're doing to create a better experience for you around trust. >>Thank you, Dani, >>Mario Andretti, who is a famous Italian sports car driver. One said that if everything feels under control, you're just not driving. You're not driving fast enough. Maya Andretti is not a software developer and a software developers. We know that no matter how fast we need to go in order to drive the innovation that we're working on, we can never allow our applications to spin out of control and a Docker. As we continue talking to our, to the developers, what we're realizing is that in order to reach that speed, the developers are the, the, the development community is looking for the building blocks and the tools that will, they will enable them to drive at the speed that they need to go and have the trust in those building blocks. And in those tools that they will be able to maintain control over their applications. So as we think about some of the things that we can do to, to address those concerns, uh, we're realizing that we can pursue them in a number of different venues, including creating reliable content, including creating partnerships that expands the options for the reliable content. >>Um, in order to, in a we're looking at creating integrations, no link security tools, talk about the reliable content. The first thing that comes to mind are the Docker official images, which is a program that we launched several years ago. And this is a set of curated, actively maintained, open source images that, uh, include, uh, operating systems and databases and programming languages. And it would become immensely popular for, for, for creating the base layers of, of the images of, of the different images, images, and applications. And would we realizing that, uh, many developers are, instead of creating something from scratch, basically start with one of the official images for their basis, and then build on top of that. And this program has become so popular that it now makes up a quarter of all of the, uh, Docker poles, which essentially ends up being several billion pulse every single month. >>As we look beyond what we can do for the open source. Uh, we're very ability on the open source, uh, spectrum. We are very excited to announce that we're launching the Docker verified publishers program, which is continuing providing the trust around the content, but now working with, uh, some of the industry leaders, uh, in multiple, in multiple verticals across the entire technology technical spec, it costs entire, uh, high tech in order to provide you with more options of the images that you can use for building your applications. And it still comes back to trust that when you are searching for content in Docker hub, and you see the verified publisher badge, you know, that this is, this is the content that, that is part of the, that comes from one of our partners. And you're not running the risk of pulling the malicious image from an employee master source. >>As we look beyond what we can do for, for providing the reliable content, we're also looking at some of the tools and the infrastructure that we can do, uh, to create a security around the content that you're creating. So last year at the last ad, the last year's DockerCon, we announced partnership with sneak. And later on last year, we launched our DACA, desktop and Docker hub vulnerability scans that allow you the options of writing scans in them along multiple points in your dev cycle. And in addition to providing you with information on the vulnerability on, on the vulnerabilities, in, in your code, uh, it also provides you with a guidance on how to re remediate those vulnerabilities. But as we look beyond the vulnerability scans, we're also looking at some of the other things that we can do, you know, to, to, to, uh, further ensure that the integrity and the security around your images, your images, and with that, uh, later on this year, we're looking to, uh, launch the scope, personal access tokens, and instead of talking about them, I will simply show you what they look like. >>So if you can see here, this is my page in Docker hub, where I've created a four, uh, tokens, uh, read-write delete, read, write, read only in public read in public creeper read only. So, uh, earlier today I went in and I, I logged in, uh, with my read only token. And when you see, when I'm going to pull an image, it's going to allow me to pull an image, not a problem success. And then when I do the next step, I'm going to ask to push an image into the same repo. Uh, would you see is that it's going to give me an error message saying that they access is denied, uh, because there is an additional authentication required. So these are the things that we're looking to add to our roadmap. As we continue thinking about the things that we can do to provide, um, to provide additional building blocks, content, building blocks, uh, and, and, and tools to build the trust so that our DACA developer and skinned code faster than Mario Andretti could ever imagine. Uh, thank you to >>Thank you, marina. It's amazing what you can do to improve the trusted content so that you can accelerate your development more and move more quickly, move more collaboratively and build upon the great work of others. Finally, we hear over and over as that developers are working on their applications that they're looking for, environments that are consistent, that are the same as production, and that they want their applications to really run anywhere, any environment, any architecture, any cloud one great example is the recent announcement of apple Silicon. We heard from developers on uproar that they needed Docker to be available for that architecture before they could add those to it and be successful. And we listened. And based on that, we are pleased to share with you Docker, desktop on apple Silicon. This enables you to run your apps consistently anywhere, whether that's developing on your team's latest dev hardware, deploying an ARM-based cloud environments and having a consistent architecture across your development and production or using multi-year architecture support, which enables your whole team to collaborate on its application, using private repositories on Docker hub, and thrilled to introduce you to Hughie cower, senior director for product management, who will walk you through more of what we're doing to create a great developer experience. >>Senior director of product management at Docker. And I'd like to jump straight into a demo. This is the Mac mini with the apple Silicon processor. And I want to show you how you can now do an end-to-end arm workflow from my M one Mac mini to raspberry PI. As you can see, we have vs code and Docker desktop installed on a, my, the Mac mini. I have a small example here, and I have a raspberry PI three with an led strip, and I want to turn those LEDs into a moving rainbow. This Dockerfile here, builds the application. We build the image with the Docker, build X command to make the image compatible for all raspberry pies with the arm. 64. Part of this build is built with the native power of the M one chip. I also add the push option to easily share the image with my team so they can give it a try to now Dr. >>Creates the local image with the application and uploads it to Docker hub after we've built and pushed the image. We can go to Docker hub and see the new image on Docker hub. You can also explore a variety of images that are compatible with arm processors. Now let's go to the raspberry PI. I have Docker already installed and it's running Ubuntu 64 bit with the Docker run command. I can run the application and let's see what will happen from there. You can see Docker is downloading the image automatically from Docker hub and when it's running, if it's works right, there are some nice colors. And with that, if we have an end-to-end workflow for arm, where continuing to invest into providing you a great developer experience, that's easy to install. Easy to get started with. As you saw in the demo, if you're interested in the new Mac, mini are interested in developing for our platforms in general, we've got you covered with the same experience you've come to expect from Docker with over 95,000 arm images on hub, including many Docker official images. >>We think you'll find what you're looking for. Thank you again to the community that helped us to test the tech previews. We're so delighted to hear when folks say that the new Docker desktop for apple Silicon, it just works for them, but that's not all we've been working on. As Dani mentioned, consistency of developer experience across environments is so important. We're introducing composed V2 that makes compose a first-class citizen in the Docker CLI you no longer need to install a separate composed biter in order to use composed, deploying to production is simpler than ever with the new compose integration that enables you to deploy directly to Amazon ECS or Azure ACI with the same methods you use to run your application locally. If you're interested in running slightly different services, when you're debugging versus testing or, um, just general development, you can manage that all in one place with the new composed service to hear more about what's new and Docker desktop, please join me in the three 15 breakout session this afternoon. >>And now I'd love to tell you a bit more about bill decks and convince you to try it. If you haven't already it's our next gen build command, and it's no longer experimental as shown in the demo with built X, you'll be able to do multi architecture builds, share those builds with your team and the community on Docker hub. With build X, you can speed up your build processes with remote caches or build all the targets in your composed file in parallel with build X bake. And there's so much more if you're using Docker, desktop or Docker, CE you can use build X checkout tonus is talk this afternoon at three 45 to learn more about build X. And with that, I hope everyone has a great Dr. Khan and back over to you, Donnie. >>Thank you UA. It's amazing to hear about what we're doing to create a better developer experience and make sure that Docker works everywhere you need to work. Finally, I'd like to wrap up by showing you everything that we've announced today and everything that we've done recently to make your lives better and give you more and more for the single price of your Docker subscription. We've announced the Docker verified publisher program we've announced scoped personal access tokens to make it easier for you to have a secure CCI pipeline. We've announced Docker dev environments to improve your collaboration with your team. Uh, we shared with you Docker, desktop and apple Silicon, to make sure that, you know, Docker runs everywhere. You need it to run. And we've announced Docker compose version two, finally making it a first-class citizen amongst all the other great Docker tools. And we've done so much more recently as well from audit logs to advanced image management, to compose service profiles, to improve where you can run Docker more easily. >>Finally, as we look forward, where we're headed in the upcoming year is continuing to invest in these themes of helping you build, share, and run modern apps more effectively. We're going to be doing more to help you create a secure supply chain with which only grows more and more important as time goes on. We're going to be optimizing your update experience to make sure that you can easily understand the current state of your application, all its components and keep them all current without worrying about breaking everything as you're doing. So we're going to make it easier for you to synchronize your work. Using cloud sync features. We're going to improve collaboration through dev environments and beyond, and we're going to do make it easy for you to run your microservice in your environments without worrying about things like architecture or differences between those environments. Thank you so much. I'm thrilled about what we're able to do to help make your lives better. And now you're going to be hearing from one of our customers about what they're doing to launch their business with Docker >>I'm Matt Falk, I'm the head of engineering and orbital insight. And today I want to talk to you a little bit about data from space. So who am I like many of you, I'm a software developer and a software developer about seven companies so far, and now I'm a head of engineering. So I spend most of my time doing meetings, but occasionally I'll still spend time doing design discussions, doing code reviews. And in my free time, I still like to dabble on things like project oiler. So who's Oberlin site. What do we do? Portal insight is a large data supplier and analytics provider where we take data geospatial data anywhere on the planet, any overhead sensor, and translate that into insights for the end customer. So specifically we have a suite of high performance, artificial intelligence and machine learning analytics that run on this geospatial data. >>And we build them to specifically determine natural and human service level activity anywhere on the planet. What that really means is we take any type of data associated with a latitude and longitude and we identify patterns so that we can, so we can detect anomalies. And that's everything that we do is all about identifying those patterns to detect anomalies. So more specifically, what type of problems do we solve? So supply chain intelligence, this is one of the use cases that we we'd like to talk about a lot. It's one of our main primary verticals that we go after right now. And as Scott mentioned earlier, this had a huge impact last year when COVID hit. So specifically supply chain intelligence is all about identifying movement patterns to and from operating facilities to identify changes in those supply chains. How do we do this? So for us, we can do things where we track the movement of trucks. >>So identifying trucks, moving from one location to another in aggregate, same thing we can do with foot traffic. We can do the same thing for looking at aggregate groups of people moving from one location to another and analyzing their patterns of life. We can look at two different locations to determine how people are moving from one location to another, or going back and forth. All of this is extremely valuable for detecting how a supply chain operates and then identifying the changes to that supply chain. As I said last year with COVID, everything changed in particular supply chains changed incredibly, and it was hugely important for customers to know where their goods or their products are coming from and where they were going, where there were disruptions in their supply chain and how that's affecting their overall supply and demand. So to use our platform, our suite of tools, you can start to gain a much better picture of where your suppliers or your distributors are going from coming from or going to. >>So what's our team look like? So my team is currently about 50 engineers. Um, we're spread into four different teams and the teams are structured like this. So the first team that we have is infrastructure engineering and this team largely deals with deploying our Dockers using Kubernetes. So this team is all about taking Dockers, built by other teams, sometimes building the Dockers themselves and putting them into our production system, our platform engineering team, they produce these microservices. So they produce microservice, Docker images. They develop and test with them locally. Their entire environments are dockerized. They produce these doctors, hand them over to him for infrastructure engineering to be deployed. Similarly, our product engineering team does the same thing. They develop and test with Dr. Locally. They also produce a suite of Docker images that the infrastructure team can then deploy. And lastly, we have our R and D team, and this team specifically produces machine learning algorithms using Nvidia Docker collectively, we've actually built 381 Docker repositories and 14 million. >>We've had 14 million Docker pools over the lifetime of the company, just a few stats about us. Um, but what I'm really getting to here is you can see actually doctors becoming almost a form of communication between these teams. So one of the paradigms in software engineering that you're probably familiar with encapsulation, it's really helpful for a lot of software engineering problems to break the problem down, isolate the different pieces of it and start building interfaces between the code. This allows you to scale different pieces of the platform or different pieces of your code in different ways that allows you to scale up certain pieces and keep others at a smaller level so that you can meet customer demands. And for us, one of the things that we can largely do now is use Dockers as that interface. So instead of having an entire platform where all teams are talking to each other, and everything's kind of, mishmashed in a monolithic application, we can now say this team is only able to talk to this team by passing over a particular Docker image that defines the interface of what needs to be built before it passes to the team and really allows us to scalp our development and be much more efficient. >>Also, I'd like to say we are hiring. Um, so we have a number of open roles. We have about 30 open roles in our engineering team that we're looking to fill by the end of this year. So if any of this sounds really interesting to you, please reach out after the presentation. >>So what does our platform do? Really? Our platform allows you to answer any geospatial question, and we do this at three different inputs. So first off, where do you want to look? So we did this as what we call an AOI or an area of interest larger. You can think of this as a polygon drawn on the map. So we have a curated data set of almost 4 million AOIs, which you can go and you can search and use for your analysis, but you're also free to build your own. Second question is what you want to look for. We do this with the more interesting part of our platform of our machine learning and AI capabilities. So we have a suite of algorithms that automatically allow you to identify trucks, buildings, hundreds of different types of aircraft, different types of land use, how many people are moving from one location to another different locations that people in a particular area are moving to or coming from all of these different analyses or all these different analytics are available at the click of a button, and then determine what you want to look for. >>Lastly, you determine when you want to find what you're looking for. So that's just, uh, you know, do you want to look for the next three hours? Do you want to look for the last week? Do you want to look every month for the past two, whatever the time cadence is, you decide that you hit go and out pops a time series, and that time series tells you specifically where you want it to look what you want it to look for and how many, or what percentage of the thing you're looking for appears in that area. Again, we do all of this to work towards patterns. So we use all this data to produce a time series from there. We can look at it, determine the patterns, and then specifically identify the anomalies. As I mentioned with supply chain, this is extremely valuable to identify where things change. So we can answer these questions, looking at a particular operating facility, looking at particular, what is happening with the level of activity is at that operating facility where people are coming from, where they're going to, after visiting that particular facility and identify when and where that changes here, you can just see it's a picture of our platform. It's actually showing all the devices in Manhattan, um, over a period of time. And it's more of a heat map view. So you can actually see the hotspots in the area. >>So really the, and this is the heart of the talk, but what happened in 2020? So for men, you know, like many of you, 2020 was a difficult year COVID hit. And that changed a lot of what we're doing, not from an engineering perspective, but also from an entire company perspective for us, the motivation really became to make sure that we were lowering our costs and increasing innovation simultaneously. Now those two things often compete with each other. A lot of times you want to increase innovation, that's going to increase your costs, but the challenge last year was how to do both simultaneously. So here's a few stats for you from our team. In Q1 of last year, we were spending almost $600,000 per month on compute costs prior to COVID happening. That wasn't hugely a concern for us. It was a lot of money, but it wasn't as critical as it was last year when we really needed to be much more efficient. >>Second one is flexibility for us. We were deployed on a single cloud environment while we were cloud thought ready, and that was great. We want it to be more flexible. We want it to be on more cloud environments so that we could reach more customers. And also eventually get onto class side networks, extending the base of our customers as well from a custom analytics perspective. This is where we get into our traction. So last year, over the entire year, we computed 54,000 custom analytics for different users. We wanted to make sure that this number was steadily increasing despite us trying to lower our costs. So we didn't want the lowering cost to come as the sacrifice of our user base. Lastly, of particular percentage here that I'll say definitely needs to be improved is 75% of our projects never fail. So this is where we start to get into a bit of stability of our platform. >>Now I'm not saying that 25% of our projects fail the way we measure this is if you have a particular project or computation that runs every day and any one of those runs sale account, that is a failure because from an end-user perspective, that's an issue. So this is something that we know we needed to improve on and we needed to grow and make our platform more stable. I'm going to something that we really focused on last year. So where are we now? So now coming out of the COVID valley, we are starting to soar again. Um, we had, uh, back in April of last year, we had the entire engineering team. We actually paused all development for about four weeks. You had everyone focused on reducing our compute costs in the cloud. We got it down to 200 K over the period of a few months. >>And for the next 12 months, we hit that number every month. This is huge for us. This is extremely important. Like I said, in the COVID time period where costs and operating efficiency was everything. So for us to do that, that was a huge accomplishment last year and something we'll keep going forward. One thing I would actually like to really highlight here, two is what allowed us to do that. So first off, being in the cloud, being able to migrate things like that, that was one thing. And we were able to use there's different cloud services in a more particular, in a more efficient way. We had a very detailed tracking of how we were spending things. We increased our data retention policies. We optimized our processing. However, one additional piece was switching to new technologies on, in particular, we migrated to get lab CICB. >>Um, and this is something that the costs we use Docker was extremely, extremely easy. We didn't have to go build new new code containers or repositories or change our code in order to do this. We were simply able to migrate the containers over and start using a new CIC so much. In fact, that we were able to do that migration with three engineers in just two weeks from a cloud environment and flexibility standpoint, we're now operating in two different clouds. We were able to last night, I've over the last nine months to operate in the second cloud environment. And again, this is something that Docker helped with incredibly. Um, we didn't have to go and build all new interfaces to all new, different services or all different tools in the next cloud provider. All we had to do was build a base cloud infrastructure that ups agnostic the way, all the different details of the cloud provider. >>And then our doctors just worked. We can move them to another environment up and running, and our platform was ready to go from a traction perspective. We're about a third of the way through the year. At this point, we've already exceeded the amount of customer analytics we produce last year. And this is thanks to a ton more albums, that whole suite of new analytics that we've been able to build over the past 12 months and we'll continue to build going forward. So this is really, really great outcome for us because we were able to show that our costs are staying down, but our analytics and our customer traction, honestly, from a stability perspective, we improved from 75% to 86%, not quite yet 99 or three nines or four nines, but we are getting there. Um, and this is actually thanks to really containerizing and modularizing different pieces of our platform so that we could scale up in different areas. This allowed us to increase that stability. This piece of the code works over here, toxin an interface to the rest of the system. We can scale this piece up separately from the rest of the system, and that allows us much more easily identify issues in the system, fix those and then correct the system overall. So basically this is a summary of where we were last year, where we are now and how much more successful we are now because of the issues that we went through last year and largely brought on by COVID. >>But that this is just a screenshot of the, our, our solution actually working on supply chain. So this is in particular, it is showing traceability of a distribution warehouse in salt lake city. It's right in the center of the screen here. You can see the nice kind of orange red center. That's a distribution warehouse and all the lines outside of that, all the dots outside of that are showing where people are, where trucks are moving from that location. So this is really helpful for supply chain companies because they can start to identify where their suppliers are, are coming from or where their distributors are going to. So with that, I want to say, thanks again for following along and enjoy the rest of DockerCon.

(bright upbeat music) >> Announcer: From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Well happy New Year, one and all welcome to 2021 in Cube Conversation continuing our ongoing series. I hope your New Year is off to a great start. I know that the end of 2020 was a very good one for Ivanti. And Jim Schaper, the CEO is going to join us to talk about that as is Nayaki Nayyar, or rather the EVP and the Chief Product Officer. So Nayaki and Jim, good to have you here with you on theCUBE and Happy New Year to you. >> Thank you, John. Happy New Year to you. 2020, I think for a lot of us couldn't get out of here quick enough. Although we had some great things happen to our company at the very end of the year. So anxious to talk to you about it and we appreciate the opportunity. >> You bet. So we're talking about two major acquisitions that you made that both closed near the end of the year back in December, not too long ago. One with Pulse Secure, the other with MobileIron. Two companies that provide you with additional expertise in terms of mobile security and the enterprise security space. And so Jim, if you would, let's first talk about just for the big picture, the acquisitions that were made and what those moves will do for you going forward. >> Okay, great, John. We closed both acquisitions interestingly enough, on December 2nd. We've been fortunate to have them part of our company now for about the last 30 days. One of the things that we made a decision on a number of months ago was that we had a real opportunity in the markets that we serve to really build our business more quickly through a series of acquisitions that strategically made sense for us, our investors and more importantly our customers. And that really is why we chose MobileIron and Pulse, for different reasons but nonetheless all very consistent with our longterm strategy of securing the end points on every network, in every location around the world. And so consequently, when you think about it and we've all witnessed here over the last 30 days or so, all of the security breaches, all of the things that go along with that, and our real focus is ensuring that every company and every individual on their network, outside their firewall, inside their firewall, on any device is secure. And so with these two particular acquisitions, in addition to the assets that we already had as a part of Ivanti, really puts us in a competitively advantaged position to deliver to the edge, and Nayaki will talk about this. The ability to secure those devices and ensure that they're secure from phishing expeditions or breaches or all of those kinds of things. So these two particular acquisitions really puts us on the map and puts us in a leadership position in the security market. So we're thrilled to have both of them. >> Before I go off to Nayaki, I want to follow with the point that you've made Jim talking about security breaches. We're all well aware. You know, the news from what we've been hearing out from the federal level about the state actors and the kind of these infiltrations of major US systems if not international systems. Some Interpol data, I read 207 some odd percent increase in breaches just in the post COVID time or in the COVID time, the past year. That gets your attention, does it not? And what does that say to you about the aggressive nature of these kinds of activities? >> Well, that they're getting more sophisticated every day and they're getting more aggressive. I think one of the most frightening conversations I had was a briefing with our chief security officer about how many attempted breaches of our network and our systems that he sees every single day. And we're able to identify what foreign actors are really trying to penetrate our systems or what are they trying to do. But the one thing I will leave you with is they're becoming much more sophisticated, whether you're inside the firewall or whether you're on your iPhone as an extension of the network, there the level of sophistication is startling. And unfortunately in many cases, as evidenced by the recent breaches, you don't even know you've had a breach for could be months, weeks, days. And so what damage is done. And so as we look forward, and as Nayaki kind of walks you through our product strategy, what you're going to hear a lot of is how do we self protect? How do we self-learn the devices at the edge, on the end of the networks, such that they can recognize foreign actors or any breach capability that somebody is trying to employ? And so, yeah, it's frightening how sophisticated and how frequent they have become. >> I think the one thing that really struck me as I read about the breaches was not so much the damage that has been done, but the damage that could be done prospectively and about which we have no idea. You don't know, it's like somebody lurking in your closet and they're going to stay there for a couple of months and wait for the time that maybe your guard is even more down. So I was, that's what shocks me. And they Nayaki, let's talk about your strategy then. You picked up obviously a couple of companies, one in the, kind of the enterprise IT space. Now the one in the VPN space, add into your already extensive portfolio. So I imagine from your office, wearing the hat of the chief product officer, you're just to look in your chops right now. You've got a lot more resources at your disposal. >> Yeah, we are very very busy John, but to Jim's point, one of the trends we are seeing in the market as we enter into the post COVID era, where everyone is working from anywhere, be it from home, be it from office, while on the move, every organization, every enterprise is struggling with this. What we call this explosive growth of devices. Devices being mobile devices, client-based devices, IoT devices, the data that is being generated from these devices, and to your point, the cybersecurity threats. It is predicted that there has been 30000% increase in the cybersecurity threats that are being targeted primarily at the remote workers. So you can imagine whether it's phishing attacks, malware attacks, I mean just an explosive growth of devices, data, cybersecurity attacks at the remote workers. So organizations need automation to be able to address this growth and this complexity which is where Ivanti's focus in discovering all the devices and managing those devices. So as we bring the MobileIron portfolio and Ivanti's portfolio together, now we can help our customers manage every type of devices be it Windows devices, Mac devices, Linux, iOS, Android devices, and secure those devices. The zero trust access that users need, the remote users need, all the way from cloud access to the endpoint is what the strength of both MobileIron and Pulse brings to our entire portfolio holistically. So we are truly excited for our customers. Now they can leverage our entire end to end stack to discover, manage secure and service all those devices that they now have to service for their employees. >> Explain to me, or just walk me through zero trust in terms of how you define that. I've read about trust nothing, verify everything, those kinds of explanations. But if you would, from your perspective, what does zero trust encompass, not only on your side, but on your client's side? Because you want to give them tools to do things for themselves to self heal and self serve and those kinds of things. >> So, zero trust is you don't trust anything. You validate and certify everything. So the access users have on your network, the access they have on the mobile devices, the applications they are accessing, the data that they are accessing. So being able to validate every access that they have when they come into your network is what the whole zero trust access really means. So, the combination of Ivanti's portfolio and also Pulse that zero trust access all the way from as users are accessing that network data, cloud data, endpoint data, is where our entire zero trust access truly differentiates. And as we bring that with our UEM portfolio with the MobileIron, there is no other vendor in the market that has that holistic offering, internal offering. >> I'm sorry, go ahead, please. >> It's interesting, John, you talk about timing is everything, right? And when we began discussions with MobileIron, it was right before COVID hit. And we had a great level of expertise inside the pre-acquisition of Ivanti to be able to secure the end points at the desktop level. But we struggled a bit with having all of the capabilities that we needed to manage mobile devices and tablets and basically anything that is attached to the network. That's what they really brought to us. And having done a number of acquisitions historically in my career, this was probably the easiest integration that we had simply because we did what they didn't do and they did what we didn't do. And then they brought some additional technologies. But what's really changed in the environment because of this work from home or work from anywhere as as we like to articulate it, is you've got multiple environments that you've got to manage. It isn't just, what's on the end of the VPN, the network, it's what's on the end points of the cloud. What kind of cloud are you running? You're running a public cloud, you're running a private cloud. Is it a hybrid environment? And so the ability to and the need to be able to do that is pretty significant. And so that's one of the real advantages that both the Pulse as well as the MobileIron acquisitions really brought to the combined offering from a product standpoint. >> Yeah, I'd like to follow up on that then, just because the cloud environment provides so many benefits, obviously, but it also provides this huge layer of complexity that comes on top of all this because you just talked about it. You can have public, you can have hybrid cloud, you can have on-prem, whatever, right? You have all these options. And yet you, Ivanti, are having to provide security on multiple levels and multiple platforms or multiple environments. And how much more complex or challenging is your mission now because of consumer demand and the capabilities the technology is providing your clients. >> Well, it's certainly more complex and Nayaki is better equipped to probably talk in detail about this. But if you just take a step back and think about it, you think about internet of things, right? I used to have a thermostat. And that thermostat control was controlled by the thermostat on the wall. Now everything is on WiFi. If I've got a problem, I had a a problem with a streaming music capability which infected other parts of my home network. And so everything is, that's just one example of how complicated and how wired everything is really become. Except when it comes to the mobile devices, which are still always remote. You've always got it with you. I don't what it was like for you, John, but you know, historically I've used my phone on email, texts and phone calls. Now it's actually a business tool. But it's a remote business tool that you still have to secure, you still have to manage and you still have to find an identify on the end of the network. That's where we really come into play. Nayaki, anything you want to add to that? >> Yeah, so, to Jim's point, John, and to your question also, as customers have what we call the multi-cloud offering. There are public clouds, private clouds, on-prem data centers, devices on the edge, and as you extend into the IOT world, being able to provide that seamless access, this is a zero trust access all the way from the cloud applications to the applications that are running on-prem, in your data centers and also the applications that are running on your devices and the IoT applications, is what that entire end to end zero trust access, is where our competitive strength resides with Pulse coming into our portfolio. Before Ivanti didn't have this. We were primarily a patch management vendor in the security space, but now we truly extend beyond that patch to this end to end access all the way from cloud to edge is what we call. And then when we combine that with our UEM portfolio in our endpoint management with MobileIron and also service management, that convergence of positive three pillars is where we truly differentiate and compete and win in the market. >> Nayaki, how does internet of things factor into this? Cause I look at sensor technology, I'm just thinking about all the billions of what you have now, right? With whether it's farming or agricultural inputs, business inputs, meteorological, or whatever. I'm sure, you're considering this as well as part of a major play of yours in terms of providing IoT security. How more proliferated is that now and how much of that is kind of in your concern zone you might say? >> Yeah, absolutely. So, just taking these trends we have in managing the end points, we will extend that into the IoT world also. John, when we say IoT world, in an industry where the devices are like healthcare devices. So, stay tuned, in January release we'll be releasing how we will be discovering managing and securing for the healthcare devices like Siemens devices, Bayer devices, Canon devices. So, you're spot on how we can leverage the strength we have in managing end points. Also IoT devices, that same capabilities that we can bring to each of the industry verticals. Now we're not trying to solve the entire vertical market but certain industry verticals where we have a strong footprint. Healthcare is a strong footprint for us. Telcos is a strong footprint for us. So that's where you will see us extending into those IoT devices too. >> Okay, so, in going forward, Jim, if you would just, let's talk about your 2021 in terms of how you further integrate these offerings that you've acquired right now. All of a sudden you've got 30 days of, you know, which is snap of a finger. But what do you see how 2021 is going to lay out, especially with distributed workforces, right? We know that's here. That's a new normal. And with a whole new set of demands on networks and certainly the need for security. >> That's exactly correct, John. I mean, everything is changed and it's never going back to the way it was. You know, everybody has their own definition of the new normal. I guess my definition is at some point in time when things do return to some form of normality, a portion of our workforce will always work from home. To what degree remains to be seen. I don't think we're different from virtually any other industry or any other company. It does put increased demands ,complexity and requirements around how you run your internal IT business. But as Nayaki talked about kind of our virtual service desk offering where you're not going to have a service desk anymore. It's got to be virtual. Well, you have to be able to still provide those services outside of your normal network. And so that's going to be a continued big push for us. I'm incredibly pleased with the way in which the employee bases of the acquired companies have really folded in and become one with our company. And I think as we all recognize cultural differences between organizations can be quite significant and an impediment to really moving forward. Fortunately for us, we have found that both of these organizations fit really nicely from an employee, from a values perspective, from a goals and objectives perspective. And so we did most of the heavy lifting on all the integration shortly after we closed the transactions on the 2nd of December. And so we've moved beyond what I would call the normal kind of concerns and asked around what's going to happen in this and that. We're now kind of heads down in what's the long-term integration going to look like from a product standpoint. We're already looking at additional acquisitions that will continue to take us deeper and wider into our three product pillars, as Nayaki described. And that'll be an ongoing kind of steady dose of acquisitions as we continue to supplement our organic growth within organic growth. >> But you've got to answer my question. I was going to ask you, you founded the company four years ago. There were two big acquisitions back in 2017. We waited four years Jim, until you dip back into that pole again. So the plan, maybe not to wait four years before moving on. >> No trust me, you won't be waiting another four years. Now you've got to bear in mind, John. I wasn't here four years ago. >> That's right, okay. Fair enough. That's okay. I want to thank you both for the time today. Congratulations on sealing those deals back in December and we certainly wish you all the best going forward. And of course, a very happy and a very safe new year for you and yours. >> Same to you, John. Thanks so much for the time. And so it was a pleasure to spend time with you today. >> Thank you, John. Happy New Year again. Thank you. Thank you. (upbeat music)

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

Hi, I'm Hideo Mabuchi from Stanford University. This is my presentation on coherent nonlinear dynamics, and combinatorial optimization. This is going to be a talk, to introduce an approach, we are taking to the analysis, of the performance of Coherent Ising Machines. So let me start with a brief introduction, to ising optimization. The ising model, represents a set of interacting magnetic moments or spins, with total energy given by the expression, shown at the bottom left of the slide. Here the cigna variables are meant to take binary values. The matrix element jij, represents the interaction, strength and sign, between any pair of spins ij, and hi represents a possible local magnetic field, acting on each thing. The ising ground state problem, is defined in an assignment of binary spin values, that achieves the lowest possible value of total energy. And an instance of the easing problem, is specified by given numerical values, for the matrix j and vector h, although the ising model originates in physics, we understand the ground state problem, to correspond to what would be called, quadratic binary optimization, in the field of operations research. And in fact, in terms of computational complexity theory, it can be established that the, ising ground state problem is NP complete. Qualitatively speaking, this makes the ising problem, a representative sort of hard optimization problem, for which it is expected, that the runtime required by any computational algorithm, to find exact solutions, should asyntonically scale, exponentially with the number of spins, and four worst case instances at each end. Of course, there's no reason to believe that, the problem instances that actually arise, in practical optimization scenarios, are going to be worst case instances. And it's also not generally the case, in practical optimization scenarios, that we demand absolute optimum solutions. Usually we're more interested in, just getting the best solution we can, within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for computation. This focus is great interest on, so-called heuristic algorithms, for the ising problem and other NP complete problems, which generally get very good, but not guaranteed optimum solutions, and run much faster than algorithms, that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem, for which extensive compilations, of benchmarking data may be found online. A recent study found that, the best known TSP solver required median runtimes, across a library of problem instances, that scaled as a very steep route exponential, for an up to approximately 4,500. This gives some indication of the change, in runtime scaling for generic, as opposed to worst case problem instances. Some of the instances considered in this study, were taken from a public library of TSPs, derived from real world VLSI design data. This VLSI TSP library, includes instances within ranging from 131 to 744,710, instances from this library within between 6,880 and 13,584, were first solved just a few years ago, in 2017 requiring days of runtime, and a 48 core two gigahertz cluster, all instances with n greater than or equal to 14,233, remain unsolved exactly by any means. Approximate solutions however, have been found by heuristic methods, for all instances in the VLSI TSP library, with, for example, a solution within 0.014% of a known lower bound, having been discovered for an instance, with n equal 19,289, requiring approximately two days of runtime, on a single quarter at 2.4 gigahertz. Now, if we simple-minded the extrapolate, the route exponential scaling, from the study yet to n equal 4,500, we might expect that an exact solver, would require something more like a year of runtime, on the 48 core cluster, used for the n equals 13,580 for instance, which shows how much, a very small concession on the quality of the solution, makes it possible to tackle much larger instances, with much lower costs, at the extreme end, the largest TSP ever solved exactly has n equal 85,900. This is an instance derived from 1980s VLSI design, and this required 136 CPU years of computation, normalized to a single core, 2.4 gigahertz. But the 20 fold larger, so-called world TSP benchmark instance, with n equals 1,904,711, has been solved approximately, with an optimality gap bounded below 0.0474%. Coming back to the general practical concerns, of applied optimization. We may note that a recent meta study, analyze the performance of no fewer than, 37 heuristic algorithms for MaxCut, and quadratic binary optimization problems. And find the performance... Sorry, and found that a different heuristics, work best for different problem instances, selected from a large scale heterogeneous test bed, with some evidence, the cryptic structure, in terms of what types of problem instances, were best solved by any given heuristic. Indeed, there are reasons to believe, that these results for MaxCut, and quadratic binary optimization, reflect to general principle, of a performance complementarity, among heuristic optimization algorithms, and the practice of solving hard optimization problems. There thus arises the critical pre processing issue, of trying to guess, which of a number of available, good heuristic algorithms should be chosen, to tackle a given problem instance. Assuming that any one of them, would incur high cost to run, on a large problem of incidents, making an astute choice of heuristic, is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight, about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This is certainly pinpointed by researchers in the field, as a circumstance and must be addressed. So adding this all up, we see that a critical frontier, for cutting edge academic research involves, both the development of novel heuristic algorithms, that deliver better performance with lower costs, on classes of problem instances, that are underserved by existing approaches, as well as fundamental research, to provide deep conceptual insight, into what makes a given problem instance, easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law, and speculate about a so-called second quantum revolution, it's natural to talk not only about novel algorithms, for conventional CPUs, but also about highly customized, special purpose hardware architectures, on which we may run entirely unconventional algorithms, for common tutorial optimizations, such as ising problem. So against that backdrop, I'd like to use my remaining time, to introduce our work on, analysis of coherent using machine architectures, and associated optimization algorithms. Ising machines in general, are a novel class of information processing architectures, for solving combinatorial optimization problems, by embedding them in the dynamics, of analog, physical, or a cyber-physical systems. In contrast to both more traditional engineering approaches, that build ising machines using conventional electronics, and more radical proposals, that would require large scale quantum entanglement the emerging paradigm of coherent ising machines, leverages coherent nominal dynamics, in photonic or optical electronic platforms, to enable near term construction, of large scale prototypes, that leverage posting as information dynamics. The general structure of current of current CIM systems, as shown in the figure on the right, the role of the easing spins, is played by a train of optical pulses, circulating around a fiber optical storage ring, that beam splitter inserted in the ring, is used to periodically sample, the amplitude of every optical pulse. And the measurement results, are continually read into an FPGA, which uses then to compute perturbations, to be applied to each pulse, by a synchronized optical injections. These perturbations are engineered to implement, the spin-spin coupling and local magnetic field terms, of the ising hamiltonian, corresponding to a linear part of the CIM dynamics. Asynchronously pumped parametric amplifier, denoted here as PPL and wave guide, adds a crucial nonlinear component, to the CIM dynamics as well. And the basic CIM algorithm, the pump power starts very low, and is gradually increased, at low pump powers, the amplitudes of the easing spin pulses, behave as continuous complex variables, whose real parts which can be positive or negative, by the role of soft or perhaps mean field spins. Once the pump power crosses the threshold, for perimetric self oscillation in the optical fiber ring, however, the amplitudes of the easing spin pulses, become effectively quantized into binary values, while the pump power is being ramped up, the FPGA subsystem continuously applies, its measurement based feedback implementation, of the using hamiltonian terms. The interplay of the linearized easing dynamics, implemented by the FPGA , and the thresholds quantization dynamics, provided by the sink pumped parametric amplifier, result in a final state, of the optical plus amplitudes, at the end of the pump ramp, that can be read as a binary strain, giving a proposed solution, of the ising ground state problem. This method of solving ising problems, seems quite different from a conventional algorithm, that runs entirely on a digital computer. As a crucial aspect, of the computation is performed physically, by the analog continuous coherent nonlinear dynamics, of the optical degrees of freedom, in our efforts to analyze CA and performance. We have therefore turn to dynamical systems theory. Namely a study of bifurcations, the evolution of critical points, and typologies of heteroclitic orbits, and basins of attraction. We conjecture that such analysis, can provide fundamental insight, into what makes certain optimization instances, hard or easy for coherent ising machines, and hope that our approach, can lead to both improvements of the course CIM algorithm, and the pre processing rubric, for rapidly assessing the CIM insuibility of the instances. To provide a bit of intuition about how this all works. It may help to consider the threshold dynamics, of just one or two optical parametric oscillators, in the CIM architecture just described. We can think of each of the pulse time slots, circulating around the fiber ring, as are presenting an independent OPO. We can think of a single OPO degree of freedom, as a single resonant optical mode, that experiences linear dissipation, due to coupling loss, and gain in a pump near crystal, as shown in the diagram on the upper left of the slide, as the pump power is increased from zero. As in the CIM algorithm, the non-linear gain is initially too low, to overcome linear dissipation. And the OPO field remains in a near vacuum state, at a critical threshold value, gain equals dissipation, and the OPO undergoes a sort of lasing transition. And the steady States of the OPO, above this threshold are essentially coherent States. There are actually two possible values, of the OPO coherent amplitude, and any given above threshold pump power, which are equal in magnitude, but opposite in phase, when the OPO cross this threshold, it basically chooses one of the two possible phases, randomly, resulting in the generation, of a single bit of information. If we consider two uncoupled OPOs, as shown in the upper right diagram, pumped at exactly the same power at all times, then as the pump power is increased through threshold, each OPO will independently choose a phase, and thus two random bits are generated, for any number of uncoupled OPOs, the threshold power per OPOs is unchanged, from the single OPO case. Now, however, consider a scenario, in which the two appeals are coupled to each other, by a mutual injection of their out coupled fields, as shown in the diagram on the lower right. One can imagine that, depending on the sign of the coupling parameter alpha, when one OPO is lasing, it will inject a perturbation into the other, that may interfere either constructively or destructively, with the field that it is trying to generate, via its own lasing process. As a result, when can easily show that for alpha positive, there's an effective ferromagnetic coupling, between the two OPO fields, and their collective oscillation threshold, is lowered from that of the independent OPO case, but only for the two collective oscillation modes, in which the two OPO phases are the same. For alpha negative, the collective oscillation threshold, is lowered only for the configurations, in which the OPO phases are opposite. So then looking at how alpha is related to the jij matrix, of the ising spin coupling hamilitonian, it follows the, we could use this simplistic to OPO CIM, to solve the ground state problem, of the ferromagnetic or antiferromagnetic angles, to ising model, simply by increasing the pump power, from zero and observing what phase relation occurs, as the two appeals first start to lase. Clearly we can imagine generalizing the story to larger, and, however, the story doesn't stay as clean and simple, for all larger problem instances. And to find a more complicated example, we only need to go to n equals four, for some choices of jij for n equals four, the story remains simple, like the n equals two case. The figure on the upper left of this slide, shows the energy of various critical points, for a non frustrated n equals for instance, in which the first bifurcated critical point, that is the one that, by forgets of the lowest pump value a, this first bifurcated critical point, flows asyntonically into the lowest energy using solution, and the figure on the upper right, however, the first bifurcated critical point, flows to a very good, but suboptimal minimum at large pump power. The global minimum is actually given, by a distinct critical point. The first appears at a higher pump power, and is not needed radically connected to the origin. The basic CIM algorithm, is this not able to find this global minimum, such non-ideal behavior, seems to become more common at margin end, for the n equals 20 instance show in the lower plots, where the lower right pod is just a zoom into, a region of the lower left block. It can be seen that the global minimum, corresponds to a critical point, that first appears that of pump parameter a around 0.16, at some distance from the adriatic trajectory of the origin. That's curious to note that, in both of these small and examples, however, the critical point corresponding to the global minimum, appears relatively close, to the adiabatic trajectory of the origin, as compared to the most of the other, local minimum that appear. We're currently working to characterise, the face portrait typology, between the global minimum, and the adiabatic trajectory of the origin, taking clues as to how the basic CIM algorithm, could be generalized, to search for non-adiabatic trajectories, that jumped to the global minimum, during the pump up, of course, n equals 20 is still too small, to be of interest for practical optimization applications. But the advantage of beginning, with the study of small instances, is that we're able to reliably to determine, their global minima, and to see how they relate to the idea, that trajectory of the origin, and the basic CIM algorithm. And the small land limit, We can also analyze, for the quantum mechanical models of CAM dynamics, but that's a topic for future talks. Existing large-scale prototypes, are pushing into the range of, n equals, 10 to the four, 10 to the five, 10 to the six. So our ultimate objective in theoretical analysis, really has to be, to try to say something about CAM dynamics, and regime of much larger in. Our initial approach to characterizing CAM behavior, in the large end regime, relies on the use of random matrix theory. And this connects to prior research on spin classes, SK models, and the tap equations, et cetera, at present we're focusing on, statistical characterization, of the CIM gradient descent landscape, including the evolution of critical points, And their value spectra, as the pump powers gradually increase. We're investigating, for example, whether there could be some way, to explain differences in the relative stability, of the global minimum versus other local minima. We're also working to understand the deleterious, or potentially beneficial effects, of non-ideologies such as asymmetry, in the implemented using couplings, looking one step ahead, we plan to move next into the direction, of considering more realistic classes of problem instances, such as quadratic binary optimization with constraints. So in closing I should acknowledge, people who did the hard work, on these things that I've shown. So my group, including graduate students, Edwin Ng, Daniel Wennberg, Ryatatsu Yanagimoto, and Atsushi Yamamura have been working, in close collaboration with, Surya Ganguli, Marty Fejer and Amir Safavi-Naeini. All of us within the department of applied physics, at Stanford university and also in collaboration with Yoshihisa Yamamoto, over at NTT-PHI research labs. And I should acknowledge funding support, from the NSF by the Coherent Ising Machines, expedition in computing, also from NTT-PHI research labs, army research office, and ExxonMobil. That's it. Thanks very much.

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hey, welcome back, everybody. Jeff Frick with theCUBE. We're in our Palo Alto Studios on this kind of continuing leadership series that we've put together. Reaching out to the community for tips and tricks on kind of getting through what is, this kind of ongoing COVID crisis and situation as it continues to go weeks and weeks and weeks. And I'm really excited to have one of my favorite members of our community, is Nick Mehta, the CEO of Gainsight. Had the real pleasure of interviewing him a couple times and had to get him on. So Nick, thanks for taking some time out of your very busy day to join us. >> Jeff, honored to be here, thank you. >> Pleasure, so let's just jump into it. One of the reasons I wanted to get you on, is that Gainsight has been a distributed company from the beginning, and so I think the COVID, suddenly everyone got this work from home order, there was no prep, there was no planning, it's like this light switch digital transformation moment. So love to hear from someone who's been doing it for awhile. What are some of the lessons? How should people think about running a distributed company? >> Yeah, it's really interesting, Jeff, 'cause we are just by happenstance, from the beginning, distributed where we have, our first two offices were St. Louis and Hyderabad, India. So two places you cannot get there through one flight. So, you have to figure out how to collaborate asynchronously and then over time, we have offices in the Bay Area. We have tons of people that work from home. And so we try to tell people we don't have a headquarters. The headquarters is wherever you are, wherever you live and wherever you want to work. And so we've always been super flexible about come in to the office if you want, don't come in, et cetera. So different than some companies in that respect. And because of that, pre-COVID, we always a very heavy video culture, lots of video conferencing. Even if some people were in an office, there's always somebody else dialing in. One benefit we got from that is you never had to miss your kids' stuff or your family things. I would go to my daughter's performance in the middle of the day and know I can just dial into a call on the way there. And so we always had that. But what's amazing is now we're all on a level playing field, there's nobody in our office. And I got to say, this is, in some ways, even better 'cause I feel like when you're the person dialed in, and a lot of people are in a room, you probably had that experience, and it feels like you're kind of not on the same playing field, right? Hard to hear the jokes or the comments and you might not feel like you're totally in crowd, so to speak, right? But now everyone's just at their computer, sitting there in a chair all day doing these Zooms and it does feel like it's equalizing a little bit. And what it's caused us to do is say, hey, what are ways we can all recreate that community from home? So as an example, every 7:45 a.m. every day, we have a Zoom call that's just pure joy and fun. Trivia, pets, kids. The employees' kids announce people's birthdays and the weather. And so these ways we've been able to integrate our home and our work that we never could before, it's really powerful. It's a tough situation overall, and we feel for all the people affected. But even in tough situations, there are silver linings, and we're finding 'em. >> Yeah, it's funny, we just had Darren Murph on the other day. I don't know if you know Darren. He is the head of Remote Work at GitLab, and he-- >> Oh, yeah. >> And he talked about kind of the social norms. And one of the instances that he brought up was, back in the day when you had some people in the office, some people joining via remote, that it is this kind of disharmony because they're very different situations. So one of his suggestions was have everybody join via their laptop, even if they're sitting at their desk, right? So, as you said, you get kind of this level playing field. And the other thing which dovetails off what you just said is he always wanted executives to have a forcing function to work from home for an extended period of time, so they got to understand what it's all about. And it's not only looking through a little laptop or this or that, but it's also the distractions of the kids and the dogs and whatever else is happening around the house. So it is wild how this forcing function has really driven it. And his kind of takeaway is, as we, like say, move from can we get it into cloud to cloud first? And does it work on mobile to mobile first? >> Now it's really-- >> Yeah. >> It's really remote first. And if you-- >> Remote first. >> A remote first attitude about it and kind of turn it on it's head, it's why shouldn't it be remote versus can it be remote? It really changes the conversation and the dynamic of the whole situation. >> I love that. And just, GitLab, by the way, has been a true inspiration 'cause they are the most remote, remote company. And they share so much, I love what you said. As just two examples of reacting to what you said, pre-COVID, we always wanted to keep a level playing field. So we actually moved our all-hands meetings to be instead of being broadcast from one room, and you're kind of seeing this small screen with all these people, we all just were at computers presenting. And so everyone's on a level playing field. So I thought what GitLab said is great. And then the other point, I think post-COVID we have learned is the kids and the dogs aren't distractions, they're part of our life. And so embracing those and saying, hey, I see that kid in the background, bring them onto the screen. Even during work meetings, even customer meetings, you know? And I'm seeing, I'm on a customer meeting and the customer's bringing their kids onto the screen and it's kind of breaking this artificial wall between who we are at home and who we are at work 'cause we're human beings all throughout. At Gainsight, we talk about a human first approach to business and we've never been more human as a world than we are right now. >> Love it, love it. So another, get your thoughts on, is this whole idea of measurement and productivity at home. And it's really, I have to say, disturbing to see some of the new product announcements that are coming out in terms of people basically snoopin' on people. Whether it's trackin' how many hours of Zoom calls they're on, or how often are they in the VPN, or having their camera flip on every so many minutes or something. We had Marten Mickos on, who's now the CEO of HackerOne. He was CEO at MySQL years ago before it went to Sun and he had the great line, he said, it's so easy to fake it at the office, but when you're at home and you're only output is your deliverable, it makes it a lot easier. So I wonder if you can share some of your thoughts in terms of kind of managing output, setting expectations, to get people to get their work done. And then, as you see some of these new tools for people that are just entering this thing, it's just not right (chuckles). >> Yeah, I agree with you and Marten. I'm a huge fan of Marten, as well, I totally agree with both of it. And I think there's an older approach to work, which is more like a factory. It's like you got to see how many widgets you're processing and you got to micromanage and you got to monitoring and inspecting. Look, I don't run a factory, so maybe there are places where that model makes sense. So I'm not going to speak for every leader, but I could say if you're in a world where your job is information, services, software, where the value is the people and their knowledge, managing them that way is a losing battle. I go back to, some folks probably know, this famous TED Talk by Dan Pink on basically what motivates people. And in these knowledge worker jobs, it's autonomy, mastery and purpose. So autonomy, we have the freedom to do what we want. Mastery, we feel like we're getting better at jobs. And purpose, which is I have a why behind what I do. And I think, take that time you spend on your micromanagement and your Zoom, analyzing the Zoom sessions, and spend it on inspiring your team, on the purpose. Spend it on enabling your team in terms of mastery. Spend it on taking away barriers so they have more autonomy. I think you'll get way more out of your team. >> Yeah, I agree. I think it's, as Darren said, again, he's like, well, would you trust your people if you're on the fourth floor and they're on the sixth? So just-- >> Yeah, exactly. >> If you don't trust your people, you got to bigger issue than worrying about how many hours they're on Zoom, which is not the most productive use of time. >> People waste so much time in the office, and getting to the office. And by the way, I'm not saying that it's wrong, it's fine too. But it's not like the office is just unfettered productivity all the time, that's a total myth. >> Yes, so let's shift gears a little bit and talk about events. So, obviously, the CUBE's in the event business. We've had to flip completely 'cause all the events are, well, they're all going digital for sure, and/or postponing it or canceling. So we've had to flip and do all dial-ins and there's a whole lot of stuff about asynchronous. But for you, I think it's interesting because as a distributed company, you had Gainsight Pulse as that moment to bring people together physically. You're in the same boat as everybody else, physical is not an option this year. So how are you approaching Gainsight Pulse, both because it's a switch from what you've done in the past, but you at least had the benefit of being in a distributed world? So you probably have a lot of advantages over people that have never done this before. >> Yeah, that's a really interesting, insightful observation. So just for a context, Pulse is an event we do every year to bring together the customer success community. 'Cause, as you observed, there is value in coming together. And so this is not just for our employees, this is for all the customer success people, and actually increasingly product management people out there, coming together around this common goal of driving success for your customers. And it started in 2013 with 300 people, and last year, we had 5,000 people at our event in San Francisco. We had similar events in London and Sydney. And so it's a big deal. And there's a lot of value to coming together physically. But obviously, that's not possible now, nor is it advisable. And we said, okay, how do we convert this and not lose what's special about Pulse? And leverage, like you said, Jeff, the fact that we're good at distributed stuff in general. And so we created what we call Pulse Everywhere. We didn't want to call it Pulse Virtual or something like that, Pulse Webinar, because we didn't want to set the bar as just like, oh, my virtual event, my webinar. This is something different. And we called it Everywhere, 'cause it's Pulse wherever you are. And we joke, it's in your house, it's in your backyard, it's on the peloton, it's walking the dog. You could be wherever you are and join Pulse this year, May 13th and 14th. And what's amazing is last year we had 5,000 people in person, this year we already have 13,000 people registered as of the end of April. And so we'll probably have more than three times the number of people at Pulse Everywhere. And we're really bringing that physical event concept into the virtual, literally with, instead of a puppy pit, where you're in a physical event, you'll bring puppies often, we have a puppy cam where you can see the puppies. We're not giving up on all of our silly music videos and jokes and we actually ship cameras and high-end equipment to all the speakers' houses. So they're going to have a very nice digital experience, our attendees are. It's not going to be like watching a video conference call. It's going to be like watching a TV show, one much like what you try to do here, right? And so we have this amazing experience for all of our presenters and then for the audience. And we're really trying to say how do we make it so it feels like you're in this really connected community? You just happen to not be able to shake people's hands. So it's coming up in a few weeks. It's a big experiment, but we're excited about it. >> There's so many conversations, and we jumped in right away, when this was all going down, what defines a digital event? And like you, I don't like the word virtual. There's nothing fake or virtual. To me, virtual's second to life. And kind of-- >> Yeah. >> Video game world. And like you, we did, it can't be a webinar, right? And so, if you really kind of get into the attributes of what is a webinar? It's generally a one-way communication for a significant portion of the allocated time and you kind of get your questions in and hopefully they take 'em, right? It's not a truly kind of engaged process. That said, as you said, to have the opportunity to separate creation, distribution and consumption of the content, now opens up all types of opportunity. And that's before you get into the benefits of the democratization, as you said, we're seeing that with a lot of the clients we work with. Their registration numbers are giant. >> Totally. >> Because-- >> You're not traveling to spend money, yeah. >> It'll be curious to see what the conversion is and I don't know we have a lot of data there. But, such a democratizing opportunity. And then, you have people that are trying to force, as Ben Nelson said on, you know Ben from Minerva, right? A car is not a mechanical horse, they're trying to force this new thing into this old paradigm and have people sit for, I saw one today, 24 hours, in front of their laptop. It's like a challenge. And it's like, no, no, no. Have your rally moment, have your fun stuff, have your kind of your one-to-many, but really there's so much opportunity for many-to-many. >> Many-to-many. >> Make all the content out there, yeah. >> We've created this concept in this Pulse Everywhere event called Tribes. And the idea is that when you go to an event, the goal is actually partially content, but a lot of times it's connection. And so in any given big event, there's lots of little communities out there and you want to meet people "like you". Might be people in a similar phase of their career, a similar type of company, in our case, it could be companies in certain industry. And so these Tribes in our kind of Pulse Everywhere experience, let people break out into their own tribes, and then kind of basically chat with each other throughout the event. And so it's not the exact same thing as having a drink with people, but at least a little bit more of that serendipitous conversation. >> Right, no, it's different and I think that's really the message, right? It's different, it's not the same. But there's a lot of stuff you can do that you can't do in the physical way, so quit focusing on what you can't do and embrace what you can. So that's great. And good luck on the event. Again, give the plug for it. >> Yeah, it's May 13th and 14th. If you go to gainsightpulse.com you can sign up, and it's basically anything related to driving better success for your customers, better retention, less churn, and better product experience. It's a great event to learn. >> Awesome, so I want to shift gears one more time and really talk about leadership. That's really kind of the focus of this series that we've been doing. And tough times call for great leadership. And it's really an opportunity for great leaders to show their stuff and let the rest of us learn. You have a really fantastic style. You know I'm a huge fan, we're social media buddies. But you're very personable and you're very, kind of human, I guess, is really the best word, in your communications. You've got ton of frequency, ton of variety. But really, most of it has kind of this human thread. I wonder if you can share kind of your philosophy behind social, 'cause I think a lot of leaders are afraid of it. I think they're afraid that there is reward for saying something stupid is not worth the benefit of saying okay things. And I think also a lot of leaders are afraid of showing some frailty, showing some emotion. Maybe you're a little bit scared, maybe we don't have all the answers. And yet you've really, you're not afraid at all. And I think it's really shines in the leadership activities and behaviors and things you do day in and day out. So how do you think about it? What's your strategy? >> Yeah, it's really interesting you ask, Jeff, because I'm in a group of CEOs that get together on a regular basis, and I'm going to be leading a session on social media for CEOs. And honestly, when I was putting it together, I was like, it's 2020, does that still need to exist? But somehow, there is this barrier. And I'll talk more about it, but I think the barrier isn't just about social media, it's just about how a CEO wants to present herself or himself into the world. And I think, to me, the three things to ask yourself are, first of all, why? Why do you want to be on social media? Why do you want to communicate to the outside? You should have a why. Hopefully you enjoy it, but also you're connecting from a business perspective with your customers. And for us, it's been a huge benefit to really be able to connect with our customers. And then, who are you targeting? So, I actually think an important thing to think about is it's okay to have a micro-audience. I don't have millions of Twitter followers like Lady Gaga, but within the world of SaaS and customer success and retention, I probably have a decent number. And that means I can really connect with my own specific audience. And then, what. So, the what is really interesting 'cause I think there's a lot of non-obvious things about, it's not just about your business. So I can tweet about customer success or retention and I do, but also the, what, about you as an individual, what's happening in your family? What's happening in the broader industry, in my case of SaaS? What's happening in the world of leading through COVID-19? All the questions you've asked, Jeff, are in this lens. And then that gets you to the final which is the, how. And I think the, how, is the most important. It's basically whether you can embrace the idea of being vulnerable. There's a famous TED Talk by Brene Brown. She talks about vulnerability is the greatest superpower for leaders. I think the reason a lot of people have a hard time on social media, is they have a hard time really being vulnerable. And just saying, look, I'm just a human being just like all of you. I'm a privileged human being. I have a lot of things that luckily kind of came my way, but I'm just a human being. I get scared, I get anxious, I get lonely, all those things. Just like all of you, you know. And really being able to take off your armor of, oh, I'm a CEO. And then when you do that, you are more human. And it's like, this goes back to this concept of human first business. There's no work persona and home persona, there's just you. And I think it's surprising when you start doing it, and I started maybe seven, eight, nine years ago, it's like, wow, the world wants more human leaders. They want you to just be yourself, to talk about your challenges. I had the kids, when we got to 13,000 registrations for Pulse Everywhere, they pied me in the face. And the world wants to see CEOs being pied in the face. Probably that one, for sure, that's a guaranteed crowd pleaser. CEOs being pied in the face. But they want to see what you're into outside of work and the pop culture you're into. And they want to see the silly things that you're doing. They want you to be human. And so I think if you're willing to be vulnerable, which takes some bravery, it can really, really pay off for your business, but I think also for you as a person. >> Yeah, yeah. I think it's so insightful. And I think people are afraid of it for the wrong reasons, 'cause it is actually going to help people, it's going to help your own employees, as well, get to know you better. >> Totally, they love it. >> And you touched on another concept that I think is so important that I think a lot of people miss as we go from kind of the old broadcast world to more narrow casting, which is touching your audience and developing your relationship with your audience. So we have a concept here at theCUBE that one is greater than 1% of 100. Why go with the old broadcast model and just spray and you hope you have these really ridiculously low conversion rates to get to that person that you're trying to get to, versus just identifying that person and reaching out directly to those people, and having a direct engagement and a relative conversation within the people that care. And it's not everybody, but, as you said, within the population that cares about it it's meaningful and they get some value out of it. So it's a really kind of different strategy. So-- >> I love that. >> You're always get a lot of stuff out, but you are super prolific. So you got a bunch of projects that are just hitting today. So as we're getting ready to sit down, I see you just have a book came out. So tell us a little bit about the book that just came out. >> Sure, yeah, it's funny. I need to get my physical copy too at my home. I've got so a few, just for context. Five years ago, we released this first book on "Customer Success" which you can kind of see here. It's surprising really, really popular in this world of SaaS and customer success and it ties, Jeff, to what you just said which is, you don't need to be the book that everyone in the world reads, you need to be the book that everyone in your world reads. And so this book turned out to be that. Thousands of company management teams and CEOs in software and SaaS read it. And so, originally when this came out, it was just kind of an introduction to what we call customer success. Basically, how do you retain your customers for the long-term? How do you get them more value? And how do you get them to use more of what they've bought and eventually spend more money with you? And that's a mega-trend that's happening. We decided that we needed an update. So this second book is called "Customer Success Economy." It just came out, literally today. And it's available on Amazon. And it's about the idea that customer success started in tech companies, but it's now gone into many, many industries, like healthcare, manufacturing, services. And it started with a specific team called the customer success management team. But now it's affecting how companies build products, how they sell, how they market. So it's sort of this book is kind of a handbook for management teams on how to apply customer success to your whole business and we call it "Customer Success Economy" 'cause we do think the future of the economy isn't about marketing and selling transactional products, but it's about making sure what your customers are buying is actually delivering value for them, right? That's better for the world, but it's also just necessary 'cause your customers have the power now. You and I have the power to decide how to transport ourselves, whether it's buying a car or rideshare, in the old world when we could leave our house. And we have the power to decide how we're going to stay in a city, whether it's a hotel or Airbnb or whatever. And so customers have the power now, and if you're not driving success, you're not going to be able to keep those customers. And so "Customer Success Economy" is all about that. >> Yeah, and for people that aren't familiar with Gainsight, obviously, there's lots of resources that they can go. They should go to the show in a couple weeks, but also, I think, the interview that we did at PagerDuty, I think you really laid out kind of a great definition of what customer success is. And it's not CRM, it has nothing to do with CRM. CRM is tracking leads and tracking ops. It's not customer success. So, people can also check that. But I want to shift gears again a little bit because one, you also have your blog, MehtaPhysical, that came out. And you just came out again recently with a new post. I don't know when you, you must have a army of helper writers, but you talk about something that is really top of mind right now. And everyone that we get on theCUBE, especially big companies that have the benefit of a balance sheet with a few bucks in it, say we want to help our customers, we want to help our people be safe, obviously, that's first. But we also want to help our customers. But nobody ever really says what exactly does that mean? And it's pretty interesting. You lay out a bunch of things that are happening in the SaaS world, but I jumped on, I think it's number 10 of your list, which is how to think about helping your customers. And you give some real specific kind of guidance and guidelines and definitions, if you will, of how do you help our customers through these tough times. >> Yeah, so I'll summarize for the folks listening. One of the things we observed is, in this terrible tough times right now, your customers are in very different situations. And for simplicity, we thought about three categories. So the companies that we call category one, which are unfortunately, adversely affected by this terrible crisis, but also by the shutdown itself, and that's hotels, restaurants, airlines, and you can put other folks in that example. What do those customers need? Well, they probably need some financial relief. And you have to figure out what you're going to do there and that's a hard decision. And they also just need empathy. It's not easy and the stress level they have is massive. Then you've got, on the other extremes, a small number of your customers might be doing great despite this crisis or maybe even because of it, because they make video conferencing technology or remote work technology, or they make stuff for virtual or telemedicine. And those folks actually are likely to be super busy because they're just trying to keep up with the demand. So what they need from you is time and help. And then you got the people in between. Most companies, right, where there may be a mix of some things going well, some don't. And so what we recommended is think about your strategy, not just inside out, what you want, but outside in, what those clients need. And so as an example, you might think about in that first category, financial relief. The second category, the companies in the middle, they may need, they may not be willing to spend more money, but they may want to do more stuff. So maybe you unlock your product, make it available, so they can use everything in your suite for a while. And maybe in that third category, they're wiling to spend money, but they're just really busy. So maybe you offer services for them or things to help them as they scale. >> Yeah, so before I let you go, I just want to get your reaction to one more great leader. And as you can tell, I love great leaders and studying great leaders. Back when I was in business school we had Dave Pottruck, who at that time was the CEO of Schwab, come and speak and he's a phenomenal speaker and if you ever get a chance to see him speak. And at that point in time, Schwab had to reinvent their business with online trading and basically kill their call-in brokerage for online brokerage, and I think that they had a fixed price of 19.99, whatever it was. This was back in the late 90s. But he was a phenomenal speaker. And we finished and he had a small dinner with a group of people, and we just said, David, you are a phenomenal speaker, why, how, why're you so good? And he goes, you know, it's really pretty simple. As a CEO, I have one job. It's to communicate. And I have three constituencies. I kind of have the street and the market, I have my internal people, and then I have my customers and my ecosystem. And so he said, I, and he's a wrestler, he said, you know I treated it like wrestling. I hired a coach, I practiced my moves, I did it over and over, and I embraced it as a skill and it just showed so brightly. And it's such a contrast to people that get wrapped around the axle with their ego, or whatever. And I think you're such a shiny example of someone who over communicates, arguably, in terms of getting the message out, getting people on board, and letting people know what you're all about, what the priorities are, and where you're going. And it's such a sheer, or such a bright contrast to the people that don't do that that I think is so refreshing. And you do it in a fun and novel and in your own personal way. >> That's awesome to hear that story. He's a inspirational leader, and I've studied him, for sure. But I hadn't heard this specific story, and I totally agree with you. Communication is not something you're born with. Honestly, you might know this, Jeff, or not, as a kid, I was super lonely. I didn't really have any friends and I was one of those kids who just didn't fit in. So I was not the one they would pick to be on stage in front of thousands of people or anything else. But you just do it over and over again and you try to get better and you find, I think a big thing is finding your own voice, your own style. I'm not a super formal style, I try to be very human and authentic. And so finding your style that works for you, I agree, it's completely learnable. >> Yeah, well, Nick, thank you. Thanks for taking a few minutes. I'm sure you're super, super busy getting ready for the show in a couple weeks. But it's always great to catch up and really appreciate you taking some time to share your thoughts and insights with us. >> Thank you, Jeff, it's an honor. >> All right, he's Nick Mehta, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (soft music)

>> From the Silicon Angle Media Office in Boston, Massachusetts, it's The Cube. Now, here's your host Dave Vellante. >> Hi everybody. Welcome to this Cube Insights powered by ETR. In this breaking analysis we're going to look at recent spending data from the ETR Spending Intentions Survey. We believe tech spending is slowing down. Now, it's not falling off a cliff but it is reverting to pre-2018 spending levels. There's some concern in the bellwethers of specifically financial services and insurance accounts and large telcos. We're also seeing less redundancy. What we mean by that is in 2017 and 2018 you had a lot of experimentation going on. You had a lot of digital initiatives that were going into, not really production, but sort of proof of concept. And as a result you were seeing spending on both legacy infrastructure and emerging technologies. What we're seeing now is more replacements. In other words people saying, "Okay, we're now going into production. We've tried that. We're not going to go with A, we're going to double down on B." And we're seeing less experimentation with the emerging technology. So in other words people are pulling out, actually some of the legacy technologies. And they're not just spraying and praying across the entire emerging technology sector. So, as a result, spending is more focused. As they say, it's not a disaster, but it's definitely some cause for concern. So, what I'd like to do, Alex if you bring up the first slide. I want to give you some takeaways from the ETR, the Enterprise Technology Research Q4 Pulse Check Survey. ETR has a data platform of 4,500 practitioners that it surveys regularly. And the most recent spending intention survey will actually be made public on October 16th at the ETR Webcast. ETR is in its quiet period right now, but they've given me a little glimpse and allowed me to share with you, our Cube audience, some of the findings. So as I say, you know, overall tech spending is clearly slowing, but it's still healthy. There's a uniform slowdown, really, across the board. In virtually all sectors with very few exceptions, and I'll highlight some of the companies that are actually quite strong. Telco, large financial services, insurance. That's rippling through to AMIA, which is, as I've said, is over-weighted in banking. The Global 2000 is looking softer. And also the global public and private companies. GPP is what ETR calls it. They say this is one of the best indicators of spending intentions and is a harbinger for future growth or deceleration. So it's the largest public companies and the largest private companies. Think Mars, Deloitte, Cargo, Coke Industries. Big giant, private companies. We're also seeing a number of changes in responses from we're going to increase to more flat-ish. So, again, it's not a disaster. It's not falling off the cliff. And there are some clear winners and losers. So adoptions are really reverting back to 2018 levels. As I said, replacements are arising. You know, digital transformation is moving from test everything to okay, let's go, let's focus now and double-down on those technologies that we really think are winners. So this is hitting both legacy companies and the disrupters. One of the other key takeaways out of the ETR Survey is that Microsoft is getting very, very aggressive. It's extending and expanding its TAM further into cloud, into collaboration, into application performance management, into security. We saw the Surface announcement this past week. Microsoft is embracing Android. Windows is not the future of Microsoft. It's all these other markets that they're going after. They're essentially building out an API platform and focusing in on the user experience. And that's paying off because CIOs are clearly more comfortable with Microsoft. Okay, so now I'm going to take you through some themes. I'm going to make some specific vendor comments, particularly in Cloud, software, and infrastructure. And then we'll wrap. So here's some major themes that really we see going on. Investors still want growth. They're punishing misses on earnings and they're rewarding growth companies. And so you can see on this slide that it's really about growth metrics. What you're seeing is companies are focused on total revenue, total revenue growth, annual recurring revenue growth, billings growth. Companies that maybe aren't growing so fast, like Dell, are focused on share gains. Lately we've seen pullbacks in the software companies and their stock prices really due to higher valuations. So, there's some caution there. There's actually a somewhat surprising focus given the caution and all the discussion about, you know, slowing economy. There's some surprising lack of focus on key performance indicators like cash flow. A few years ago, Splunk actually stopped giving, for example, cash flow targets. You don't see as much focus on market capitalization or shareholders returns. You do see that from Oracle. You see that last week from the Dell Financial Analyst Meeting. I talked about that. But it's selective. You know these are the type of metrics that Oracle, Dell, VMware, IBM, HPE, you know generally HP Inc. as well will focus on. Another thing we see is the Global M&A across all industries is back to 2016 levels. It basically was down 16% in Q3. However, well and that's by the way due to trade wars and other uncertainties and other economic slowdowns and Brexit. But tech M&A has actually been pretty robust this year. I mean, you know take a look at some examples. I'll just name a few. Google with Looker, big acquisitions. Sales Force, huge acquisition. A $15 billion acquisition of Tableau. It also spent over a billion dollars on Click software. Facebook with CTRL-labs. NVIDIA, $7 billion acquisition of Mellanox. VMware just plunked down billion dollars for Carbon Black and its own, you know, sort of pivotal within the family. Splunk with a billion dollar plus acquisition of SignalFx. HP over a billion dollars with Cray. Amazon's been active. Uber's been active. Even nontraditional enterprise tech companies like McDonald's trying to automate some of the drive-through technology. Mastercard with Nets. And of course the stalwart M&A companies Apple, Intel, Microsoft have been pretty active as well as many others. You know but generally I think what's happening is valuations are high and companies are looking for exits. They've got some cool tech so they're putting it out there. That you know, hey now's the time to buy. They want to get out. That maybe IPO is not the best option. Maybe they don't feel like they've got, you know, a long-term, you know, plan that is going to really maximize shareholder value so they're, you know, putting forth themselves for M&A today. And so that's been pretty robust. And I would expect that's going to continue for a little bit here as there are, again, some good technology companies out there. Okay, now let's get into, Alex if you pull up the next slide of the Company Outlook. I want to start with Cloud. Cloud, as they say here, continues it's steady march. I'm going to focus on the Big 3. Microsoft, AWS, and Google. In the ETR Spending Surveys they're all very clearly strong. Microsoft is very strong. As I said it's expanding it's total available market. It's into collaboration now so it's going after Slack, Box, Dropbox, Atlassian. It's announced application performance management capabilities, so it's kind of going after new relic there. New SIM and security products. So IBM, Splunk, Elastic are some targets there. Microsoft is one of the companies that's gaining share overall. Let me talk about AWS. Microsoft is growing faster in Cloud than AWS, but AWS is much, much larger. And AWS's growth continues. So it's not as strong as 2018 but it's stronger, in fact, much stronger than its peers overall in the marketplace. AWS appears to be very well positioned according to the ETR Surveys in database and AI it continues to gain momentum there. The only sort of weak spot is the ECS, the container orchestration area. And that looks a little soft likely due to Kubernetes. Drop down to Google. Now Google, you know, there's some strength in Google's business but it's way behind in terms of market share, as you all know, Microsoft and AWS. You know, its AI and machine learning gains have stalled relative to Microsoft and AWS which continue to grow. Google's strength and strong suit has always been analytics. The ETR data shows that its holdings serve there. But there's deceleration in data warehousing, and even surprisingly in containers given, you know, its strength in contributing to the Kubernetes project. But the ETR 3 Year Outlook, when they do longer term outlook surveys, shows GCP, Google's Cloud platform, gaining. But there's really not a lot of evidence in the existing data, in the near-term data to show that. But the big three, you know, Cloud players, you know, continue to solidify their position. Particularly AWS and Microsoft. Now let's turn our attention to enterprise software. Just going to name a few. ETR will have an extensive at their webcast. We'll have an extensive review of these vendors, and I'll pick up on that. But I just want to pick out a few here. Some of the enterprise software winners. Workday continues to be very, very strong. Especially in healthcare and pharmaceutical. Salesforce, we're seeing a slight deceleration but it's pretty steady. Very strong in Fortune 100. And Einstein, its AI offering appears to be gaining as well. Some of the acquisitions Mulesoft and Tableu are also quite strong. Demandware is another acquisition that's also strong. The other one that's not so strong, ExactTarget is somewhat weakening. So Salesforce is a little bit mixed, but, you know, continues to be pretty steady. Splunk looks strong. Despite some anecdotal comments that point to pricing issues, and I know Splunk's been working on, you know, tweaking its pricing model. And maybe even some competition. There's no indication in the ETR data yet that Splunk's, you know, momentum is attenuating. Security as category generally is very, very strong. And it's lifting all ships. Splunk's analytics business is showing strength is particularly in healthcare and pharmaceuticals, as well as financial services. I like the healthcare and pharmaceuticals exposure because, you know, in a recession healthcare will, you know, continue to do pretty well. Financial services in general is down, so there's maybe some exposure there. UiPath, I did a segment on RPA a couple weeks ago. UiPath continues its rapid share expansion. The latest ETR Survey data shows that that momentum is continuing. And UiPath is distancing itself in the spending surveys from its broader competition as well. Another company we've been following and I did a segment on the analytics and enterprise data warehousing sector a couple weeks ago is Snowflake. Snowflake continues to expand its share. Its slightly slower than its previous highs, which were off the chart. We shared with you its Net Score. Snowflake and UiPath have some of the highest Net Scores in the ETR Survey data of 80+%. Net Score remembers. You take the we're adding the platform, we're spending more and you subtract we're leaving the platform or spending less and that gives you the Net Score. Snowflake and UiPath are two of the highest. So slightly slower than previous ties, but still very very strong. Especially in larger companies. So that's just some highlights in the software sector. The last sector I want to focus on is enterprise infrastructure. So Alex if you'd bring that up. I did a segment at the end of Q2, post Q2 looking at earning statements and also some ETR data on the storage spending segment. So I'll start with Pure Storage. They continue to have elevative spending intentions. Especially in that giant public and private, that leading indicator. There are some storage market headwinds. The storage market generally is still absorbing that all flash injection. I've talked about this before. There's still some competition from Cloud. When Pure came out with its earnings last quarter, the stock dropped. But then when everybody else announced, you know, negative growth or, in Dell's case, Dell's the leader, they were flat. Pure Storage bounced back because on a relative basis they're doing very well. The other indication is Pure storage is very strong in net app accounts. Net apps mix, they don't call them out here but we'll do some further analysis down the road of net apps. So I would expect Pure to continue to gain share and relative to the others in that space. But there are some headwinds overall in the market. VMware, let's talk about VMware. VMware's spending profile, according to ETR, looks like 2018. It's still very strong in Fortune 1000, or 100 rather, but weaker in Fortune 500 and the GPP, the global public and private companies. That's a bit of a concern because GPP is one of the leading indicators. VMware on Cloud on AWS looks very strong, so that continues. That's a strategic area for them. Pivotal looks weak. Carbon Black is not pacing with CrowdStrike. So clearly VMware has some work to do with some of its recent acquisitions. It hasn't completed them yet. But just like the AirWatch acquisition, where AirWatch wasn't the leader in that space, really Citrix was the leader. VMware brought that in, cleaned it up, really got focused. So that's what they're going to have to do with Carbon Black and Security, which is going to be a tougher road to hoe I would say than end user computing and Pivotal. So we'll see how that goes. Let's talk about Dell, Dell EMC, Dell Technologies. The client side of the business is holding strong. As I've said many times server and storage are decelerating. We're seeing market headwinds. People are spending less on server and storage relative to some of the overall initiatives. And so, that's got to bounce back at some point. People are going to still need compute, they're still going to need storage, as I say. Both are suffering from, you know, the Cloud overhang. As well, storage there was such a huge injection of flash it gave so much headroom in the marketplace that it somewhat tempered storage demand overall. Customers said, "Hey, I'm good for a while. Cause now I have performance headroom." Whereas before people would buy spinning discs, they buy the overprovision just to get more capacity. So, you know, that was kind of a funky value proposition. The other thing is VxRail is not as robust as previous years and that's something that Dell EMC talks about as, you know, one of the market share leaders. But it's showing a little bit of softness. So we'll keep an eye on that. Let's talk about Cisco. Networking spend is below a year ago. The overall networking market has been, you know, somewhat decelerating. Security is a bright spot for Cisco. Their security business has grown in double digits for the last couple of quarters. They've got work to do in multi-Cloud. Some bright spots Meraki and Duo are both showing strength. HP, talk about HPE it's mixed. Server and storage markets are soft, as I've said. But HPE remains strong in Fortune 500 and that critical GPP leading indicator. You know Nimble is growing, but maybe not as fast as it used to be and Simplivity is really not as strong as last year. So we'd like to see a little bit of an improvement there. On the bright side, Aruba is showing momentum. Particularly in Fortune 500. I'll make some comments about IBM, even though it's really, you know, this IBM enterprise infrastructure. It's really services, software, and yes some infrastructure. The Red Hat acquisition puts it firmly in infrastructure. But IBM is also mixed. It's bouncing back. IBM Classic, the core IBM is bouncing back in Fortune 100 and Fortune 500 and in that critical GPP indicator. It's showing strength, IBM, in Cloud and it's also showing strength in services. Which is over half of its business. So that's real positive. Its analytics and EDW software business are a little bit soft right now. So that's a bit of a concern that we're watching. The other concern we have is Red Hat has been significantly since the announcement of the merger and acquisition. Now what we don't know, is IBM able to inject Red Hat into its large service and outsourcing business? That might be hidden in some of the spending intention surveys. So we're going to have to look at income statement. And the public statements post earnings season to really dig into that. But we'll keep an eye on that. The last comment is Cloudera. Cloudera once was the high-flying darling. They are hitting all-time lows. They made the acquisition of Hortonworks, which created some consolidation. Our hope was that would allow them to focus and pick up. CEO left. Cloudera, again, hitting all-time lows. In particular, AWS and Snowflake are hurting Cloudera's business. They're particularly strong in Cloudera's shops. Okay, so let me wrap. Let's give some final thoughts. So buyers are planning for a slowdown in tech spending. That is clear, but the sky is not falling. Look we're in the tenth year of a major tech investment cycle, so slowdown, in my opinion, is healthy. Digital initiatives are really moving into higher gear. And that's causing some replacement on legacy technologies and some focus on bets. So we're not just going to bet on every new, emerging technology, were going to focus on those that we believe are going to drive business value. So we're moving from a try-everything mode to a more focused management style. At least for a period of time. We're going to absorb the spend, in my view, of the last two years and then double-down on the winners. So not withstanding the external factors, the trade wars, Brexit, other geopolitical concerns, I would expect that we're going to have a period of absorption. Obviously it's October, so the Stock Market is always nervous in October. You know, we'll see if we get Santa Claus rally going into the end of the year. But we'll keep an eye on that. This is Dave Vellante for Cube Insights powered by ETR. Thank you for watching this breaking analysis. We'll see you next time. (upbeat tech music)

(bright music) >> Hey welcome back, everybody. Jeff Frick here with theCUBE We are high atop San Francisco in the Sales Force Tower in the new Accenture offices, it's really beautiful and as part of that, they have their San Francisco Innovation Hubs. So it's five floors of maker's labs, and 3D printing, and all kinds of test facilities and best practices, innovation theater, and this studio which is really fun to be at. So we're talking about hybrid cloud and the development of cloud and multi-cloud and continuing on this path. Not only are customers on this path, but everyone is kind of on this path as things kind of evolve and transform. We are excited to have a couple of experts in the field we've got Larry Socher, he's the Global Managing Director of Intelligent Cloud Infrastructure Services growth and strategy at Accenture. Larry, great to see you again. >> Great to be here, Jeff. And Ajay Patel, he's the Senior Vice President and General Manager at Cloud Provider Software Business Unit at VMWare and a theCUBE alumni as well. >> Excited to be here, thank you for inviting me. >> So, first off, how do you like the digs up here? >> Beautiful place, and the fact we're part of the innovation team, thank you for that. >> So let's just dive into it. So a lot of crazy stuff happening in the marketplace. Lot of conversations about hybrid cloud, multi-cloud, different cloud, public cloud, movement of back and forth from cloud. Just want to get your perspective today. You guys have been in the middle of this for a while. Where are we in this kind of evolution? Everybody's still kind of feeling themselves out, is it, we're kind of past the first inning so now things are settling down? How do you kind of view the evolution of this market? >> Great question and I think Pat does a really nice job of defining the two definitions. What's hybrid versus multi? And simply put, we look at hybrid as when you have consistent infrastructure. It's the same infrastructure regardless of location. Multi is when you have disparate infrastructure, but are using them in a collective. So just from a from a level setting perspective, the taxonomy is starting to get standardized. Industry is starting to recognize hybrid is the reality. It's not a step in the long journey. It is an operating model that going to exist for a long time. So it's not about location. It's about how do you operate in a multi-cloud and a hybrid cloud world. And together at Accenture VMware have a unique opportunity. Also, the technology provider, Accenture, as a top leader in helping customers figure out where best to land their workload in this hybrid, multi-cloud world. Because workloads are driving decisions. >> Jeff: Right. >> We are going to be in this hybrid, multi-cloud world for many years to come. >> Do I need another layer of abstraction? 'Cause I probably have some stuff that's in hybrid and I probably have some stuff in multi, right? 'Cause those are probably not mutually exclusive, either. >> We talked a lot about this, Larry and I were chatting as well about this. And the reality is the reason you choose a specific cloud, is for those native differentiator capability. So abstraction should be just enough so you can make workloads portable. To be able to use the capability as natively as possible. And by fact that we now at VMware have a native VMware running on every major hyperscaler and on pram, gives you that flexibility you want of not having to abstract away the goodness of the cloud while having a common and consistent infrastructure while tapping into the innovations that the public cloud brings. So, it is the evolution of what we've been doing together from a private cloud perspective to extend that beyond the data center, to really make it an operating model that's independent of location. >> Right, so Larry, I'm curious your perspective when you work with customers, how do you help them frame this? I mean I always feel so sorry for corporate CIAOs. I mean they got security going on like crazy, they go GDPR now I think, right? The California regs that'll probably go national. They have so many things to be worried about. They go to keep up on the latest technology, what's happening in containers. I thought it was doc, now you tell me it's Kubernetes. It's really tough. So how do you help them kind of, put a wrapper around it? >> It's got to start with the application. I mean you look at cloud, you look at infrastructure more broadly I mean. It's there to serve the applications and it's the applications that really drive business value. So I think the starting point has to be application led. So we start off, we have our intelligent engineering guys, our platform guys, who really come in and look and do an application modernization strategy. So they'll do an assessment, you know, most of our clients given their scale and complexity usually have from 500 to 20,000 applications. You know, very large estates. And you got to start to figure out okay what's my current applications? A lot of times they'll use the six Rs methodology and they say hey okay what is it? I'm going to retire this, I no longer need it. It no longer has business value. Or I'm going to replace this with SaaS. I move it to sales force for example, or service now, etcetera . Then they're going to start to look at their workloads and say okay, hey, do I need to re-fact of reformat this. Or re-host it. And one of the things obviously, VMware has done a fantastic job is allowing you to re-host it using their software to find data center, you know, in the hyperscaler's environment. >> We call it just, you know, migrate and then modernize. >> Yeah, exactly. But the modernized can't be missed. I think that's where a lot of times we see clients kind of get in the trap, hey, i'm just going to migrate and then figure it out. You need to start to have a modernization strategy and then, 'cause that's ultimately going to dictate your multi and your hybrid cloud approach, is how those apps evolve and you know the dispositions of those apps to figure out do they get replaced. What data sets need to be adjacent to each other? >> Right, so Ajay, you know we were there when Pat was with Andy and talking about VMware on AWS. And then, you know, Sanjay is showing up at everybody else's conference. He's at Google Cloud talking about VMware on Google Cloud. I'm sure there was a Microsoft show I probably missed you guys were probably there, too. You know, it's kind of interesting, right, from the outside looking in, you guys are not a public cloud, per se, and yet you've come up with this great strategy to give customers the options to adopt VMware in a public cloud and then now we're seeing where even the public cloud providers are saying, "Here, stick this box in your data center". It's like this little piece of our cloud floating around in your data center. So talk about the evolution of the strategy, and kind of what you guys are thinking about 'cause you know you are clearly in a leadership position making a lot of interesting acquisitions. How are you guys see this evolving and how are you placing your bets? >> You know Pat has been always consistent about this and any strategy. Whether it's any cloud or any device. Any workload, if you will, or application. And as we started to think about it, one of the big things we focused on was meeting the customer where he was at in his journey. Depending on the customer, they may simply be trying to figure out working out to get on a data center. All the way, to how to drive an individual transformation effort. And a partner like Accenture, who has the breadth and depth and sometimes the vertical expertise and the insight. That's what customers are looking for. Help me figure out in my journey, first tell me where I'm at, where am I going, and how I make that happen. And what we've done in a clever way in many ways is, we've created the market. We've demonstrated that VMware is the only, consistent infrastructure that you can bet on and leverage the benefits of the private or public cloud. And I often say hybrid's a two-way street now. Which is they are bringing more and more hybrid cloud services on pram. And where is the on pram? It's now the edge. I was talking to the Accenture folks and they were saying the metro edge, right? So you're starting to see the workloads And I think you said almost 40 plus percent of future workloads are now going to be in the central cloud. >> Yeah, and actually there's an interesting stat out there. By 2022, seventy percent of data will be produced and processed outside the cloud. So I mean the edge is about to, as we are on the tipping point of IOT finally taking off beyond smart meters. We're going to see a huge amount of data proliferate out there. So the lines between between public and private have becoming so blurry. You can outpost, you look at, Antheos, Azure Stack for ages. And that's where I think VMware's strategy is coming to fruition. You know they've-- >> Sometimes it's great when you have a point of view and you stick with it against the conventional wisdom. And then all of a sudden everyone is following the herd and you are like, "This is great". >> By the way, Anjay hit on a point about the verticalization. Every one of our clients, different industries have very different paths there. And to the meaning that the customer where they're on their journey. I mean if you talk to a pharmaceutical, you know, GXP compliance, big private cloud, starting to dip their toes into public. You go to Mians and they've been very aggressive public. >> Or in manufacturing with Edge Cloud. >> Exactly. >> So it really varies by industry. >> And that's a very interesting area. Like if you look at all the OT environments of the manufacturing. We start to see a lot of end of life of environments. So what's that next generation of control systems going to run on? >> So that's interesting on the edge because and you've brought up networking a couple times while we've been talking as a potential gate, right, when one of them still in the gates, but we're seeing more and more. We were at a cool event, Churchill Club when they had psy links, micron, and arm talking about shifting more of the compute and store on these edge devices to accommodate, which you said, how much of that stuff can you do at the edge versus putting in? But what I think is interesting is, how are you going to manage that? There is a whole different level of management complexity when now you've got this different level of distributing computing. >> And security. >> And security. Times many, many thousands of these devices all over the place. >> You might have heard recent announcements from VMware around the Carbon Black acquisition. >> Yeah. >> That combined with our workspace one and the pulse IOT, we are now giving you the management framework whether it's for people, for things, or devices. And that consistent security on the client, tied with our network security with NSX all the way to the data center security. We're starting to look at what we call intrinsic security. How do we bake security into the platform and start solving these end to end? And have our partner, Accenture, help design these next generation application architectures, all distributed by design. Where do you put a fence? You could put a fence around your data center but your app is using service now and other SaaS services. So how do you set up an application boundary? And the security model around that? So it's really interesting times. >> You hear a lot about our partnership around software defined data center, around networking. With Villo and NSX. But we've actually been spending a lot of time with the IOT team and really looking and a lot of our vision aligns. Actually looking at they've been working with similar age in technology with Liota where, ultimately the edge computing for IOT is going to have to be containerized. Because you're going to need multiple modalware stacks, supporting different vertical applications. We were actually working with one mind where we started off doing video analytics for predictive maintenance on tires for tractors which are really expensive the shovels, et cetera. We started off pushing the data stream, the video stream, up into Azure but the network became a bottleneck. We couldn't get the modality. So we got a process there. They're now looking into autonomous vehicles which need eight megabits load latency band width sitting at the edge. Those two applications will need to co-exist and while we may have Azure Edge running in a container down doing the video analytics, if Caterpillar chooses Green Grass or Jasper, that's going to have to co-exist. So you're going to see the whole containerization that we are starting to see in the data center, is going to push out there. And the other side, Pulse, the management of the Edge, is going to be very difficult. >> I think the whole new frontier. >> Yeah absolutely. >> That's moving forward and with 5G IntelliCorp. They're trying to provide value added services. So what does that mean from an infrastructure perspective? >> Right, right. >> When do you stay on the 5G radio network versus jumping on a back line? When do you move data versus process on the edge? Those are all business decisions that need to be there into some framework. >> So you guys are going, we can go and go and go. But I want to follow up on your segway on containers. 'Cause containers is such an important part of this story and an enabler to this story. And you guys made and aggressive move with Hep TO. We've had Craig McLuckie on when he was still at Google and Dan, great guys. But it's kind of funny right? 'Cause three years ago, everyone was going to DockerCon right? That was like, we're all about shows. That was the hot show. Now Docker's kind of faded and Kubernetes is really taking off. Why, for people that aren't familiar with Kubernetes, they probably hear it at cocktail parties if they live in the Bay area. Why is containers such an important enabler and what's so special about Kubernetes specifically? >> Do you want to go on the general or? >> Why don't your start off? >> I brought my products stuff for sure. >> If you look at the world its getting much more dynamic. Particularly as you start to get more digitally decoupled applications, you're starting, we've come from a world where a virtual machine might have been up for months or years to all the sudden you have containers that are much more dynamic, allowed to scale quickly, and then they need to be orchestrated. And that's essentially what Kubernetes does, is really start to orchestrate that. And as we get more distributed workloads, you need to coordinate them. You need to be able to scale up as you need for performance etcetera So Kubernetes is an incredible technology that allows you really to optimize the placement of that. So just like the virtual machine changed how we compute, containers now gives us a much more flexible, portable, you can run on any infrastructure at any location. Closer to the data etcetera to do that. >> I think the bold move we made is, we finally, after working with customers and partners like Accenture, we have a very comprehensive strategy. We announced Project Tanzu at our last VM World. And Project Tanzu really focused on three aspects of containers, How do you build applications, which is what Pivotal and the acquisition of Pivotal was driven around. How do we run these on a robust enterprise class run time? And what if you could take every vSphere ESX out there and make it a container platform. Now we have half a million customers. 70 million VM's. All the sudden, that run time we are container enabling with a Project Pacific. So vSphere 7 becomes a common place for running containers and VMs. So that debate of VMs or containers? Done, gone. One place or just spend up containers and resources. And then the more important part is how do I manage this? As you have said. Becoming more of a platform, not just an orchestration technology. But a platform for how do I manage applications. Where I deploy them where it makes more sense. I've decoupled my application needs from the resources and Kubernetes is becoming that platform that allows me to portably. I'm the Java Weblogic guy, right? So this is like distributed Weblogic Java on steroids, running across clouds. So pretty exciting for a middleware guy, this is the next generation middleware. >> And to what you just said, that's the enabling infrastructure that will allow it to roll into future things like edge devices. >> Absolutely. >> You can manage an Edge client. You can literally-- >> the edge, yeah. 'Cause now you've got that connection. >> It's in the fabric that you are going to be able to connect. And networking becomes a key part. >> And one of the key things, and this is going to be the hard part is optimization. So how do we optimize across particularly performance but even cost? >> And security, rewiring security and availability. >> So still I think my all time favorite business book is Clayton Christensen, "Innovator's Dilemma". One of the most important lessons in that book is what are you optimizing for? And by rule, you can't optimize for everything equally. You have to rank order. But what I find really interesting in this conversation and where we're going and the complexity of the size of the data, the complexity of what am I optimizing for now just begs for plight AI. This is not a people problem to solve. This is AI moving fast. >> Smart infrastructure going to adapt. >> Right, so as you look at that opportunity to now apply AI over the top of this thing, opens up tremendous opportunity. >> Absolutely, I mean standardized infrastructure allows you, sorry, allows you to get more metrics. It allows you to build models to optimize infrastructure over time. >> And humans just can't get their head around it. I mean because you do have to optimize across multiple dimensions as performance, as cost. But then that performance is compute, it's the network. In fact the network's always going to be the bottleneck. So you look at it, even with 5G which is an order magnitude more band width, the network will still lag. You go back to Moore's Law, right? It's a, even though it's extended to 24 months, price performance doubles, so the amount of data potentially can exponentially grow our networks don't keep pace. So that optimization is constantly going to have to be tuned as we get even with increases in network we're going to have to keep balancing that. >> Right, but it's also the business optimization beyond the infrastructure optimization. For instance, if you are running a big power generation field of a bunch of turbines, right, you may want to optimize for maintenance 'cause things are running in some steady state but maybe there's an oil crisis or this or that, suddenly the price rises and you are like, forget the maintenance right now, we've got a revenue opportunity that we want to tweak. >> You just talked about which is in a dynamic industry. How do I real time change the behavior? And more and more policy driven, where the infrastructure is smart enough to react, based on the policy change you made. That's the world we want to get to and we are far away from that right now. >> I mean ultimately I think the Kubernetes controller gets an AI overlay and then operators of the future are tuning the AI engines that optimize it. >> Right, right. And then we run into the whole thing which we talked about many times in this building with Dr. Rumman Chowdhury from Accenture. Then you got the whole ethics overlay on top of the business and the optimization and everything else. That's a whole different conversation for another day. So, before we wrap I just want to give you kind of last thoughts. As you know customers are in all different stages of their journey. Hopefully, most of them are at least off the first square I would imagine on the monopoly board. What does, you know, kind of just top level things that you would tell people that they really need just to keep always at the top as they're starting to make these considerations? Starting to make these investments? Starting to move workloads around that they should always have at the top of their mind? >> For me it's very simple. It's really about focus on the business outcome. Leverage the best resource for the right need. And design architectures that are flexible that give you choice, you're not locked in. And look for strategic partners, whether it's technology partners or services partners that allow you to guide. Because if complexity is too high, the number of choices are too high, you need someone who has the breadth and depth to give you that platform which you can operate on. So we want to be the ubiquitous platform from a software perspective. Accenture wants to be that single partner who can help them guide on the journey. So, I think that would be my ask is start thinking about who are your strategic partners? What is your architecture and the choices you're making that give you the flexibility to evolve. Because this is a dynamic market. Once you make decisions today, may not be the ones you need in six months even. >> And that dynanicism is accelerating. If you look at it, I mean, we've all seen change in the industry, of decades in the industry. But the rate of change now, the pace, things are moving so quickly. >> And we need to respond to competitive or business oriented industry. Or any regulations. You have to be prepared for that. >> Well gentleman, thanks for taking a few minutes and great conversation. Clearly you're in a very good space 'cause it's not getting any less complicated any time soon. >> Well, thank you again. And thank you. >> All right, thanks. >> Thanks. >> Larry and Ajay, I'm Jeff, you're watching theCUBE. We are top of San Francisco in the Sales Force Tower at the Accenture Innovation Hub. Thanks for watching. We'll see you next time.

>> Announcer: Live from San Francisco, celebrating 10 years of high tech coverage, it's theCUBE! Covering VMworld 2019. Brought to you by VMware and its ecosystem partners. >> Okay, welcome back everyone live CUBE coverage here in San Francisco at VMworld 2019 I'm Jon Furrier, my co-host this segment, Stu Miniman. 10 years Stu it's been a long run. A lot of CUBE alumnis around, we got two here. Honore LaBourdette Vice President go-to-market Telco Edge Cloud at VMware. And Lakshmi Mandyam, Vice President Product Manager, go-to-market Edge IoT at VMware. Great to see you, thanks for coming back. >> Thank you for having us. >> So, I think IoT's going to be a pretty big deal. 5G, jury's still out on 5G but it's looking good. Look, if Pat Gelsinger said it's going to be great, it's probably going to be great. What's new? Give us the update. >> Well, just a commentary on 5G, when you say you think it's going to be great, there is some skepticism in the marketplace because if you go back and look at all the different generations of cellular technologies, it's the odd numbers that have never been successful and the even numbers that have, from a monetary perspective for the telcos. Interesting thing about 5G is because it's such a system-oriented technology, that we do believe that it's going to enable a lot of the capabilities associated with IoT, right? So there's an interdependency between 5G and IoT and IoT and 5G that I think is going to make 5G more successful than any of its predecessors. >> All of us are nerds that geek out on RF and physics. I mean 5G has a lot of skeptics but they're deploying 5G, it's not like it's a vaporware. There are deployments going on in the United States, certainly outside of the United States. So it is real, it's actually happening. The question is what will be the impact to the network effect and what's it going to enable, which will certainly impact the industrial IoT and IoT markets. >> Well so one of the things that's happening with the deployments of 5G isn't just the innovation associated with the spectrum technology of five generations of mobile technology, right? There is an entire transformation happening with the core infrastructure of the telco network. And there's an interdependency there as well, right? So as the telco's software define the infrastructure on which they run all of their services, that then extends all the way through from the cloud to the core to the edge for all of the radio access and everything associated with 5G. >> And we're also seeing on the IoT side that there's a similar transformation going on, 'cause right now when you look at kind of example manufacturing, right? There's a real siloed infrastructure, siloed use cases and people are not able to scale and especially when you start to see the business impact that IoT's actually going to have, because most of the data that's being generated is actually being generated from the devices at the edge. And there's a viewpoint that a lot of the workloads that are actually being generated for the enterprise are actually going to be executed at the edge and when you take those things into consideration, it's really important to have an infrastructure that scales. And just like we've seen in other areas where a sprawl of infrastructure is really not going to be be effective in terms of delivering business value. That's the same problem that we see here. >> That brings up a good point. You mention systems view. I think this is interesting 'cause I think this business model innovation, as well as the architecture. I mean, you become what you're known for in the old infrastructure. You don't want that legacy to be dictating the new things, you mentioned backhaul. That's a topic that people talk about in the cellular business. You got the radios, you backhaul through a network, go to the core. But now you're getting at something different where if you're going to be backhauling, which implies moving packets around, moving data has become a really big problem or concern because the cost to move data, the physics involved, latency is a requirement. Processing at the edge becomes the new architecture. >> Yeah, I think the old paradigm was around moving data to the compute but the new paradigm is going to be moving compute to the data, especially on the edge and the IoT. And this is where managing that whole compute infrastructure is going to be really, really important. And that's what, you know, the VMware Telco Edge-- >> Well, we're going to ask Pat Gelsinger a question that riffs off what Dave asked years ago. Stu, I don't remember what year it was, 2012 or 2013, Dave Vellante asked Pat Gelsinger, "Is security a do-over?" You know Pat's very opinionated, he's like, "Absolutely a do-over." Really risky, bold take to say at that time, turns out he was right. The question I want to preview with you guys is, is the architecture a do-over? Because if you think about it, there's new capabilities, you mentioned the systems view. Is there an opportunity, not to throw it away, but like, just rethink it, get a second chance at deploying large scale edge, cloud, versus backhauling through the data center, maybe backhaul through the cloud. So, to me it's just kind of feels like a do-over. >> Well, there's very much an opportunity to, I'll say evolve rather than to do it over, right? 'Cause do-over kind of implies everybody's going to throw out everything that they have. But when you think about the beauty of software is that now we can have inherent security in all of the aspects of the software defined network all the way through the edge. So if you happened to hear Pat's keynote this morning, you know, he put up a slide of all the different security vendors across all of the different types of, the different areas of the clouds, the different cloud technologies and basically said that there is an opportunity now for us to do for security basically what we did for compute and networking and storage, by software defining that. And so that's the opportunity for security is to leverage all of what you can do with a software defined approach and have security be intrinsic to everything from the cloud to the core to the edge. And specifically for IoT. If you think about Lakshmi's comment about pushing the compute to the apps, and pushing the compute where the applications are going to be, or the user is going to be, I think there's going to be a greater requirement for security actually at the edge than even what we see in the cloud today. >> Lakshmi, you know, one of the comments we made is if you looked at the keynote this morning, the virtual machine is not the center of the the discussion. There's, you know, VMware, now plays a lot of places where that VM is not at the center. If you can bring us up to speed, when VMware looks at the edge architectures and how they're going to work with enterprises there, you know, what are the solutions that you're going to bring to bare out of the portfolio? >> Yeah so we have a, you know, when you think about IoT and there's all these things that are out there, oftentimes when someone installed it in the factory they didn't even update the factory settings, the threat surface of that is just expansive. And so, what we're doing with the product that I'm going to talk about, Pulse, we actually life cycle manage these devices, software updating, making sure that they're compliant with IT kind of security and other requirements. And so, what we see is the architecture, is we see kind of this managed infrastructure at the device level, that then feeds into kind of the thin edge, and you heard Pat talk about it this morning, right? Pulse and NSX and VeloCloud for the thin edge and that kind of, it's a continuum really. You can't define-- >> It's difficult to do. >> It's a continuum of compute ranging from very small footprint all the way up to our Dell EMC announcement. BMC on Dell EMC, sorry. >> We actually did some original research back when, you know, GE was putting together their industrial internet and one of the biggest stumbling blocks we saw is that huge gap between the IT and OT, they don't talk. You talk about the telco, that telco role doesn't tie in to the traditional data center world. It's at the edge and some expert comes in and does their piece but, you know, smashing these worlds together is a real challenge. >> What's interest-- >> Oh, I'm sorry. >> I was going to say 5G is the technology that I think is going to create the catalyst for those technologies to come together, right? So you have the enterprise edge, you have the industrial edge, and you have the telco edge. And over time, the more the telcos start pushing compute out to their edge, enterprise push compute out to their edge. And then you have all of these industrial IoT devices. The definition of the edge is going to begin to blur. >> I think this is, I think the IoT, industrial IoT, is probably the most important tech story this generation. It doesn't get as much play as AI, 'cause AI kind of sounds cooler, attracts young kids to be coders, but IoT is really the most important thing because think about the industrial IoT, the threats, cyber threats, cyber security. One hole, one hole and the attacker is in. Just to speak security and critical. >> I actually think it's beyond that because I don't know if you heard Pat talk about his definition of the edge, which is actually that merging of the digital and physical worlds. When you think about that, most of human problems can be solved by great technology, technology for good. And so you think about industries being pushed to produce more, 70% more food with just 5% extra land, or you know, carbon emissions, all of these problems which with good visibility control and management can be solved and that's really what we're trying to do-- >> Yeah, but good intentions, I understand where Pat's coming from. It's good, it's good marketing on the stage but the reality is, is when you roll out the tech to make that happen, if you don't have that security intrinsically pulled in, this means that you got to have the zero trust. But IoT is a different animal on a thin edge, than it is, say a data center. So like, it's just one of those things where we're watching 'cause it's just, there's so many, the service area is so large. >> Yeah, and in fact, one of the things that we're doing in terms of incorporating security in the management is looking at hardware Root of Trust right down to every device that's managed and being able to, you know, to attest whether something is legitimate or not. So we're rolling all of those things into our technologies. >> So, Pat brought up the telco. Earlier on, we were asking some of our guests about the business model on telco, because, you know, telcos have been struggling, they had owned infrastructure. So when you own infrastructure, it's hard to go out of business unless you actually run out of cash, but they had plenty of working capital, but they got to get their business model. You guys have any thoughts on as telco starts to modernize, whether they migrate and modernize or modernize and migrate with cloud, what's hopeful things that you can share that's showing business models for telco? Because 5G, someone's got to pay for it. It's not inexpensive to roll out 5G. >> So, what we're seeing with our telco customers is that they're finally beginning to realize that they can actually accelerate their time of revenue with new services, with a software defined infrastructure. So, I think when first we met, you know, we were in the early stages of developing the market for telco with software defined. But we've crossed the chasm now to where we have over a hundred discreet telcos that are in production on our platform. And so we have proof points that says, "Okay, now they can accelerate time to new revenue". What we're focused on now is helping them extend that out to the edge. And as you know, partners with Lakshmi, we see the telcos as a route to the enterprise market for our edge an IoT solutions. Right, so there's an opportunity for telcos to participate not just in the cloud economy but the edge economy. In terms of the business models, the change is driving the business model transformation. You know, the technology is driving business model transformation. But it's an excellent point. Its operating models are transforming, business models are transforming, and interestingly enough, commercial models are transforming as well. >> Lakshmi, you know the app side's going to be where the growth is now. Getting back to the good thing, once that infrastructure is stable, the apps can come out. So the application development, the microservices, that kind of to me connects that Kubernetes piece to it. That is an opportunity to telco providers, right? >> Yeah, absolutely. I mean again, it's all about deploying and managing applications right at the edge and so the infrastructure that we're building, with all of the announcements that you heard and the features that we're adding into the product profile is really about how do you deploy and manage these applications right down at the device level and that's really where I think it's going to transform. >> A lot of action. >> A study came out just yesterday that the edge market is targeted to be a $4.1 trillion market. >> Yeah, it's going to be huge. >> That's trillion with a T. >> Yeah, it's going to be huge. >> So, wondering what you can say about the ecosystem. Because, you know we've looked, VMware has always had ecosystems but it's many ecosystems, and you've got a cloud marketplace, and there's lots of different customers so will some of your existing partners go along with this, is it building out a new suite, you know, when you look at the edge and IoT? >> I think there will be a group of partners that come along, for sure, but, you know, IoT, especially when you think about industrial IoT, it is a new space of players and we're building that ecosystem and trying to figure out what customers want, right? Because, it's an ocean, you could boil it but that may not be the right approach. >> Yeah, I mean, it's like you said, there's a T on the TAM. It's a huge, huge TAM. It's going to be a huge application boom and IT culture's got to evolve from that perimeter-based security to a surface area that's out there, that's one light bulb on a factory, that IP enabled, could be a malware entry point. It could be something for a worm to get in there. >> Well, it's really like any device. What's that, any-- >> Any device, any application, any device, any Cloud. >> Any cloud, I think in IoT, it's anywhere. >> Anywhere, exactly. >> Totally, totally. >> And to your very accurate point about the security associated with that, right? In the telcos, actually owning that last mile. Right, so when we talk about $4.1 trillion of opportunity, and the need to develop an ecosystem that can support those edge and IoT solutions, the telcos really are in the cat seat to take advantage of that because they own that last mile of customer access, customer influence, they own the cell towers. Right, so as we push compute out to the radio access, telcos have an opportunity to participate. >> Honore, I want to get your thoughts while you're here and Lakshmi, if you can chime in, that's cool too. I'm doing a big editorial on industrial IoT national security. This all kind of leads into policy, potential regulation. You know, I mentioned tech for good, tech for bad is neutral how it's shaped. I'm assuming you guys are going to take a shape in some of those conversations. Any thoughts on regulatory things happening because with cyber security, cyber war that's happening on our digital turf, the telcos are in a prime position to assist and help shape that, you guys can do that. Any thoughts on how you see that, that conversation? Anything you'd like to add? >> So VMware is participating in consortiums associate with those very topics. And of course we are developing technology with an appreciation and understanding respect for the governing agencies across every country as it relates to privacy and security. And so I'm sure, you know and it varies from country to country. In terms of what data you have access to and how you deliver that data and what you do with that data, that's a really hot topic in Washington these days, right? >> And software helps too. >> Software does help, right? You have so much flexibility with software but at the same time you have so much risk that you have to prevent. What we've learned is, it's really about the individual's information. Whether that is a device or an industrial device or an end user or a potentially, a point of presence. It really does depend on what you do with that data, who touches the data, and where is that data going to be housed. And so each of the different countries, each of the different telcos, depending on their location are adhering to the governmental requirements for who does what with the data. >> Yeah, it's interesting, we just did a power panel in our studio, we had experts come in talking about called the "Cybergeddon" scenario, which is a hacker taking over not just malware and getting penetrated with worms and getting access to data, but actually taking over physical devices to harm people. So, this is kind of a nation threat thing. It's not so much a corporate thing, but you know, there is a shaping opportunity here when we're trying to identify where, you know, good governance, at least from a policy stand point, tech are coming together. More and more, it's happening. >> And of course, we participate very actively here in the U.S., right? Because we are a U.S. headquartered company. We try to participate where we can in some of the other countries for the regulatory agencies. And we're a part of the world economic forum. So through that vehicle, you know, through that consortium we're also trying to influence, for good, of course. We just recently, we announced this morning that we acquired Uhana and Uhana is an artificial intelligence machine learning and specific to telco, it will observe, analyze and report back on data all the way to the consumer level across a radio access network. And the one question we get asked from every telco that we do business with is, "What do you do with the data?" And of course, we don't do anything with the data. In that particular technology, we're observing it but we don't necessarily touch it. But you're exactly right, I think it's something that's going to be a hot topic for a time, awhile to come. >> It's an opportunity for tech for good. Guys, thanks for coming on, sharing your insights. Great to see you again, thanks for coming on. Great insights, a lot changing and certainly very relevant, the IoT Edge, telco, IoT's all happening, AI is a part of it. It's theCUBE, live coverage. I'm John Furrier with Stu Miniman. Be right back after this short break. (light techno music)

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> Welcome back, everyone. Live Cube coverage here in San Francisco, California Mosconi North were in the lobby for VM World 2019. I'm John for a day. Volante are 10 years covering VM World's been exciting, Dave, and we've watched all the changes and our next guest is going to illuminate all the benefits at the top of the stack, as I call the end user experience. Shaker Ire, Who's the V S v. P. And general manager End User Computing within VM, where what that means is, he takes care of all the stuff that we're virtualization creates those efficiencies. I think what Palmer's just called end user computing still, but they still have that name back then, if I remember correctly, >> yeah, you >> know the name is stuck because it's ah, it's sort of income, passes all the technologies and uses use right as digital interface is. So that's why it's and use the computing. It's any digital interface that anybody at work uses. Now, the interesting thing is people don't work in an office anymore, and the interface is no longer just a laptop. >> Well, I want to get into some stupid questions around the work environment cause whether you working at a cafe or at home is all kinds of security issues. Also, user experiences. Collaboration software. But let's first get the news out of the way. Digital work, Space news What's the What's going on? The show? What you guys announcing? Yeah, so >> before we get to >> the news that we met me, frame it up a little bit right? Because when you think about organizations today, especially with the changing demographics, where they're going in terms of new devices, the mobility phenomenon, right, the transformation they're going through in terms of just their own cloud and APS and so on, right it. Every every one of those things effects employees, right. And at the end of the day, you know what organizations want is for the employees to have a great experience all the way, as we call it from higher to retire. Not to do that, you know you need a platform because I can just give you a pretty apt running in the laptop and say, Great, that's That's the end of the employees experience, right? It's fundamentally transforming the own whole environment. That's why it's still retains its term and use the computing. And to do that, you have to hit at least three facets, right? One is, of course, How do you deliver a great experience for the employees where they can get any app, any device, anywhere, any form? Anyway, that's one aspect of it. The second aspect of it is from a nightie standpoint. I've gotta manage all this complexity, right, and it's only growing. It's not shrinking with all the head virginity, so there's a management angle of it, and then the tone angle of it is, you know, security. As you pointed out, right security so important. In fact, what you users want is they don't want any security driven compromises. What is an example of security, even compromise, that I have to go through three passwords because he simply don't trust me? Heck, figure it out. Is what the user's Saito I t especially the millennials. Right. So s So you gotta address that. So the platform that we have workspace one actually addresses all three So we have innovations today and news in all three areas, right? So it's an example. Employ experiences, something we've been driving with enterprises and corporations for at least two years. Now we've upped the ante. We have now introducing a virtual assistant that employees can use either through voice or text to essentially ask questions. Hey, what's how do I get into WiFi? What's my employee directory? Um, you know who I go to first? You know this and that, right? As employed onboard the organization. Those examples of virtual assistant can do it. So we released the virtual assistant. That's a big piece of news in the employ experience. Sadie. Another big piece of news is we are introducing a tech preview of what we call digital employees experience management, which means I t now has a user expedient score that they can look at and say, Hey, is David getting a great expedience? No, it's poor, and I can die right in. I can find out the root cause I can fix the issue, and I could do that automatically. >> KP eyes can come out of that right? Absolutely serviceability. >> Absolutely. And I think you know, I've talked to many Cee Io's and we you know, we drive works based one and they for awhile sort of told me, Hey, this is all good. But >> I don't know how I'm doing all my >> doing with respect to, you know, your best best customer. Um, I ahead and behind and far behind. So this really helps them. >> Here. Let me ask the questions. That's a good point I want because this gets down to the heart of the issue. What is the top requests that you're getting from your customers or top two or three features? That pattern that continued comes back from your customer base when it comes to end user computing. These the experience, >> it spends all three things, right? So the first thing is, they're saying, Listen, I want to be able to deliver a great employee experience some, you know, help me do that. Helping measure and make sure I know what journey, Eman That's one right. Second is I've got to set virginity. I've got this complexity of God. You know, I always phones. I've got android tablets. I've got a you know, Dell laptop. I've got a Mac book. I've got you know a rugged device. I've got some work space I ot devices like printers and ex sector X factor. I've got this head virginity. Just help me manage this complexity in a sort of a unified, seamless, uniform way. Right? And third is help me secure my enterprise. So there's a whole model emerging called zero Trust. Where in the old world, what you do is you just build a huge wall around the enterprise, right? A pedometer, and say I'm inside the wall. I need to be domain joined on that inside the fire world. Therefore, I'm good. I mean, you got to throw that out of the window anymore. >> Doesn't exist in your model, because if a millennial or workaround working at home, that means every single i p device on my network potentially a compromise point. >> Correct. So you have You have to start with that device never ought to be trusted. And every network is hostile, right? If you start out for that reminds, then you build trust over time, right? And how do you build trust? You first say you leverage user identity, You say Okay, Davis who he is, right? And so that becomes an identity. You say this device is trusted or partially trusted. So one of the things we're announcing its part of innovations today is what we call workspace to risk analytic, which means we're able to provide a risk or write for the device. And we can say, Hey, this device is a risk on a score of 1 to 10 of eight, which means I can mostly trust it. Maybe you don't trust the sensitive apse. So therefore, a block access to the most sensitive apse, right? So use a combination of different things. They use things like NSX micro segmentation to your point about how we build on the Via Mary Stack. The carbon black acquisition is phenomenal because it gives us that intelligence. So collectively, we're able to sort of implement the zero trust model. Right. So >> those are the >> three main topics, right? Is employed expedience, unified management and zero trust security are really, really >> important. I want to ask you about your tenure, gm, where coincided with the air watch expedition. And prior to that event theme, we're struggled in this space. Ana Citrix dominated your pre Gerald. You know, your former company kind of fumbling around in air watch now. Air watch, if I recall correctly from wrong was not like the number one player. Just like people are saying carbon blacks, not the number one player. Absolutely. And then you get into the VM where flywheel effect or Sanjay Putin came in and it was great leader. But I wonder if you could sort of describe the ascendancy of the end user computing business at at VM wear. And I'm curious you mentioned carbon black and you kind of replicate that with our end point cloud security, peace. There's obviously a security use case. You clearly just described it, but take us back to >> great, great, great question. So actually, I joined right when literally, maybe a month before the air watch acquisition. Right then. So a Sandy and I and the rest of the team sort of worked this. We said, Hey, listen, a watch is a phenomenal sort of mobile management and security player. We had a very good product and horizon VD I, but it was a little bit isolated, and there were others, like, say, tricks that are sort of motor head in that space. So what? The first thing we did is we have three assets. Actually, the third I said what we had a Fed rated identity asset that we had purchase, but not leverage. So we said he know what the identity really has to get coupled with. You know, the death star pulled the mobile world, so we actually took these three piece parts and started integrating it as he started integrating it. We said, You know, this actually forms a very interesting work space, and we said It's a digital work space to be sort of coined that term and started to really tight together. The experience is a user would have, whether they were in a mobile device, a physical desktop or a virtual desktop right and made that seamless. So that's when the work's based one app was born and this was probably around the 2015 time frame. So we started releasing it, and then we started to stitching together basically all the back and integrations, right, So out >> of >> this out of that was born a workspace. And so, in 2016 with the momentum of the workspace, desktop business came back because now it had it been on. We've done a lot of work on the desktop businesses. Well, we made it very competitive with Citrix. We bought volumes. We integrated that we made it actually the best media solution. The markets, with a tremendous traction by itself in the horizon space and then integrating it works with people, said You know what, I need to get that workspace. And why am I dealing with Citrix this horizon solution within workspace in a more than salts my problem. In fact, it's better in certain areas. So that sort of got momentum going around that we really built that workspace momentum. And that was, I would say, till about 2016 or so. And then we saw these three things coming up. One is Hey, employees, experience matters. We really started pouring effort into the employees experience from, you know, day one day two and beyond. And then recently, including this show, we added divided sort of Day zero and then the off boarding pieces. Well, so employees experience became sort of the lightning rod for why somebody would adopt this workspace one platform which were built by then, right, and then we added on this ability to do modern management, especially on Windows and Mac, which was really starting to take off last year completely. Darden rounded out that portfolio and handsome capability, and then we added Now zero trust model, which is which is now sort of bolstered by the acquisition of carbon black. So you can see this a set off cascading talk, full moves. But we did it in a way where, you know, it was really truly integrated. So when as we come out with carbon black now, one of the most interesting things is right when carbon black comes into the fold, we've already done the integration. We're actually going to show it on my keynote right after this, right? We're actually showing the integration between workspace one intelligence and carbon backs You There you have it. You already have an asset that's completely integrated. >> So the risk or is interesting to me as well, so as endpoint security, because much, much more importantly, no fishing is you know, the big way that people get give up credentials. Does >> any of >> this seep into machines and I ot and edge? >> Yeah, and fabulous question. >> Wonder if you could come. >> Absolutely. I think listen, be if you think about risk oars and if >> you think about >> risks at large and devices they've been largely and Windows devices and not to and blame it on Windows, I think they might accept in a fabulous job of sort of progressing windows. But by far it's the most used operating system in the enterprise, right? But Mobile is getting used there. There, you know, it's starting to make a huge starting take a large part of the real estate of the enterprise. So I think we have a unique opportunity now through the data we collect on mobile devices with workspace one using the underlying air watch technology coupled with some of the, um, you know, data that, you know, data analytics tools we have in the carbon black cloud and the way they do sort of threat analysis and, uh, and determine potential attack vectors. We have an opportunity to leverage that intelligence. And that day, the lake and that technology, coupled with the data, we have to really now build a broader sort of threat surface understanding across multiple devices, and eventually that goes into a I ot. Right. So we're actually going to be working with some of the other technologies we have in Wimmer called Paul's Right. Pulse is very interesting because they have the ability to speak multiple device protocols that nobody does. Okay, so we're gonna take advantage of them potentially to sort of be able to start to poke into devices that are attached to the office, but not quite attached to the office. In the sense they're not mainstream devices you and I would use. But indirectly, you may use it, right? So be able to sort of get a much broader view off a visibility of devices. Second is how to manage them through a combination of workspace, one impulse and third, to get the data so that we can feed it into this federated cloud of workspace one intelligence and carbon black to understand the risk. And that way you have this three prom thing, right? I >> wanna ask you a personal question. Pat gal singer was very prolific this week again. Props of in social Media, Mojo doing a selfie on stage with Craig. Job ate up. Yeah, um, doing a little morning thing, telling people how he prepares for his keynote. Yeah. So how do you prepare for your keynote. Do you like, give it for a M and hit the gym and get a job coming up right after this interview? >> I do. I I I'm not fat. That's incredibly disciplined, I think. I think it's been waking up at 4 a.m. for a long time, so I'm not that much of an early bird. But I prepare because, you know, I've been involved in the construction of the keynote. So for me, it's, um, be started work on this, probably about three months ago, because the story came together. It's very natural to me. Just like you asked me the question. You know, tell me about the evolution. It's just a very natural thing because, like telling you >> on relevant story, not just beady eye. Yeah, it's so much more now. >> It's so much more And, you know, and I've lived through this and I participated in most of the decision making, so, you know, when my head of product marketing company said, Hey, what should we do with the keynote? I said, You know, I have the storyline in mind, right? And sit on the same three or four pillars I'm talking to you about, right? How do we tell the story to the audience about what is the platform? Why should they sort of bet on it? How did they sort of deploy it, show them some real world examples and then basically sprinkling all the innovations? That sounds exciting. So? So because of that story lines always being in my head. So it's not that hard. It's just sometimes you just need to sort of a CZ. You're unstable. >> You're preparing Saul, you're part of Yeah, I was handing it to you. Nobody related it. So >> for me, I think it's just sometimes just rehearsing some of the key parts. And then, of course, the visual cues. And they >> want to slam home the big point. They go. You know, I've been looking at your career. You have to check your technologies, but also, you're pretty much been a product leader. Yeah, and your career definite. So I gotta ask you around from the big movements in the innocent. Like your perspective as a participant. This was a product leaders Well, executive in there and done that. Amazon introduced their first conference around cloud security called reinforces. Here we get Cube coverage there. It was interesting because it wasn't like a typical security conference like black hat. Definitely on our say wasn't so much I t is really about cloud security. And so Dave and I were speculating like, this is the first cloud security show. I mean, dedicated to kind of cloud security didn't say cloud security, but essentially, cloud security. >> What is >> your take on the cloud security? Because a >> little bit >> of a different view, little bit architectural change. If you gotta have the on premises, you're gonna have the cloud if things any working together, some things you're doing and security quite frankly, around isolation to, you know, working in in any environment. You're that year in the middle of it all. >> Yeah. >> What is cloud >> security and why I have a conference isn't relevant with your thoughts. >> That's a >> great question. I think you know, you see many of these trends, I think, you know, listen, many of these conferences, they provoke their thought provoking, so it forces you to think right? So when I think about cloud security now, traditionally when you think about cloud security, you would think about technologies like Cass be light cloud access service broker. You would think about encryption to means much more than I do >> all the usual stuff in the back. If he's there, other people are there. But no. >> Yeah, I mean more than my coffee. I think you know you. It's sort of you think of the the the NL unlocked to cloud securities Data center security where you think of the sort of Amazon cloud living in Amazon Data Center. And, you know, how can we protect the, you know, the data and the egress access into those cloud and in the same technology sort of apply, but to your point that you sort of just touched upon its That cloud is not living in isolation. First of all, that Amazon Cloud is connected to a whole bunch of, you know, applications that are still sitting in the data center. Right. So they were not there. Potentially not moving the Oracle database today isn't there moving some workloads to the cloud, right? That's what most most companies are. Hey, guess what? There's all these end points of the connecting the connecting both the data center in the cloud. You're not gonna proxy to the cloud to get to the data center. So there's gateways. So do me. Cloud security can't be an isolated, you know, sort of technology that companies have to sort of think about now is there Is there opportunity to leverage the cloud to manage security better and get visibility in the security environment to do security? Analytics? Absolutely. So I think to me, that's where it's going. Because security, I think, has been proven, is no longer. You know, the one sing single thing. It's just you have to do multiple things. Every time I go talk to CSO's, they tell me they got this technology. I said, Hey, wait a minute. You you have 20. Did you cut down any yet? We've got down a few, but you know, they're just nervous about cutting down too much. Because of that one piece of software >> insurance policy. They're insecure. >> They cut to the added four, >> another tool. Bullshit. I think I think the architecture will get simpler because it's way too complex, but the same time I think you have to. There's no sustenance, cloud security and network security or endpoint security, and >> maybe there's a whole new group emerging within VM where that you could add to your repertoire en Pointe computing group your end user computing. Why don't have endpoint computing? That's >> what you're holding >> is you know is all about what do we need to do for the user? Both as I t and the end user? Okay. And now he now folks like hr and so on, the securities has to be built into it, right? So much like that. I think when you go build our data centers are the public cloud and build this hybrid clouds, security is to be built into that as >> well. We'll shake our thanks for coming on and sharing your insights. A super important area. We're gonna be covering this. This is cloud to point of this end user computing. This is where the edge of the network is. That's where the people are. They are part of the edge. A thin part of the edge of a big part of the edge. You're gonna be in the middle of it will be following the attraction. Thanks for coming on. You So much for having me having played Cuba, Cuba live here in San Francisco on chopper develop the state tune from or we have two sets. Three days of wall to wall coverage, worldly in day one. Stay with us. We gotta have Michael Dell. Pat Nelson. Come on Tomorrow and a lot more guests coming onto. They stay with us. We'll be right back.

(cheerful music) >> Live from San Francisco. It's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome to theCUBE, I'm Lisa Martin. We are at day one of IBM Think 2019, I'm with Dave Vellante. Hey Dave! Hey Lisa, good to see you. The new improved Moscone. >> Exactly, and Stu Miniman, yeah. >> Shiny. >> Yeah, this is the new, it is shiny, The carpets smells new. This is the second annual IBM Think, gentleman where there's this conglomeration of five to six previous events. Doesn't really kick off yet today. I think Partner World starts today but here we are in San Francisco. Moscone North, I think south, and west they have here expecting about 25,000 people. No news yet today, Dave, so let's kind of talk about where IBM is right now with the early part of Q1 of 2019. Red Hat acquisition just approved by shareholders last month. What are your thoughts on the status of Big Blue? >> Well, I think you're right, Lisa, that the Red Hat news is the big news for IBM. We're now entering the next chapter but if you look back for the last five years IBM had to go out and pay two billion dollars for a soft layer to get into the cloud business. That was precipitated by the big, high profile loss of the CIA deal against Amazon. So that was a wake up call for IBM. So they got into the public cloud game. So that's the good news. The bad news is the public cloud's not easy when you're going up against the likes of Google and Microsoft and of course, Amazon. But the linchpin of IBM's cloud strategy is it's SAS portfolio. Over the last 20 years Steve Mills and his organization built a very large software business which they now have migrated into their cloud and so they've got that advantage much like Oracle. They're not a big, dominant cloud infrastructure as a service player but they have a platform where they can put things like Cognitive Solutions and Watson and offer those SAS services to clients. So you'll check on that but when you'll peel through the numbers IBM beat it's numbers last quarter. Stock was up. You know, when it announced the Red Hat acquisition the stock actually got crushed because when you spend 34 billion dollars on a company, you know the shareholders don't necessarily love that but we'll talk about the merits of that move. But they beat in the fourth quarter. They beat on the strength of services. So IBM remains largely a services company, about 60% plus of it's revenues comes from services. It's a somewhat lower margin business, even though IBM margins have been ticking up. As I say, you go back the last five, six years IBM Genesys did Mike's it's microelectronics business, which was a, you know, lost business. It got rid of it's x86 business which is a x86 server business, which is a low margin business. So again, like Oracle, it's focusing on high margin software and services and now we enter the era, Stu, of hybrid cloud with the Red Hat acquisition. A lot of money to pay, but it gets IBM into the next generation of multi cloud. >> Yeah, Dave, the knock I've had against IBM is in many ways they always try to be all things to all people and of course we know you can be good at some things but, you know, it's really tough to be great at everything. And, you know, you talked about cloud, Dave, you know, the SoftLayer acquisition to kind of get into public cloud but, you know, IBM is not one of the big players in public cloud. It's easy. It's Amazon and then followed by you know, Azure, Google, and let's talk Alibaba if we're talking globally. In a multi cloud world IBM has a strong play. As you said, they've got a lot of application assets, they have public cloud, they partner with a lot of the different cloud players out there and with Red Hat they get a key asset to be able to play across all of these multi cloud environments whether we're talking public cloud, private cloud, across all these environments. IBM's been pushing hard into the Kubernetes space, doing a lot with Istio. You know, where they play there, in Red Hat is a key piece of this puzzle. Red Hat running at about three billion dollars of revenue and paying 34 billion dollars but, you know, this is a linchpin as to say how does IBM stay relevant in this cloud world going forward? It's really a you know, a key moment for IBM as to what this means. A lot of discussion as to you know, it's not just the revenue piece but what will Red Hat do to the culture of IBM? IBM has a strong history in open source but you know, you got to, you have a large bench of Red Hat's strong executive team. We're going to see some of them here at the show. We're even going to have one Red Hat executive on our program here and so what will happen once this deal finally closes, which is expected later this year, probably October if you read, you know everything right. But what will it look like as to how will, you know, relatively small Red Hat impact the larger IBM going forward? >> Well, I think it's a big lever, right? I mean we were, Lisa, we were at Cisco Live in Barcelona last week kind of laying out the horses on the track for this multi cloud. Cisco doesn't own it's own public cloud. VMware and Dell don't own it's own public cloud. They both tried to get into the public cloud in the early days and IBM does own it's own public cloud as does Oracle but they're also going hard after this notion of multi cloud as is Cisco, as is VMware. So it sort of sets up the sort of Cisco, IBM Red Hat, VMware, Dell, sort of competing to get after that multi cloud revenue and then HPE fits in there somewhere. We can talk about that. >> So I saw a stat the other day that said in 2018, 80% of companies moved data or apps from public cloud. Reasons being security, control, cost, performance. So to some of the things I've read, Dave, that you've covered recently, if IBM isn't able to really go head to head against the Azures and the AWS, what is their differentiator in this new, hybrid multi cloud world? Is it being able to bring AI, Watson, Cognitive Solutions, better than their competitors in that space that you just mentioned? >> Yeah, IBM does complicate it. You know and cloud and hybrid cloud is complicated and so that's IBM's wheelhouse. And so it tends not to do commodity. So if it's complicated and sophisticated and requires a lot of services and a lot of business processing happening and things like that, IBM tends to excel. So, you know, if you do the SWOT analysis it's big opportunity is to be that multi-cloud provider for it's largest customers. And the larger customers are running, you know, transaction systems on mainframe. They're running cognitive systems on things like power. They've got a giant portfolio, at IBM that is, and they can cobble things together with their services and solve problems and that's kind of how IBM approaches the marketplace. Much different than say, Stu, Cisco or VMware. >> Yeah, Dave, you're absolutely right. You know one of the things I look at is you know, in this multi-cloud space we've see the SI's that are very important there. Companies like Accenture and KPMG and the like. IBM partners with them but IBM also has a large services business. So, you know who's going to be able to help customers get in there and figure out this rather complicated environment. So we are definitely one of the things I want to dig into this week is understand where IBM is at the Cisco Show, Dave. We've talked about their messaging was the bridge to you know what's possible. You know meet the customers where they are, show them how to reach into the future and from Cisco's standpoint, it's strong partnerships with AWS and Google at the forefront. So IBM has just one of the broadest portfolios in the industry. They absolutely play in every single piece but you know customers need good consulting as to Okay, what's going to be the fit for my business. How do I modernize, how do I go forward? And IBM's been down this trip for a number of years. >> Well the in the legacy of Ginni Rometty, in my opinion is going to be determined by the pace at which it can integrate Red Hat and use Red Hat as a lever. Ginni Rometty, when she was doing the roadshow with Jim Whitehurst kept saying it's not a backend loaded deal, and the reason it's not a backend loaded deal is because IBM is a 20 plus billion dollar outsourcing business and they're going to plug Red Hat right into that business to modernize applications. So there's a captive revenue source for IBM. In my view they have to really move fast, faster than typically IBM moves. We've been hearing about strategic initiatives and cloud, and Watson and it's been moving too slow in my opinion. The Red Hat acquisition has to move very very quickly. It's got to move at the speed of cloud and that's going to determine in my opinion-- >> So, actually, so a couple of weeks after the acquisition Red Hat had brought in an analyst to hear what was going on, and while the discussion is Red Hat will stay a distinct brand, there's going to be no lay offs were >> Yeah absolutely. >> Going to keep them separate, what they will get is IBM can really help them scale so >> Yep. Red Hat is getting into some new environments, you know that whole services organization, Red Hat doesn't have that. So IBM absolutely can plug in there and we think really accelerate, the old goal for Red Hat was okay how do we get from that three billion dollars to five billion dollars in the next couple of years. IBM thinks that they can accelerate that even faster. >> And Lisa I think the good news is IBM has always had an affinity toward open source. IBM was really the first, really to make a big investment you know they poured a billion dollars into Linux as a means of competing with Microsoft back in the day, and so they've got open source chops. So for those large IBM customers that might not want to go it alone on open source and you know Red Hat's kind of the cool kid on the block. But at the same time, you know there's some risks there. Now IBM can take that big blue blanket wrap it around it's largest customers and say okay, we've got you covered in open source, we've got the Red Hat asset, and we've got the services organization to help you modernize your application portfolio. >> One of the things too that Stu, you brought up a couple minutes ago is culture. And so looking at what, Red Hat estimates that it's got about eight million developers world wide using their technologies and this is an area that IBM had historically not been really focused on. What are some of the things that you're expecting to hear this week or see this week with respect to the developer community embracing IMB? >> Yeah and Lisa it's not like IBM hasn't been trying to get into the developer community. I remember back at some of the previous shows Edge and Pulse and the like, they would have you know Dev at and try to do a nice little piece of it but it really didn't gain as much traction as you might like. Compare and contrast that with cisco, we've been watching over the last five years the DevNet community. They've got over half a million developers on that platform. So you know, developer engagement usually requires that ground level activity where I've seen good work from IBM has been getting into that cloud native space. So absolutely seen them at the Kubernetes shows working in the container space very heavily and of course that's an area that Red Hat exceeds. So the Linux developers are absolutely there. Now you mentioned how many developers Red Hat has and in that multi cloud, cloud native space, you know Red Hat one of the leaders if not kind of the leader in that space and therefore it should help super charge what IBM is doing, give them some credibility. I'd love to see how many developers we see at this show, you know, you've been to this show Dave and you've been to this show before, it looked more enterprisey to me from the outside-- >> Well, I'm glad you brought up developers because that is the lynch pin of the Red Hat acquisition. If you look at the companies that actually have in the cloud that have a strong developer affinity obviously Microsoft does and always had AWS clearly does Google has you know it's developer community. Stu you mentioned Sisco. Sisco came at it from a networking standpoint and opened up it's network for infrastructure's code. One of the few legacy hardware companies that's done a good job there. VMware, you know not so much. Right? Not really a big developer world and IBM has tried as you pointed out. When they announced Bluemix but that really didn't take off in the developer world. Now with Red Hat IBM, it's your point eight million developers. That is a huge asset for IBM and one that as I said before it absolutely has to leverage and leverage fast. >> And what are you expectations in terms of any sort of industry deeper penetration? There's been some big cloud deals, cloud wins that IBM has made is recent history. One of them being really big in the energy sector. Are you guys kind of expecting to see any sort of industry deeper penetration as a result of what the Red Hat Acquisition will bring? >> Well thats IBM's strength. Stu you pointed out before, it's Accenture, you know Ernie Young, to a lesser extend maybe KPNG but those big SI's and IBM. When IBM bought PWC Gerstner transformed the company and it became a global leader with deep deep industry expertise. That is IBM's you know, savior frankly over these past many many years. So it can compete with virtually anybody on that front and so yes absolutely every industry is being transformed because of digital transformation. IBM understands this as well as anybody. It's a boon for services, it's a good margin business and so that's their competitive advantage. >> Yeah I mean it ties back into their services. I think back when I lived on the vendor side I learned a lot of the industry off of watching IBM. I see how many companies are talking about smarter cities. IBM had you know a long history of working In those environment's. Energy, industrial, IBM is very good at digging into the needed requirements of specific industries and driving that forward. >> So we're going to be here for four days as we mentioned, today is day one. We're going to be talking a lot about this hybrid multi-cloud world. But some of the double clicks we're going to do is talking about data protection, modern data protection, you know a lot of the statistics say that there's eighty percent of the worlds data isn't searchable yet. We all hear every event we do guys, data is the new oil. If companies can actually harness that, extract insights faster than their competition. Create new business models, new services, new products. What are your expectations about how, I hear a lot get your data AI ready. As a marketer I go, what does that mean? What are your thoughts Stu on, and we're sitting in a lot of signage here. How is IBM going to help companies get AI, Data rather AI ready and what does that actually mean? >> So IBM really educated a lot of the world and the broader world as to what some of this AI is. I mean I know we all watched many years ago when Watson was on Jeopardy and we kind of hit through the past the peak and have been trying to sort out okay well how can IBM monetize this? They're taking Watson and getting it into healthcare, they're getting it into all these other environments. So IBM is well known in the AI space. Really well known in the data space but there's a lot of competition and we're still relatively early in the sorting how this new machine learning and AI are going to fit in there. You know we spent a lot of time looking at things like RPA was kind of the gateway drug of AI if you will robotic process automation. And I'm not sure where IBM fit's into that environment. So once again IBM has always had a broad portfolio they do a lot of acquisitions in the space. So you know how can they take all those pieces, pull them together, get after the multicloud world, enable developers to be able to really leverage data even more that's possible and as you said you know more than eighty percent of data today isn't used, you know from an infrastructure stand point I'm looking at how do things like edge computing all get pulled into this environment and lot of questions still. >> IBM is going after hard problems like I said before. You don't expect IBM to be doing things like ad serving with Alexa. You know that's not IBM's game, they're not going to appropriate to sell ad's they're going to take really hard complex problems and charge a lot of money for big services engagements to transform companies. That's their game and that's a data game for sure. >> It's a data game and one of the pieces too that I'm excited to learn about this week is what they're doing about security. We all know you can throw a ton of technology at security and infrastructure but there's the people piece. So we're going to be having a lot of conversations about that as well. Alright guys looking forward to a full week with you and with John joining us at IBM Think I'm Lisa Martin for Dave Vellante and Stu Miniman. You're watching theCUBE live day one IBM Think 2019. Stick around we'll be right back with our next guest. (energetic electronic music)

>> Live from Las Vegas, it's theCUBE! Covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome back to VMworld day three, continuing coverage for theCUBE, I'm Lisa Martin with Dave Vellante sporting this fantastic salmon tie, and what you can't see is the matching salmon pants. Dave- >> There ya go. I still have my voice. (laughs) >> The outfit game is on point, Dave. >> Thank you. >> So we've been here, this is our third day, this is a huge event, 25,000 or so people here, lots of great announcements. We're excited to welcome to theCUBE for the first time Mimi Spier the Vice President of the Internet of Things Business at Vmware, Mimi, it's great to have you! >> Thank you! I'm so happy to be here. >> Thanks for comin' on. >> Yeah, it's great. >> So, three action packed days, lot's of announcements, lots of momentum. You lead a team at VMware that launched the VMware IoT business about a year and a half ago, including, launching the product, the GTM strategy, the partner in marketing strategy. In the last year and a half, talk to us about the evolution of VMware IoT, the business challenges that you're helping customers to solve. >> Absolutely, so, this has been a journey for almost a couple years now, and, VMware saw a need to really start to look at what we'll call the edge or IoT use cases. Our customers started coming to us saying "Wait a minute, this is coming, I know my business units are starting to invest in IoT, I have no control over it, I have no exposure to it, what should I do?" And, we are really committed to being an infrastructure company, we knew that when started this journey, and we said "We really want to focus on infrastructure, but we want to help our customers go to the edge, really start to embrace this new opportunity in the industry, to be able to take advantage of this data." We call it, the data is the gold, how do you actually be able to take advantage of it? So, we're really excited, we just started the journey and now we've really this VMworld is where the momentum is starting to take off. >> How do you look at that opportunity? Because it's complicated, especially for a bunch of IT people, right? And now you're entering this world of operations technology. But how do you sort of look at the landscape of the market? >> I'm really glad you asked that, 'cause that's one of my favorite topics, so. I want our customers to think about, first of all, what are the mission critical objectives of their business? They shouldn't do IoT just to do IoT, they need to do what's right for their business; but I also think it's important that they look beyond that. So, if you look at some of the macro trends happening in the world today, there will need to be 70% more food that's created, and there's only 5% more land that it can be built on. There's going to be 300 million connected cars out on the roads There was a statistic that there will be two thirds of energy is consumed by cities, yet we still have very old ways of doing it, but it's in this very consolidated area; why would we not take advantage of that? So I think industries, whether you're in energy or you're in smart cities or you're in automotive, you have to really think about where is your industry going? And even IT people need to think about this, I think, and I'll explain why in a minute but, how can I actually create an industry and a company that can sustain in this future world, and also contribute to the future of what our world's going to be like. So I think, and the technology, and the way we set this up, and the architecture, is really the foundation to do that. So, that's where VMware comes in. >> Okay. And talk a little bit more about VMware's specific strategy as it relates to IoT. I mean I was at the big Dell announcement last fall. Okay, so you've got Dell sort of with existing relationships actually with a lot of the industrial giants. But now enter VMware, what's your strategy? >> So, first I want to say that Dell and VMware have come together into one big business unit to solve IoT and edge. And the beauty of that is we believe that our customers can really have a more simplistic way of achieving this infrastructure foundation, if we can offer these end-to-end solutions together; so I'll talk about how the Dell piece fits into the VMware strategy. But what VMware's trying to do is drastically simplify the complexity of the infrastructure and the foundation you'll need for IoT. So we want to extend what we're doing in the cloud and the multi-cloud, because we fundamentally believe most of our customers are actually in multiple clouds, private, public, multiple public, and actually be able to extend that down to whatever edge they need as well. Because of the amount of data that will be generated at the edge, there's going to be, I don't know, analysts say 50 to 75% of data will be generated at the edges of our business by 2020. And think about it, all of our applications today are in the cloud, so there must be edge computing that is local to be able to process that data. And there also needs to be, there's this heterogeneous set of devices that will need to be managed, monitored, secure, and collect that data; so this requires, it's complex, so we want to drastically simplify that and that's the overarching part of our strategy. But we also want to allow our customers to do it in a way that's secure, that's scalable, and that's manageable over time, so. >> So does that mean putting some, first of all the Dell partnership is interesting, and Alan Cohen one of our guest analysts this week said "Partnerships used to be like tennis, one-on-one, and now partnerships are like soccer." There's just so many parts of the ecosystem so that's sort of one observation, but. Are you sort of bringing VMware to the edge? Is that? >> We are, so we're bringing VMware to the edge, we announced a new portfolio of solutions called VMware Edge it will take advantage of the ability to do the compute edge which is the processing at the edge, and really extending our hyper-converge technology as a service, like we're doing for VMC on AWS, to the edge; and it includes our device edge, and there's a lot of things that is happening on the device edge, which is like gateways and things, that we want to help provide a more software-defined approach, as well as ensure that those can be managed, monitored, secure, across all the diverse set of devices. Now, you can't do that alone. The ecosystem you mentioned, I've never seen any in my history of my career the amount of collaboration that's going on across the ecosystem, because IoT is so hard; so, you really do need to collaborate. And we are collaborating with the IoT platform providers, the gateway and the thing providers, the hardware providers, the system integrators; it requires that to be successful. But what we want to do with Dell is do it in a way that we offer these end-to-end solutions so that it's just more simple, you can go to one place to consume it, to ensure that it gets deployed, and to actually support that solution, but offering it from a multitude of our partners, typically so. >> So let's dig into to simplicity because we hear that, Mimi, all the time, as you do too. Customers want choice, they want simplicity, right Dave? They want flexibility. >> They want it all! >> They want it all! We all want it all. But how is the VMware edge computing strategy, the technology level, actually facilitating simplicity, in what is inherently a complex world of multiple devices, multiple clouds, et cetera? Talk to us about the technology and the actual enablers of that simple approach they need. >> I'm so glad you asked me that! So, we've been saying very consistently, that we want to offer consistent infrastructure, consistent operations, but we want to give you the choice of your application platform or development platform. We're going to do the exact same thing at the edge. So everything that VMware customers experience in their private cloud, their SDDC solution, private cloud, public cloud, we are now going to offer as a service at the edge same infrastructure, same operational model as the HyperCloud model, but at the edge; with the choice of the application development tools that they would like, because, they might want Greengrass from Amazon, they might want the Azure, they might IoT Watson, whatever they want at the edge we want to be able to support that on our infrastructure, but still maintaining that simplicity of a consistent infrastructure no matter where you choose to run your applications. We want to just eliminate the even thought process, run your applications anywhere, on a consistent infrastructure, with the same management, the same operations, and move 'em around as much as you like. >> So is there an abstraction layer almost that this can enable so that that management of all of these different applications and development platforms can be really done seamlessly? >> Yeah, so Project Dimension we announced a tech preview, and, well we'll be launching it later this year, and it will have a management layer that allows you to move your infrastructure and be able to actually, actually it's a VMware managed solution, so we will do it for you, it's even more simple; but be able to choose where you want to run that appliance as a service or infrastructure, whether it be the public cloud, the private cloud or the data center, and the edge. So that is the new what you call extraction, it's almost a new dimension, no pun intended. >> Hence the name. >> Hence the name, of, across all of your different clouds, or edge. >> So the notes I had on dimension, a hybrid cloud control plane, and the end-to-end VMware stack, on-prem cloud at the edge. And I think I heard Lenovo, VMware, and Dell are the initial sort of platform providers. >> That's right, Lenovo, Dell is the hardware. >> And that, what's the consumption model, is that an as a service consumption model? >> So we'll start with as a service, and what that means is VMware will actually manage your hardware, your infrastructure, and your software, we will do it for you. Obviously with the collaboration of when to do it and if everything, because this could be at the edge running mission critical applications. We want to make sure the OT, it's really an opportunity for OT and IT to collaborate and ensure that it's meeting the OT needs as well. >> So it's bringing a cloud-like consumption model to the edge, which of course is huge for VMware, I think probably 10% of your business today is SaaS-based, and the trend is clear; and the trend is your friend as they say, but, it's not easy to necessarily get there. So that's exciting I think that you're delivering as a service. >> I think we got really lucky. We ended up with this hybrid cloud strategy, it was the right thing to do, it's absolutely where the market's gone, and we're now almost at a multi-cloud strategy. And that puts us at the perfect position because we have set up our customers to be flexible and be able to choose whatever cloud or private they want in a cloud, we are very easily able to extend that to the edge, so it puts us in a very good position. >> Talking about the ecosystem again, I mean IoT it's every industry, every sector, every size of company, and I want you to discuss an ISV piece of this it's a very complex situation. >> I would love to talk with ISVs. >> But there's so many ISVs it makes your eyes bleed when you look at the list of ISVs, hard to figure out, okay who's real, who's not, and who to partner with; how are you guys sorting all that out? >> Okay. So, we are the infrastructure, what is beautiful about that is we are not competing with ISVs at all, so they all want to work with us. And the ISVs in the IoT world consist of not only specific application providers, but also IoT platform providers. So it's the SAPs of the world, it's Microsoft, it's also the Bosch, the GE, everybody that wants to do something with that data and build applications it. Most of those are doing industry-specific things, so what we're going to do is take Project Dimension and we're going to offer appliances as a service for industry-specific use cases, and sometimes they're horizontal like building management, but we're going to pick the best ones that we think have the right solution that can scale to the level our customers need in a secure way, and doing the most rich experience with our data. In fact we have 15 different partners in our zone right now really showing what they can do across six different industries, and that's what we're going to do with them. We're also, with Pulse, so I need to talk a little bit about Pulse because it's my baby, we announced Pulse IoT Center 2.0. And what that is, is it's the ability to manage, monitor, and secure things, or IoT gateways. So, one example of that is surveillance, we are partnering with camera companies that also offer analytic applications for visualization and surveillance, and we offering an end-to-end solution. In fact we announced the Dell Technologies surveillance solution partnering with companies like Access Communications owned by Cannon, Pulse runs on the camera to ensure that that camera is working properly, hasn't been hacked into, can get patched, can get isolated, God forbid something happens; and we're doing the same thing across many of the device and thing providers as well, which really falls into that. >> Let's talk about- Sorry Mimi, let's talk about an actual customer. Where do they start in this conversation? Because as you were saying in the beginning, the world is going IoT, there's this proliferation of devices, companies are moving in this direction because they have no choice. We were talking with a school district yesterday and the proliferation of BYOD, all of the things. So where does the conversation start with a customer about VMware edge? Does it start with the business level leadership who need to be able to get a handle on this, and identify new revenue streams, new business models? Does it start with the technology folks who have to have the infrastructure to support it? What is that sort of, I'm a customer, maybe a hospital or what not, where do I start? >> Great question. So, it starts, it depends is the answer, it can start either way, even if it starts on the infrastructure side. What we always tell IT is that you really need to have a reason to do this. You need to work with your business, you need to prioritize, you need to understand the mission critical objectives of your business, the outcome you're trying to achieve; and then let's work together on a use case, and we can help solve it with your business. So, whether we go through IT and we really educate them on the importance of this digital foundation at the edge, and then we work with one of their businesses, maybe in security and surveillance, or maybe it's with a bank, the ATM group; actually there is a group that runs the ATMs and we're working with that group. It might be the bank of the future retail bank, and they're all different organizations with many different use cases, we'll work with all of them. The nice thing about starting with IT is IT understands the challenge that they're faced with, and they really want to have the impact that they've had on the IT organization now on the OT, OT's very siloed. So, anyway, it starts there, but, with our partners, and the beauty about working with partners like ISVs, it will start on the OT side, and it will start with a use case; and then they'll go to the IT side and say "Hey, what about VMware to solve this?" And the IT will say are you serious? That's a dream. So, it absolutely is both, but it has to have a business outcome. >> Mimi, how about the data model? I mean, we know from talking to IT people they understand data, they've lived data their whole lives. A lot of the operations side of the business is analog today, and it's becoming digital. What's the conversation like around data? >> So, okay, so my whole background is data, I started business intelligence and then analytics, and then big data, now IoT. The purpose of the data, so first of all it depends on the use case, so the one thing we like to educate our anyone we're talking to is that you are going to need deep learning, and you're going to need real-time analytics. And each use case will be unique, and depending on the use case, you will need a slightly different architecture. So we'll help support this foundation based on the data, it's always about the data, or actually even more importantly the insights you're trying to get from the data. Once you know your use case, then you can determine where am I getting this data? Although sometimes you already know. And what's the right analytic process? Am I doing machine learning, am I doing AI, am I doing just predictive analytics, do I want to do something quickly at the edge to determine something in real-time and then send it back to make that process smarter, that's actually what I think will ultimately happen, it will be a decision making loop that goes from the edge to the cloud and back. But that's the data conversation we have, and I could talk all day, just in that topic. (laughs) >> And I mean I know we're tight on time but, how prominent is the discussion around data ownership? I mean, does the factory own the data? Does the device manufacturer own the data? I mean yes and yes? I don't know. >> I mean, there is controversy there, but typically, I know the device manufacturers want to own the data, and often times they have access to that data. Every industry's slightly different, but at the end of the day, the customer should own the data, I mean they should at least have access to that data. And we will always say in our situation the customer, the data is yours. And we will work with the both of those organizations 'cause those will be our constituents to a use case, and we will do what's right for that use case, and hopefully everybody wins. It really does depend. If it's car manufacturer, they have to own the data, because they have to make sure that car's safe and secure, but there might need to be level of access that the consumers get as well, so. >> Mimi, thanks so much for stopping by. I can tell by your energy and your genuine passion for this, we're going to hear a lot more, Dave, about what VMware edge is doing and helping customers embrace the superpowers that Pat Gelsinger was talking about on Monday. Great to have you on the show, Mimi. >> Thank you for having me, have a great day. >> Thank you, for Dave Vellante, I'm Lisa Martin, you're watch theCUBE, continuing coverage of VMworld 2018, this is our third day, stick around, we'll be right back with our next guest. (bubbly music)

Section 1 of 13 [00:00:00 - 00:10:04] (NOTE: speaker names may be different in each section) Speaker 1: Ladies and gentlemen our program will begin momentarily. Thank you. (singing) This presentation and the accompanying oral commentary may include forward looking statements that are subject to risks uncertainties and other factors beyond our control. Our actual results, performance or achievements may differ materially and adversely from those anticipated or implied by such statements because of various risk factors. Including those detailed in our annual report on form 10-K for the fiscal year ended July 31, 2017 filed with the SEC. Any future product or roadmap information presented is intended to outline general product direction and is not a commitment to deliver any functionality and should not be used when making any purchasing decision. (singing) Ladies and gentlemen please welcome Vice President Corporate Marketing Nutanix, Julie O'Brien. Julie O'Brien: All right. How about those Nutanix .NEXT dancers, were they amazing or what? Did you see how I blended right in, you didn't even notice I was there. [French 00:07:23] to .NEXT 2017 Europe. We're so glad that you could make it today. We have such a great agenda for you. First off do not miss tomorrow morning. We're going to share the outtakes video of the handclap video you just saw. Where are the customers, the partners, the Nutanix employee who starred in our handclap video? Please stand up take a bow. You are not going to want to miss tomorrow morning, let me tell you. That is going to be truly entertaining just like the next two days we have in store for you. A content rich highly interactive, number of sessions throughout our agenda. Wow! Look around, it is amazing to see how many cloud builders we have with us today. Side by side you're either more than 2,200 people who have traveled from all corners of the globe to be here. That's double the attendance from last year at our first .NEXT Conference in Europe. Now perhaps some of you are here to learn the basics of hyperconverged infrastructure. Others of you might be here to build your enterprise cloud strategy. And maybe some of you are here to just network with the best and brightest in the industry, in this beautiful French Riviera setting. Well wherever you are in your journey, you'll find customers just like you throughout all our sessions here with the next two days. From Sligro to Schroders to Societe Generale. You'll hear from cloud builders sharing their best practices and their lessons learned and how they're going all in with Nutanix, for all of their workloads and applications. Whether it's SAP or Splunk, Microsoft Exchange, unified communications, Cloud Foundry or Oracle. You'll also hear how customers just like you are saving millions of Euros by moving from legacy hypervisors to Nutanix AHV. And you'll have a chance to post some of your most challenging technical questions to the Nutanix experts that we have on hand. Our Nutanix technology champions, our MPXs, our MPSs. Where are all the people out there with an N in front of their certification and an X an R an S an E or a C at the end. Can you wave hello? You might be surprised to know that in Europe and the Middle East alone, we have more than 2,600 >> Julie: In Europe and the Middle East alone, we have more than 2,600 certified Nutanix experts. Those are customers, partners, and also employees. I'd also like to say thank you to our growing ecosystem of partners and sponsors who are here with us over the next two days. The companies that you meet here are the ones who are committed to driving innovation in the enterprise cloud. Over the next few days you can look forward to hearing from them and seeing some fantastic technology integration that you can take home to your data center come Monday morning. Together, with our partners, and you our customers, Nutanix has had such an exciting year since we were gathered this time last year. We were named a leader in the Gartner Magic Quadrant for integrated systems two years in a row. Just recently Gartner named us the revenue market share leader in their recent market analysis report on hyper-converged systems. We know enjoy more than 35% revenue share. Thanks to you, our customers, we received a net promoter score of more than 90 points. Not one, not two, not three, but four years in a row. A feat, I'm sure you'll agree, is not so easy to accomplish, so thank you for your trust and your partnership in us. We went public on NASDAQ last September. We've grown to more than 2,800 employees, more than 7,000 customers and 125 countries and in Europe and the Middle East alone, in our Q4 results, we added more than 250 customers just in [Amea 00:11:38] alone. That's about a third of all of our new customer additions. Today, we're at a pivotal point in our journey. We're just barely scratching the surface of something big and Goldman Sachs thinks so too. What you'll hear from us over the next two days is this: Nutanix is on it's way to building and becoming an iconic enterprise software company. By helping you transform your data center and your business with Enterprise Cloud Software that gives you the power of freedom of choice and flexibility in the hardware, the hypervisor and the cloud. The power of one click, one OS, any cloud. And now, to tell you more about the digital transformation that's possible in your business and your industry and share a little bit around the disruption that Nutanix has undergone and how we've continued to reinvent ourselves and maybe, if we're lucky, share a few hand clap dance moves, please welcome to stage Nutanix Founder, CEO and Chairman, Dheeraj Pandey. Ready? Alright, take it away [inaudible 00:13:06]. >> Dheeraj P: Thank you. Thank you, Julie and thank you every one. It looks like people are still trickling. Welcome to Acropolis. I just hope that we can move your applications to Acropolis faster than we've been able to move people into this room, actually. (laughs) But thank you, ladies and gentlemen. Thank you to our customers, to our partners, to our employees, to our sponsors, to our board members, to our performers, to everybody for their precious time. 'Cause that's the most precious thing you actually have, is time. I want to spend a little bit of time today, not a whole lot of time, but a little bit of time talking about the why of Nutanix. Like why do we exist? Why have we survived? Why will we continue to survive and thrive? And it's simpler than an NQ or category name, the word hyper-convergence, I think we are all complicated. Just thinking about what is it that we need to talk about today that really makes it relevant, that makes you take back something from this conference. That Nutanix is an obvious innovation, it's very obvious what we do is not very complicated. Because the more things change, the more they remain the same, so can we draw some parallels from life, from what's going on around us in our own personal lives that makes this whole thing very natural as opposed to "Oh, it's hyper-converged, it's a category, it's analysts and pundits and media." I actually think it's something new. It's not that different, so I want to start with some of that today. And if you look at our personal lives, everything that we had, has been digitized. If anything, a lot of these gadgets became apps, they got digitized into a phone itself, you know. What's Nutanix? What have we done in the last seven, eight years, is we digitized a lot of hardware. We made everything that used to be single purpose hardware look like pure software. We digitized storage, we digitized the systems manager role, an operations manager role. We are digitizing scriptures, people don't need to write scripts anymore when they automate because we can visually design automation with [com 00:15:36]. And we're also trying to make a case that the cloud itself is not just a physical destination. That it can be digitized and must be digitized as well. So we learn that from our personal lives too, but it goes on. Look at music. Used to be tons of things, if you used to go to [inaudible 00:15:55] Records, I'm sure there were European versions of [inaudible 00:15:57] Records as well, the physical things around us that then got digitized as well. And it goes on and on. We look at entertainment, it's very similar. The idea that if you go to a movie hall, the idea that you buy these tickets, the idea that we'd have these DVD players and DVDs, they all got digitized. Or as [inaudible 00:16:20] want to call it, virtualized, actually. That is basically happening in pretty much new things that we never thought would look this different. One of the most exciting things happening around us is the car industry. It's getting digitized faster than we know. And in many ways that we'd not even imagined 10 years ago. The driver will get digitized. Autonomous cars. The engine is definitely gone, it's a different kind of an engine. In fact, we'll re-skill a lot of automotive engineers who actually used to work in mechanical things to look at real chemical things like battery technologies and so on. A lot of those things that used to be physical are now in software in the car itself. Media itself got digitized. Think about a physical newspaper, or physical ads in newspapers. Now we talk about virtual ads, the digital ads, they're all over on websites and so on is our digital experience now. Education is no different, you know, we look back at the kind of things we used to do physically with physical things. Their now all digital. The experience has become that digital. And I can go on and on. You look at retail, you look at healthcare, look at a lot of these industries, they all are at the cusp of a digital disruption. And in fact, if you look at the data, everybody wants it. We all want a digital transformation for industries, for companies around us. In fact, the whole idea of a cloud is a highly digitized data center, basically. It's not just about digitizing servers and storage and networks and security, it's about virtualizing, digitizing the entire data center itself. That's what cloud is all about. So we all know that it's a very natural phenomenon, because it's happening around us and that's the obviousness of Nutanix, actually. Why is it actually a good thing? Because obviously it makes anything that we digitize and we work in the digital world, bring 10X more productivity and decision making efficiencies as well. And there are challenges, obviously there are challenges, but before I talk about the challenges of digitization, think about why are things moving this fast? Why are things becoming digitally disrupted quicker than we ever imagined? There are some reasons for it. One of the big reasons is obviously we all know about Moore's Law. The fact that a lot of hardware's been commoditized, and we have really miniaturized hardware. Nutanix today runs on a palm-sized server. Obviously it runs on the other end of the spectrum with high-end IBM power systems, but it also runs on palm-sized servers. Moore's Law has made a tremendous difference in the way we actually think about consuming software itself. Of course, the internet is also a big part of this. The fact that there's a bandwidth glut, there's Trans-Pacific cables and Trans-Atlantic cables and so on, has really connected us a lot faster than we ever imagined, actually, and a lot of this was also the telecom revolution of the '90s where we really produced a ton of glut for the internet itself. There's obviously a more subtle reason as well, because software development is democratizing. There's consumer-grade programming languages that we never imagined 10, 15, 20 years ago, that's making it so much faster to write- >> Speaker 1: 15-20 years ago that's making it so much faster to write code, with this crowdsourcing that never existed before with Githubs and things like that, open source. There's a lot more stuff that's happening that's outside the boundary of a corporation itself, which is making things so much faster in terms of going getting disrupted and writing things at 10x the speed it used to be 20 years ago. There is obviously this technology at the tip of our fingers, and we all want it in our mobile experience while we're driving, while we're in a coffee shop, and so on; and there's a tremendous focus on design on consumer-grade simplicity, that's making digital disruption that much more compressed in some of sense of this whole cycle of creative disruption that we talk about, is compressed because of mobility, because of design, because of API, the fact that machines are talking to machines, developers are talking to developers. We are going and miniaturizing the experience of organizations because we talk about micro-services and small two-pizza teams, and they all want to talk about each other using APIs and so on. Massive influence on this digital disruption itself. Of course, one of the reasons why this is also happening is because we want it faster, we want to consume it faster than ever before. And our attention spans are reducing. I like the fact that not many people are watching their cell phones right now, but you can imagine the multi-tasking mode that we are all in today in our lives, makes us want to consume things at a faster pace, which is one of the big drivers of digital disruption. But most importantly, and this is a very dear slide to me, a lot of this is happening because of infrastructure. And I can't overemphasize the importance of infrastructure. If you look at why did Google succeed, it was the ninth search engine, after eight of them before, and if you take a step back at why Facebook succeeded over MySpace and so on, a big reason was infrastructure. They believed in scale, they believed in low latency, they believed in being able to crunch information, at 10x, 100x, bigger scale than anyone else before. Even in our geopolitical lives, look at why is China succeeding? Because they've made infrastructure seamless. They've basically said look, governance is about making infrastructure seamless and invisible, and then let the businesses flourish. So for all you CIOs out there who actually believe in governance, you have to think about what's my first role? What's my primary responsibility? It's to provide such a seamless infrastructure, that lines of business can flourish with their applications, with their developers that can write code 10x faster than ever before. And a lot of these tenets of infrastructure, the fact of the matter is you need to have this always-on philosophy. The fact that it's breach-safe culture. Or the fact that operating systems are hardware agnostic. A lot of these tenets basically embody what Nutanix really stands for. And that's the core of what we really have achieved in the last eight years and want to achieve in the coming five to ten years as well. There's a nuance, and obviously we talk about digital, we talk about cloud, we talk about everything actually going to the cloud and so on. What are the things that could slow us down? What are the things that challenge us today? Which is the reason for Nutanix? Again, I go back to this very important point that the reason why we think enterprise cloud is a nuanced term, because the word "cloud" itself doesn't solve for a lot of the problems. The public cloud itself doesn't solve for a lot of the problems. One of the big ones, and obviously we face it here in Europe as well, is laws of the land. We have bureaucracy, which we need to deal with and respect; we have data sovereignty and computing sovereignty needs that we need to actually fulfill as well, while we think about going at breakneck speed in terms of disrupting our competitors and so on. So there's laws of the land, there's laws of physics. This is probably one of the big ones for what the architecture of cloud will look like itself, over the coming five to ten years. Our take is that cloud will need to be more dispersed than they have ever imagined, because computing has to be local to business operations. Computing has to be in hospitals and factories and shop floors and power plants and on and on and on... That's where you really can have operations and computing really co-exist together, cause speed is important there as well. Data locality is one of our favorite things; the fact that computing and data have to be local, at least the most relevant data has to be local as well. And the fact that electrons travel way faster when it's actually local, versus when you have to have them go over a Wide Area Network itself; it's one of the big reasons why we think that the cloud will actually be more nuanced than just some large data centers. You need to disperse them, you need to actually think about software (cloud is about software). Whether data plane itself could be dispersed and even miniaturized in small factories and shop floors and hospitals. But the control plane of the cloud is centralized. And that's the way you can have the best of both worlds; the control plane is centralized. You think as if you're managing one massive data center, but it's not because you're really managing hundreds or thousands of these sites. Especially if you think about edge-based computing and IoT where you really have your tentacles in tens of thousands of smaller devices and so on. We've talked about laws of the land, which is going to really make this digital transformation nuanced; laws of physics; and the third one, which is really laws of entropy. These are hackers that do this for adrenaline. These are parochial rogue states. These are parochial geo-politicians, you know, good thing I actually left the torture sign there, because apparently for our creative designer, geo-politics is equal to torture as well. So imagine one bad tweet can actually result in big changes to the way we actually live in this world today. And it's important. Geo-politics itself is digitized to a point where you don't need a ton of media people to go and talk about your principles and what you stand for and what you strategy for, for running a country itself is, and so on. And these are all human reasons, political reasons, bureaucratic reasons, compliance and regulations reasons, that, and of course, laws of physics is yet another one. So laws of physics, laws of the land, and laws of entropy really make us take a step back and say, "What does cloud really mean, then?" Cause obviously we want to digitize everything, and it all should appear like it's invisible, but then you have to nuance it for the Global 5000, the Global 10000. There's lots of companies out there that need to really think about GDPR and Brexit and a lot of the things that you all deal with on an everyday basis, actually. And that's what Nutanix is all about. Balancing what we think is all about technology and balancing that with things that are more real and practical. To deal with, grapple with these laws of the land and laws of physics and laws of entropy. And that's where we believe we need to go and balance the private and the public. That's the architecture, that's the why of Nutanix. To be able to really think about frictionless control. You want things to be frictionless, but you also realize that you are a responsible citizen of this continent, of your countries, and you need to actually do governance of things around you, which is computing governance, and data governance, and so on. So this idea of melding the public and the private is really about melding control and frictionless together. I know these are paradoxical things to talk about like how do you really have frictionless control, but that's the life you all lead, and as leaders we have to think about this series of paradoxes itself. And that's what Nutanix strategy, the roadmap, the definition of enterprise cloud is really thinking about frictionless control. And in fact, if anything, it's one of the things is also very interesting; think about what's disrupting Nutanix as a company? We will be getting disrupted along the way as well. It's this idea of true invisibility, the public cloud itself. I'd like to actually bring on board somebody who I have a ton of respect for, this leader of a massive company; which itself is undergoing disruption. Which is helping a lot of its customers undergo disruption as well, and which is thinking about how the life of a business analyst is getting digitized. And what about the laws of the land, the laws of physics, and laws of entropy, and so on. And we're learning a lot from this partner, massively giant company, called IBM. So without further ado, Bob Picciano. >> Bob Picciano: Thanks, >> Speaker 1: Thank you so much, Bob, for being here. I really appreciate your presence here- >> Bob Picciano: My pleasure! >> Speaker 1: And for those of you who actually don't know Bob, Bob is a Senior VP and General Manager at IBM, and is all things cognitive and obviously- >> Speaker 1: IBM is all things cognitive. Obviously, I learn a lot from a lot of leaders that have spent decades really looking at digital disruption. >> Bob: Did you just call me old? >> Speaker 1: No. (laughing) I want to talk about experience and talking about the meaning of history, because I love history, actually, you know, and I don't want to make you look old actually, you're too young right now. When you talk about digital disruption, we look at ourselves and say, "Look we are not extremely invisible, we are invisible, but we have not made something as invisible as the public clouds itself." And hence as I. But what's digital disruption mean for IBM itself? Now, obviously a lot of hardware is being digitized into software and cloud services. >> Bob: Yep. >> Speaker 1: What does it mean for IBM itself? >> Bob: Yeah, if you allow me to take a step back for a moment, I think there is some good foundational understanding that'll come from a particular point of view. And, you talked about it with the number of these dimensions that are affecting the way businesses need to consider their competitiveness. How they offer their capabilities into the market place. And as you reflected upon IBM, you know, we've had decades of involvement in information technology. And there's a big disruption going on in the information technology space. But it's what I call an accretive disruption. It's a disruption that can add value. If you were to take a step back and look at that digital trajectory at IBM you'd see our involvement with information technology in a space where it was all oriented around adding value and capability to how organizations managed inscale processes. Thinking about the way they were going to represent their businesses in a digital form. We came to call them applications. But it was how do you open an account, how do you process a claim, how do you transfer money, how do you hire an employee? All the policies of a company, the way the people used to do it mechanically, became digital representations. And that foundation of the digital business process is something that IBM helped define. We invented the role of the CIO to help really sponsor and enter in this notion that businesses could re represent themselves in a digital way and that allowed them to scale predictably with the qualities of their brand, from local operations, to regional operations, to international operations, and show up the same way. And, that added a lot of value to business for many decades. And we thrived. Many companies, SAP all thrived during that span. But now we're in a new space where the value of information technology is hitting a new inflection point. Which is not about how you scale process, but how you scale insight, and how you scale wisdom, and how you scale knowledge and learning from those operational systems and the data that's in those operational systems. >> Speaker 1: How's it different from 1993? We're talking about disruption. There was a time when IBM reinvented itself, 20-25 years ago. >> Bob: Right. >> Speaker 1: And you said it's bigger than 25 years ago. Tell us more. >> Bob: You know, it gets down. Everything we know about that process space right down to the very foundation, the very architecture of the CPU itself and the computer architecture, the von Neumann architecture, was all optimized on those relatively static scaled business processes. When you move into the notion where you're going to scale insight, scale knowledge, you enter the era that we call the cognitive era, or the era of intelligence. The algorithms are very different. You know the data semantically doesn't integrate well across those traditional process based pools and reformation. So, new capabilities like deep learning, machine learning, the whole field of artificial intelligence, allows us to reach into that data. Much of it unstructured, much of it dark, because it hasn't been indexed and brought into the space where it is directly affecting decision making processes in a business. And you have to be able to apply that capability to those business processes. You have to rethink the computer, the circuitry itself. You have to think about how the infrastructure is designed and organized, the network that is required to do that, the experience of the applications as you talked about have to be very natural, very engaging. So IBM does all of those things. So as a function of our transformation that we're on now, is that we've had to reach back, all the way back from rethinking the CPU, and what we dedicate our time and attention to. To our services organization, which is over 130,000 people on the consulting side helping organizations add digital intelligence to this notion of a digital business. Because, the two things are really a confluence of what will make this vision successful. >> Speaker 1: It looks like massive amounts of change for half a million people who work with the company. >> Bob: That's right. >> Speaker 1: I'm sure there are a lot of large customers out here, who will also read into this and say, "If IBM feels disrupted ... >> Bob: Uh hm >> Speaker 1: How can we actually stay not vulnerable? Actually there is massive amounts of change around their own competitive landscape as well. >> Bob: Look, I think every company should feel vulnerable right. If you're at this age, this cognitive era, the age of digital intelligence, and you're not making a move into being able to exploit the capabilities of cognition into the business process. You are vulnerable. If you're at that intersection, and your competitor is passing through it, and you're not taking action to be able to deploy cognitive infrastructure in conjunction with the business processes. You're going to have a hard time keeping up, because it's about using the machines to do the training to augment the intelligence of our employees of our professionals. Whether that's a lawyer, or a doctor, an educator or whether that's somebody in a business function, who's trying to make a critical business decision about risk or about opportunity. >> Speaker 1: Interesting, very interesting. You used the word cognitive infrastructure. >> Bob: Uh hm >> Speaker 1: There's obviously computer infrastructure, data infrastructure, storage infrastructure, network infrastructure, security infrastructure, and the core of cognition has to be infrastructure as well. >> Bob: Right >> Speaker 1: Which is one of the two things that the two companies are working together on. Tell us more about the collaboration that we are actually doing. >> Bob: We are so excited about our opportunity to add value in this space, so we do think very differently about the cognitive infrastructure that's required for this next generation of computing. You know I mentioned the original CPU was built for very deterministic, very finite operations; large precision floating point capabilities to be able to accurately calculate the exact balance, the exact amount of transfer. When you're working in the field of AI in cognition. You actually want variable precision. Right. The data is very sparse, as opposed to the way that deterministic or scorecastic operations work, which is very dense or very structured. So the algorithms are redefining the processes that the circuitry actually has to run. About five years ago, we dedicated a huge effort to rethink everything about the chip and what we made to facilitate an orchestra of participation to solve that problem. We all know the GPU has a great benefit for deep learning. But the GPU in many cases, in many architectures, specifically intel architectures, it's dramatically confined by a very small amount of IO bandwidth that intel allows to go on and off the chip. At IBM, we looked at all 686 roughly square millimeters of our chip and said how do we reuse that square area to open up that IO bandwidth? So the innovation of a GPU or a FPGA could really be utilized to it's maximum extent. And we could be an orchestrator of all of the diverse compute that's going to be necessary for AI to really compel these new capabilities. >> Speaker 1: It's interesting that you mentioned the fact that you know power chips have been redefined for the cognitive era. >> Bob: Right, for Lennox for the cognitive era. >> Speaker 1: Exactly, and now the question is how do you make it simple to use as well? How do you bring simplicity which is where ... >> Bob: That's why we're so thrilled with our partnership. Because you talked about the why of Nutanix. And it really is about that empowerment. Doing what's natural. You talked about the benefits of calm and being able to really create that liberation of an information technology professional, whether it's in operations or in development. Having the freedom of action to make good decisions about defining the infrastructure and deploying that infrastructure and not having to second guess the physical limitations of what they're going to have to be dealing with. >> Speaker 1: That's why I feel really excited about the fact that you have the power of software, to really meld the two forms together. The intel form and the power form comes together. And we have some interesting use cases that our CIO Randy Phiffer is also really exploring, is how can a power form serve as a storage form for our intel form. >> Bob: Sure. >> Speaker 1: It can serve files and mocks and things like that. >> Bob: Any data intensive application where we have seen massive growth in our Lennox business, now for our business, Lennox is 20% of the revenue of our power systems. You know, we started enabling native Lennox distributions on top of little Indian ones, on top of the power capabilities just a few years ago, and it's rocketed. And the reason for that if for any data intensive application like a data base, a no sequel database or a structured data base, a dupe in the unstructured space, they typically run about three to four times better price performance on top of Lennox on power, than they will on top of an intel alternative. >> Speaker 1: Fascinating. >> Bob: So all of these applications that we're talking about either create or consume a lot of data, have to manage a lot of flexibility in that space, and power is a tremendous architecture for that. And you mentioned also the cohabitation, if you will, between intel and power. What we want is that optionality, for you to utilize those benefits of the 3X better price performance where they apply and utilize the commodity base where it applies. So you get the cost benefits in that space and the depth and capability in the space for power. >> Speaker 1: Your tongue in cheek remark about commodity intel is not lost on people actually. But tell us about... >> Speaker 1: Intel is not lost on people actually. Tell us about ... Obviously we digitized Linux 10, 15 years ago with [inaudible 00:40:07]. Have you tried to talk about digitizing AIX? That is the core of IBM's business for the last 20, 25, 30 years. >> Bob: Again, it's about this ability to compliment and extend the investments that businesses have made during their previous generations of decision making. This industry loves to talk about shifts. We talked about this earlier. That was old, this is new. That was hard, this is easy. It's not about shift, it's about using the inflection point, the new capability to extend what you already have to make it better. And that's one thing that I must compliment you, and the entire Nutanix organization. It's really empowering those applications as a catalog to be deployed, managed, and integrated in a new way, and to have seamless interoperability into the cloud. We see the AIX workload just having that same benefit for those businesses. And there are many, many 10's of thousands around the world that are critically dependent on every element of their daily operations and productivity of that operating platform. But to introduce that into that network effect as well. >> Speaker 1: Yeah. I think we're looking forward to how we bring the same cloud experience on AIX as well because as a company it keeps us honest when we don't scoff at legacy. We look at these applications the last 10, 15, 20 years and say, "Can we bring them into the new world as well?" >> Bob: Right. >> Speaker 1: That's what design is all about. >> Bob: Right. >> Speaker 1: That's what Apple did with musics. We'll take an old world thing and make it really new world. >> Bob: Right. >> Speaker 1: The way we consume things. >> Bob: That governance. The capability to help protect against the bad actors, the nefarious entropy players, as you will. That's what it's all about. That's really what it takes to do this for the enterprise. It's okay, and possibly easier to do it in smaller islands of containment, but when you think about bringing these class of capabilities into an enterprise, and really helping an organization drive both the flexibility and empowerment benefits of that, but really be able to depend upon it for international operations. You need that level of support. You need that level of capability. >> Speaker 1: Awesome. Thank you so much Bob. Really appreciate you coming. [crosstalk 00:42:14] Look forward to your [crosstalk 00:42:14]. >> Bob: Cheers. Thank you. >> Speaker 1: Thanks again for all of you. I know that people are sitting all the way up there as well, which is remarkable. I hope you can actually see some of the things that Sunil and the team will actually bring about, talk about live demos. We do real stuff here, which is truly live. I think one of the requests that I have is help us help you navigate the digital disruption that's upon you and your competitive landscape that's around you that's really creating that disruption. Thank you again for being here, and welcome again to Acropolis. >> Speaker 3: Ladies and gentlemen, please welcome Chief Product and Development Officer, Nutanix Sunil Potti. >> Sunil Potti: Okay, so I'm going to just jump right in because I know a bunch of you guys are here to see the product as well. We are a lot of demos lined up for you guys, and we'll try to mix in the slides, and the demos as well. Here's just an example of the things I always bring up in these conferences to look around, and say in the last few months, are we making progress in simplifying infrastructure? You guys have heard this again and again, this has been our mantra from the beginning, that the hotter things get, the more differentiated a company like Nutanix can be if we can make things simple, or keep things simple. Even though I like this a lot, we found something a little bit more interesting, I thought, by our European marketing team. If you guys need these tea bags, which you will need pretty soon. It's a new tagline for the company, not really. I thought it was apropos. But before I get into the product and the demos, to give you an idea. Every time I go to an event you find ways to memorialize the event. You meet people, you build relationships, you see something new. Last night, nothing to do with the product, I sat beside someone. It was a customer event. I had no idea who I was sitting beside. He was a speaker. How many of you guys know him, by the way? Sir Ranulph Fiennes. Few hands. Good for you. I had no idea who I was sitting beside. I said, "Oh, somebody called Sir. I should be respectful." It's kind of hard for me to be respectful, but I tried. He says, "No, I didn't do anything in the sense. My grandfather was knighted about 100 years ago because he was the governor of Antigua. And when he dies, his son becomes." And apparently Sir Ranulph's dad also died in the war, and so that's how he is a sir. But then I started looking it up because he's obviously getting ready to present. And the background for him is, in my opinion, even though the term goes he's the World's Greatest Living Explorer. I would have actually called it the World's Number One Stag, and I'll tell you why. Really, you should go look it up. So this guy, at the age of 21, gets admitted to Special Forces. If you're from the UK, this is as good as it gets, SAS. Six, seven years into it, he rebels, helps out his local partner because he doesn't like a movie who's building a dam inside this pretty village. And he goes and blows up a dam, and he's thrown out of that Special Forces. Obviously he's in demolitions. Goes all the way. This is the '60's, by the way. Remember he's 74 right now. The '60's he goes to Oman, all by himself, as the only guy, only white guy there. And then around the '70's, he starts truly exploring, truly exploring. And this is where he becomes really, really famous. You have to go see this in real life, when he sees these videos to really appreciate the impact of this guy. All by himself, he's gone across the world. He's actually gone across Antarctica. Now he tells me that Antarctica is the size of China and India put together, and he was prepared for -50 to 60 degrees, and obviously he got -130 degrees. Again, you have to see the videos, see his frostbite. Two of his fingers are cut off, by the way. He hacksawed them himself. True story. And then as he, obviously, aged, his body couldn't keep up with him, but his will kept up with him. So after a recent heart attack, he actually ran seven marathons. But most importantly, he was telling me this story, at 65 he wanted to do something different because his body was letting him down. He said, "Let me do something easy." So he climbed Mount Everest. My point being, what is this related to Nutanix? Is that if Nutanix is a company, without technology, allows to spend more time on life, then we've accomplished a piece of our vision. So keep that in mind. Keep that in mind. Now comes the boring part, which is the product. The why, what, how of Nutanix. Neeris talked about this. We have two acts in this company. Invisible Infrastructure was what we started off. You heard us talk about it. How did we do it? Using one-click technologies by converging infrastructure, computer storage, virtualization, et cetera, et cetera. What we are now about is about changing the game. Saying that just like we'd applicated what powers Google and Amazon inside the data center, could we now make them all invisible? Whether it be inside or outside, could we now make clouds invisible? Clouds could be made invisible by a new level of convergence, not about computer storage, but converging public and private, converging CAPEX and OPEX, converging consumption models. And there, beyond our core products, Acropolis and Prism, are these new products. As you know, we have this core thesis, right? The core thesis says what? Predictable workloads will stay inside the data center, elastic workloads will go outside, as long as the experience on both sides is the same. So if you can genuinely have a cloud-like experience delivered inside a data center, then that's the right a- >> Speaker 1: Genuinely have a cloud like experience developed inside the data center. And that's the right answer of predictable workloads. Absolutely the answer of elastic workloads, doesn't matter whether security or compliance. Eventually a public cloud will have a data center right beside your region, whether through local partner or a top three cloud partner. And you should use it as your public cloud of choice. And so, our goal is to ensure that those two worlds are converged. And that's what Calm does, and we'll talk about that. But at the same time, what we found in late 2015, we had a bunch of customers come to us and said "Look, I love this, I love the fact that you're going to converge public and private and all that good stuff. But I have these environments and these apps that I want to be delivered as a service but I want the same operational tooling. I don't want to have two different environments but I don't want to manage my data centers. Especially my secondary data centers, DR data centers." And that's why we created Xi, right? And you'll hear a lot more about this, obviously it's going to start off in the U.S but very rapidly launch in Europe, APJ globally in the next 9-12 months. And so we'll spend some quality time on those products as well today. So, from the journey that we're at, we're starting with the score cloud that essentially says "Look, your public and private needs to be the same" We call that the first instantiation of your cloud architectures and we're essentially as a company, want to build this enterprise cloud operating system as a fabric across public and private. But that's just the starting point. The starting point evolves to the score architecture that we believe that the cloud is being dispersed. Just like you have a public and a private cloud in the core data centers and so forth, you'll need a similar experience inside your remote office branch office, inside your DR data centers, inside your branches, and it won't stop there. It'll go all the way to the edge. All we're already seeing this right? Not just in the army where your forward operating bases in Afghanistan having a three note cluster sitting inside a tent. But we're seeing this in a variety of enterprise scenarios. And here's an example. So, here's a customer, global oil and gas company, has couple of primary data centers running Nutanix, uses GCP as a core public cloud platform, has a whole bunch of remote offices, but it also has this interesting new edge locations in the form of these small, medium, large size rigs. And today, they're in the process of building a next generation cloud architecture that's completely dispersed. They're using one node, coming out on version 5.5 with Nutanix. They're going to use two nodes, they're going to throw us three nods, multicultural architectures. Day one, they're going to centrally manage it using Prism, with one click upgrades, right? And then on top of that, they're also now provisioning using Calm, purpose built apps for the various locations. So, for example, there will be a re control app at the edge, there's an exploration data lag in Google and so forth. My point being that increasingly this architecture that we're talking about is happening in real time. It's no longer just an existing cellular civilization data center that's being replatformed to look like a private cloud and so forth, or a hybrid cloud. But the fact that you're going into this multi cloud era is getting excel bated, the more someone consumes AWL's GCP or any public cloud, the more they're excel bating their internal transformation to this multi cloud architecture. And so that's what we're going to talk about today, is this construct of ONE OS and ONE Click, and when you think about it, every company has a standard stack. So, this is the only slide you're going to see from me today that's a stack, okay? And if you look at the new release coming out, version 5.5, it's coming out imminently, easiest way to say it is that it's got a ton of functionality. We've jammed as much as we can onto one slide and then build a product basically, okay? But I would encourage you guys to check out the release, it's coming out shortly. And we can go into each and every feature here, we'd be spending a lot of time but the way that we look at building Nutanix products as many of you know, it is not feature at a time. It's experience at a time. And so, when you really look at Nutanix using a lateral view, and that's how we approach problems with our customers and partners. We think about it as a life cycle, all the way from learning to using, operating, and then getting support and experiences. And today, we're going to go through each of these stages with you. And who better to talk about it than our local version of an architect, Steven Poitras please come up on stage. I don't know where you are, Steven come on up. You tucked your shirt in? >> Speaker 2: Just for you guys today. >> Speaker 1: Okay. Alright. He's sort of putting on his weight. I know you used a couple of tight buckles there. But, okay so Steven so I know we're looking for the demo here. So, what we're going to do is, the first step most of you guys know this, is we've been quite successful with CE, it's been a great product. How many of you guys like CE? Come on. Alright. I know you had a hard time downloading it yesterday apparently, there's a bunch of guys had a hard time downloading it. But it's been a great way for us not just to get you guys to experience it, there's more than 25,000 downloads and so forth. But it's also a great way for us to see new features like IEME and so forth. So, keep an eye on CE because we're going to if anything, explode the way that we actually use as a way to get new features out in the next 12 months. Now, one thing beyond CE that we did, and this was something that we did about ... It took us about 12 months to get it out. While people were using CE to learn a lot, a lot of customers were actually getting into full blown competitive evals, right? Especially with hit CI being so popular and so forth. So, we came up with our own version called X-Ray. >> Speaker 2: Yup. >> Speaker 1: What does X-Ray do before we show it? >> Speaker 2: Yeah. Absolutely. So, if we think about back in the day we were really the only ACI platform out there on the market. Now there are a few others. So, to basically enable the customer to objectively test these, we came out with X-Ray. And rather than talking about the slide let's go ahead and take a look. Okay, I think it's ready. Perfect. So, here's our X-Ray user interface. And essentially what you do is you specify your targets. So, in this case we have a Nutanix 80150 as well as some of our competitors products which we've actually tested. Now we can see on the left hand side here we see a series of tests. So, what we do is we go through and specify certain workloads like OLTP workloads, database colocation, and while we do that we actually inject certain test cases or scenarios. So, this can be snapshot or component failures. Now one of the key things is having the ability to test these against each other. So, what we see here is we're actually taking a OLTP workload where we're running two virtual machines, and then we can see the IOPS OLTP VM's are actually performing here on the left hand side. Now as we're actually go through this test we perform a series of snapshots, which are identified by these red lines here. Now as you can see, the Nutanix platform, which is shown by this blue line, is purely consistent as we go through this test. However, our competitor's product actually degrades performance overtime as these snapshots are taken. >> Speaker 1: Gotcha. And some of these tests by the way are just not about failure or benchmarking, right? It's a variety of tests that we have that makes real life production workloads. So, every couple of months we actually look at our production workloads out there, subset those two cases and put it into X-Ray. So, X-Ray's one of those that has been more recently announced into the public. But it's already gotten a lot of update. I would strongly encourage you, even if you an existing Nutanix customer. It's a great way to keep us honest, it's a great way for you to actually expand your usage of Nutanix by putting a lot of these real life tests into production, and as and when you look at new alternatives as well, there'll be certain situations that we don't do as well and that's a great way to give us feedback on it. And so, X-Ray is there, the other one, which is more recent by the way is a fact that most of you has spent many days if not weeks, after you've chosen Nutanix, moving non-Nutanix workloads. I.e. VMware, on three tier architectures to Atrio Nutanix. And to do that, we took a hard look and came out with a new product called Xtract. >> Speaker 2: Yeah. So essentially if we think about what Nutanix has done for the data center really enables that iPhone like experience, really bringing it simplicity and intuitiveness to the data center. Now what we wanted to do is to provide that same experience for migrating existing workloads to us. So, with Xtract essentially what we've done is we've scanned your existing environment, we've created design spec, we handled the migration process ... >> Steven: ... environment, we create a design spec. We handle for the migration process as well as the cut over. Now, let's go ahead and take a look in our extract user interface here. What we can see is we have a source environment. In this case, this is a VC environment. This can be any VC, whether it's traditional three tier or hypherconverged. We also see our Nutanix target environments. Essentially, these are our AHV target clusters where we're going to be migrating the data and performing the cut over to you. >> Speaker 2: Gotcha. Steven: The first thing that we do here is we go ahead and create a new migration plan. Here, I'm just going to specify this as DB Wave 2. I'll click okay. What I'm doing here is I'm selecting my target Nutanix cluster, as well as my target Nutanix container. Once I'll do that, I'll click next. Now in this case, we actually like to do it big. We're actually going to migrate some production virtual machines over to this target environment. Here, I'm going to select a few windows instances, which are in our database cluster. I'll click next. At this point, essentially what's occurring is it's going through taking a look at these virtual machines as well as taking a look at the target environment. It takes a look at the resources to ensure that we actually have enough, an ample capacity to facilitate the workload. The next thing we'll do is we'll go ahead and type in our credentials here. This is actually going to be used for logging into the virtual machine. We can do a new device driver installation, as well as get any static IP configuration. Well specify our network mapping. Then from there, we'll click next. What we'll do is we'll actually save and start. This will go through create the migration plan. It'll do some analysis on these virtual machines to ensure that we can actually log in before we actually start migrating data. Here we have a migration, which has been in progress. We can see we have a few virtual machines, obviously some Linux, some Windows here. We've cut over a few. What we do to actually cut over these VMS, is go ahead select the VMS- Speaker 2: This is the actual task of actually doing the final stage of cut over. Steven: Yeah, exactly. That's one of the nice things. Essentially, we can migrate the data whenever we want. We actually hook into the VADP API's to do this. Then every 10 minutes, we send over a delta to sync the data. Speaker 2: Gotcha, gotcha. That's how one click migration can now be possible. This is something that if you guys haven't used this, this has been out in the wild, just for a month or so. Its been probably one of our bestselling, because it's free, bestselling features of the recent product release. I've had customers come to me and say, "Look, there are situations where its taken us weeks to move data." That is now minutes from the operator perspective. Forget where the director, or the VP, it's the line architecture and operator that really loves these tools, which is essentially the core of Nutanix. That's one of our core things, is to make sure that if we can keep the engineer and the architect truly happy, then everything else will be fine for us, right? That's extract. Then we have a lot of things, right? We've done the usual things, there's a tunnel functionality on day zero, day one, day two, kind of capabilities. Why don't we start with something around Prism Central, now that we can do one click PC installs? We can do PC scale outs, we can go from managing thousands of VMS, tens of thousands of VMS, while doing all the one click operations, right? Steven: Yep. Speaker 2: Why don't we take a quick look at what's new in Prism Central? Steven: Yep. Absolutely. Here, we can see our Prism element interface. As you mentioned, one of the key things we added here was the ability to deploy Prism Central very simply just with a few clicks. We'll actually go through a distributed PC scale of deployment here. Here, we're actually going to deploy, as this is a new instance. We're going to select our 5.5 version. In this case, we're going to deploy a scale out Prism Central cluster. Obviously, availability and up-time's very critical for us, as we're mainly distributed systems. In this case we're going to deploy a scale-out PC cluster. Here we'll select our number of PC virtual machines. Based upon the number of VMS, we can actually select our size of VM that we'd deploy. If we want to deploy 25K's report, we can do that as well. Speaker 2: Basically a thousand to tens of thousands of VM's are possible now. Steven: Yep. That's a nice thing is you can start small, and then scale out as necessary. We'll select our PC network. Go ahead and input our IP address. Now, we'll go to deploy. Now, here we can see it's actually kicked off the deployment, so it'll go provision these virtual machines to apply the configuration. In a few minutes, we'll be up and running. Speaker 2: Right. While Steven's doing that, one of the things that we've obviously invested in is a ton of making VM operations invisible. Now with Calm's, what we've done is to up level that abstraction. Two applications. At the end of the day, more and more ... when you go to AWS, when you go to GCP, you go to [inaudible 01:04:56], right? The level of abstractions now at an app level, it's cloud formations, and so forth. Essentially, what Calm's able to do is to give you this marketplace that you can go in and self-service [inaudible 01:05:05], create this internal cloud like environment for your end users, whether it be business owners, technology users to self-serve themselves. The process is pretty straightforward. You, as an operator, or an architect, or [inaudible 01:05:16] create these blueprints. Consumers within the enterprise, whether they be self-service users, whether they'll be end business users, are able to consume them for a simple marketplace, and deploy them on whether it be a private cloud using Nutanix, or public clouds using anything with public choices. Then, as a single frame of glass, as operators you're doing conversed operations, at an application centric level between [inaudible 01:05:41] across any of these clouds. It's this combination of producer, consumer, operator in a curated sense. Much like an iPhone with an app store. It's the core construct that we're trying to get with Calm to up level the abstraction interface across multiple clouds. Maybe we'll do a quick demo of this, and then get into the rest of the stuff, right? Steven: Sure. Let's check it out. Here we have our Prism Central user interface. We can see we have two Nutanix clusters, our cloudy04 as well as our Power8 cluster. One of the key things here that we've added is this apps tab. I'm clicking on this apps tab, we can see that we have a few [inaudible 01:06:19] solutions, we have a TensorFlow solution, a [inaudible 01:06:22] et cetera. The nice thing about this is, this is essentially a marketplace where vendors as well as developers could produce these blueprints for consumption by the public. Now, let's actually go ahead and deploy one of these blueprints. Here we have a HR employment engagement app. We can see we have three different tiers of services part of this. Speaker 2: You need a lot of engagement at HR, you know that. Okay, keep going. Steven: Then the next thing we'll do here is we'll go and click on. Based upon this, we'll specify our blueprint name, HR app. The nice thing when I'm deploying is I can actually put in back doors. We'll click clone. Now what we can see here is our blueprint editor. As a developer, I could actually go make modifications, or even as an in-user given the simple intuitive user interface. Speaker 2: This is the consumers side right here, but it's also the [inaudible 01:07:11]. Steven: Yep, absolutely. Yeah, if I wanted to make any modifications, I could select the tier, I could scale out the number of instances, I could modify the packages. Then to actually deploy, all I do is click launch, specify HR app, and click create. Speaker 2: Awesome. Again, this is coming in 5.5. There's one other feature, by the way, that is coming in 5.5 that's surrounding Calm, and Prism Pro, and everything else. That seems to be a much awaited feature for us. What was that? Steven: Yeah. Obviously when we think about multi-tenant, multi-cloud role based access control is a very critical piece of that. Obviously within the organization, we're going to have multiple business groups, multiple units. Our back's a very critical piece. Now, if we go over here to our projects, we can see in this scenario we just have a single project. What we've added is if you want to specify certain roles, in this case we're going to add our good friend John Doe. We can add them, it could be a user or group, but then we specify their role. We can give a developer the ability to edit and create these blueprints, or consumer the ability to actually provision based upon. Speaker 2: Gotcha. Basically in 5.5, you'll have role based access control now in Prism and Calm burned into that, that I believe it'll support custom role shortly after. Steven: Yep, okay. Speaker 2: Good stuff, good stuff. I think this is where the Nutanix guys are supposed to clap, by the way, so that the rest of the guys can clap. Steven: Thank you, thank you. Okay. What do we have? Speaker 2: We have day one stuff, obviously there's a ton of stuff that's coming in core data path capabilities that most of you guys use. One of the most popular things is synchronous replication, especially in Europe. Everybody wants to do [Metro 01:08:49] for whatever reason. But we've got something new, something even more enhanced than Metro, right? Steven: Yep. Speaker 2: Do you want to talk a little bit about it? Steven: Yeah, let's talk about it. If we think about what we had previously, we started out with a synchronous replication. This is essentially going to be your higher RPO. Then we moved into Metro cluster, which was RPO zero. Those are two ins of the gamete. What we did is we introduced new synchronous replication, which really gives you the best of both worlds where you have very, very decreased RPO's, but zero impact in line mainstream performance. Speaker 2: That's it. Let's show something. Steven: Yeah, yeah. Let's do it. Here, we're back at our Prism Element interface. We'll go over here. At this point, we provisioned our HR app, the next thing we need to do is to protect that data. Let's go here to protection domain. We'll create a new PD for our HR app. Speaker 2: You clearly love HR. Steven: Spent a lot of time there. Speaker 2: Yeah, yeah, yeah. Steven: Here, you can see we have our production lamp DBVM. We'll go ahead and protect that entity. We can see that's protected. The next thing we'll do is create a schedule. Now, what would you say would be a good schedule we should actually shoot for? Speaker 2: I don't know, 15 minutes? Steven: 15 minutes is not bad. But I ... Section 7 of 13 [01:00:00 - 01:10:04] Section 8 of 13 [01:10:00 - 01:20:04] (NOTE: speaker names may be different in each section) Speaker 1: ... 15 minutes. Speaker 2: 15 minutes is not bad, but I think the people here deserve much better than that, so I say let's shoot for ... what about 15 seconds? Speaker 1: Yeah. They definitely need a bathroom break, so let's do 15 seconds. Speaker 2: Alright, let's do 15 seconds. Speaker 1: Okay, sounds good. Speaker 2: K. Then we'll select our retention policy and remote cluster replicate to you, which in this case is wedge. And we'll go ahead and create the schedule here. Now at this point we can see our protection domain. Let's go ahead and look at our entities. We can see our database virtual machine. We can see our 15 second schedule, our local snapshots, as well as we'll start seeing our remote snapshots. Now essentially what occurs is we take two very quick snapshots to essentially see the initial data, and then based upon that then we'll start taking our continuous 15 second snaps. Speaker 1: 15 seconds snaps, and obviously near sync has less of impact than synchronous, right? From an architectural perspective. Speaker 2: Yeah, and that's a nice thing is essentially within the cluster it's truly pure synchronous, but externally it's just a lagged a-sync. Speaker 1: Gotcha. So there you see some 15 second snapshots. So near sync is also built into five-five, it's a long-awaited feature. So then, when we expand in the rest of capabilities, I would say, operations. There's a lot of you guys obviously, have started using Prism Pro. Okay, okay, you can clap. You can clap. It's okay. It was a lot of work, by the way, by the core data pad team, it was a lot of time. So Prism Pro ... I don't know if you guys know this, Prism Central now run from zero percent to more than 50 percent attach on install base, within 18 months. And normally that's a sign of true usage, and true value being supported. And so, many things are new in five-five out on Prism Pro starting with the fact that you can do data[inaudible 01:11:49] base lining, alerting, so that you're not capturing a ton of false positives and tons of alerts. We go beyond that, because we have this core machine-learning technology power, we call it cross fit. And, what we've done is we've used that as a foundation now for pretty much all kinds of operations benefits such as auto RCA, where you're able to actually map to particular [inaudible 01:12:12] crosses back to who's actually causing it whether it's the network, a computer, and so forth. But then the last thing that we've also done in five-five now that's quite different shading, is the fact that you can now have a lot of these one-click recommendations and remediations, such as right-sizing, the fact that you can actually move around [inaudible 01:12:28] VMs, constrained VMs, and so forth. So, I now we've packed a lot of functionality in Prism Pro, so why don't we spend a couple of minutes quickly giving a sneak peak into a few of those things. Speaker 2: Yep, definitely. So here we're back at our Prism Central interface and one of the things we've added here, if we take a look at one of our clusters, we can see we have this new anomalies portion here. So, let's go ahead and select that and hop into this. Now let's click on one of these anomaly events. Now, essentially what the system does is we monitor all the entities and everything running within the system, and then based upon that, we can actually determine what we expect the band of values for these metrics to be. So in this scenario, we can see we have a CPU usage anomaly event. So, normal time, we expect this to be right around 86 to 100 percent utilization, but at this point we can see this is drastically dropped from 99 percent to near zero. So, this might be a point as an administrator that I want to go check out this virtual machine, ensure that certain services and applications are still up and running. Speaker 1: Gotcha, and then also it changes the baseline based on- Speaker 2: Yep. Yeah, so essentially we apply machine-learning techniques to this, so the system will dynamically adjust based upon the value adjustment. Speaker 1: Gotcha. What else? Speaker 2: Yep. So the other thing here that we mentioned was capacity planning. So if we go over here, we can take a look at our runway. So in this scenario we have about 30 days worth of runway, which is most constrained by memory. Now, obviously, more nodes is all good for everyone, but we also want to ensure that you get the maximum value on your investment. So here we can actually see a few recommendations. We have 11 overprovision virtual machines. These are essentially VMs which have more resources than are necessary. As well as 19 inactives, so these are dead VMs essentially that haven't been powered on and not utilized. We can also see we have six constrained, as well as one bully. So, constrained VMs are essentially VMs which are requesting more resources than they actually have access to. This could be running at 100 percent CPU utilization, or 100 percent memory, or storage utilization. So we could actually go in and modify these. Speaker 1: Gotcha. So these are all part of the auto remediation capabilities that are now possible? Speaker 2: Yeah. Speaker 1: What else, do you want to take reporting? Speaker 2: Yeah. Yeah, so I know reporting is a very big thing, so if we think about it, we can't rely on an administrator to constantly go into Prism. We need to provide some mechanism to allow them to get emailed reports. So what we've done is we actually autogenerate reports which can be sent via email. So we'll go ahead and add one of these sample reports which was created today. And here we can actually get specific detailed information about our cluster without actually having to go into Prism to get this. Speaker 1: And you can customize these reports and all? Speaker 2: Yep. Yeah, if we hop over here and click on our new report, we can actually see a list of views we could add to these reports, and we can mix and match and customize as needed. Speaker 1: Yeah, so that's the operational side. Now we also have new services like AFS which has been quite popular with many of you folks. We've had hundreds of customers already on it live with SMB functionality. You want to show a couple of things that is new in five-five? Speaker 2: Yeah. Yep, definitely. So ... let's wait for my screen here. So one of the key things is if we looked at that runway tab, what we saw is we had over a year's worth of storage capacity. So, what we saw is customers had the requirement for filers, they had some excess storage, so why not actually build a software featured natively into the cluster. And that's essentially what we've done with AFS. So here we can see we have our AFS cluster, and one of the key things is the ability to scale. So, this particular cluster has around 3.1 or 3.16 billion files, which are running on this AFS cluster, as well as around 3,000 active concurrent sessions. Speaker 1: So basically thousands of concurrent sessions with billions of files? Speaker 2: Yeah, and the nice thing with this is this is actually only a four node Nutanix cluster, so as the cluster actually scales, these numbers will actually scale linearly as a function of those nodes. Speaker 1: Gotcha, gotcha. There's got to be one more bullet here on this slide so what's it about? Speaker 2: Yeah so, obviously the initial use case was realistically for home folders as well as user profiles. That was a good start, but it wasn't the only thing. So what we've done is we've actually also introduced important and upcoming release of NFS. So now you can now use NFS to also interface with our [crosstalk 01:16:44]. Speaker 1: NFS coming soon with AFS by the way, it's a big deal. Big deal. So one last thing obviously, as you go operationalize it, we've talked a lot of things on features and functions but one of the cool things that's always been seminal to this company is the fact that we all for really good customer service and support experience. Right now a lot of it is around the product, the people, the support guys, and so forth. So fundamentally to the product we have found ways using Pulse to instrument everything. With Pulse HD that has been allowed for a little bit longer now. We have fine grain [inaudible 01:17:20] around everything that's being done, so if you turn on this functionality you get a lot of information now that we built, we've used when you make a phone call, or an email, and so forth. There's a ton of context now available to support you guys. What we've now done is taken that and are now externalizing it for your own consumption, so that you don't have to necessarily call support. You can log in, look at your entire profile across your own alerts, your own advisories, your own recommendations. You can look at collective intelligence now that's coming soon which is the fact that look, here are 50 other customers just like you. These are the kinds of customers that are using workloads like you, what are their configuration profiles? Through this centralized customer insights portal you going to get a lot more insight, not just about your own operations, but also how everybody else is also using it. So let's take a quick look at that upcoming functionality. Speaker 2: Yep. Absolutely. So this is our customer 360 portal, so as [inaudible 01:18:18] mentioned, as a customer I can actually log in here, I can get a high-level overview of my existing environment, my cases, the status of those cases, as well as any relevant announcements. So, here based upon my cluster version, if there's any updates which are available, I can then see that here immediately. And then one of the other things that we've added here is this insights page. So essentially this is information that previously support would leverage to essentially proactively look out to the cluster, but now we've exposed this to you as the customer. So, clicking on this insights tab we can see an overview of our environment, in this case we have three Nutanix clusters, right around 550 virtual machines, and over here what's critical is we can actually see our cases. And one of the nice things about this is these area all autogenerated by the cluster itself, so no human interaction, no manual intervention was required to actually create these alerts. The cluster itself will actually facilitate that, send it over to support, and then support can get back out to you automatically. Speaker 1: K, so look for customer insights coming soon. And obviously that's the full life cycle. One cool thing though that's always been unique to Nutanix was the fact that we had [inaudible 01:19:28] security from day one built-in. And [inaudible 01:19:31] chunk of functionality coming in five-five just around this, because every release we try to insert more and more security capabilities, and the first one is around data. What are we doing? Speaker 2: Yeah, absolutely. So previously we had support for data at rest encryption, but this did have the requirement to leverage self-encrypting drives. These can be very expensive, so what we've done, typical to our fashion is we've actually built this in natively via software. So, here within Prism Element, I can go to data at rest encryption, and then I can go and edit this configuration here. Section 8 of 13 [01:10:00 - 01:20:04] Section 9 of 13 [01:20:00 - 01:30:04] (NOTE: speaker names may be different in each section) Steve: Encryption and then I can go and edit this configuration here. From here I could add my CSR's. I can specify KMS server and leverage native software base encryption without the requirement of SED's. Sunil: Awesome. So data address encryption [inaudible 01:20:15] coming soon, five five. Now data security is only one element, the other element was around network security obviously. We've always had this request about what are we doing about networking, what are we doing about network, and our philosophy has always been simple and clear, right. It is that the problem in networking is not the data plan. Problem in networking is the control plan. As in, if a packing loss happens to the top of an ax switch, what do we do? If there's a misconfigured board, what do we do? So we've invested a lot in full blown new network visualization that we'll show you a preview of that's all new in five five, but then once you can visualize you can take action, so you can actually using our netscape API's now in five five. You can optovision re lands on the switch, you can update reps on your load balancing pools. You can update obviously rules on your firewall. And then we've taken that to the next level, which is beyond all that, just let you go to AWS right now, what do you do? You take 100 VM's, you put it in an AWS security group, boom. That's how you get micro segmentation. You don't need to buy expensive products, you don't need to virtualize your network to get micro segmentation. That's what we're doing with five five, is built in one click micro segmentation. That's part of the core product, so why don't we just quickly show that. Okay? Steve: Yeah, let's take a look. So if we think about where we've been so far, we've done the comparison test, we've done a migration over to a Nutanix. We've deployed our new HR app. We've protected it's data, now we need to protect the network's. So one of the things you'll see that's new here is this security policies. What we'll do is we'll actually go ahead and create a new security policy and we'll just say this is HR security policy. We'll specify the application type, which in this case is HR. Sunil: HR of course. Steve: Yep and we can see our app instance is automatically populated, so based upon the number of running instances of that blueprint, that would populate that drop-down. Now we'll go ahead and click next here and what we can see in the middle is essentially those three tiers that composed that app blueprint. Now one of the important things is actually figuring out what's trying to communicate with this within my existing environment. So if I take a look over here on my left hand side, I can essentially see a few things. I can see a Ha Proxy load balancer is trying to communicate with my app here, that's all good. I want to allow that. I can see some sort of monitoring service is trying to communicate with all three of the tiers. That's good as well. Now the last thing I can see here is this IP address which is trying to access my database. Now, that's not designed and that's not supposed to happen, so what we'll do is we'll actually take a look and see what it's doing. Now hopping over to this database virtual machine or the hack VM, what we can see is it's trying to perform a brute force log in attempt to my MySQL database. This is not good. We can see obviously it can connect on the socket, however, it hasn't guessed the right password. In order to lock that down, we'll go back to our policies here and we're going to click deny. Once we've done that, we'll click next and now we'll go to Apply Now. Now we can see our newly created security policy and if we hop back over to this VM, we can now see it's actually timing out and what this means is that it's not able to communicate with that database virtual machine due to micro segmentation actively blocking that request. Sunil: Gotcha and when you go back to the Prism site, essentially what we're saying now is, it's as simple as that, to set up micro segmentation now inside your existing clusters. So that's one click micro segmentation, right. Good stuff. One other thing before we let Steve walk off the stage and then go to the bathroom, but is you guys know Steve, you know he spends a lot time in the gym, you do. Right. He and I share cubes right beside each other by the way just if you ever come to San Jose Nutanix corporate headquarters, you're always welcome. Come to the fourth floor and you'll see Steve and Sunil beside each other, most of the time I'm not in the cube, most of the time he's in the gym. If you go to his cube, you'll see all kinds of stuff. Okay. It's true, it's true, but the reason why I brought this up, was Steve recently became a father, his first kid. Oh by the way this is, clicker, this is how his cube looks like by the way but he left his wife and his new born kid to come over here to show us a demo, so give him a round of applause. Thank you, sir. Steve: Cool, thanks, Sunil. That was fun. Sunil: Thank you. Okay, so lots of good stuff. Please try out five five, give us feedback as you always do. A lot of sessions, a lot of details, have fun hopefully for the rest of the day. To talk about how their using Nutanix, you know here's one of our favorite customers and partners. He normally comes with sunglasses, I've asked him that I have to be the best looking guy on stage in my keynotes, so he's going to try to reduce his charm a little bit. Please come on up, Alessandro. Thank you. Alessandro R.: I'm delighted to be here, thank you so much. Sunil: Maybe we can stand here, tell us a little bit about Leonardo. Alessandro R.: About Leonardo, Leonardo is a key actor of the aerospace defense and security systems. Helicopters, aircraft, the fancy systems, the fancy electronics, weapons unfortunately, but it's also a global actor in high technology field. The security information systems division that is the division I belong to, 3,000 people located in Italy and in UK and there's several other countries in Europe and the U.S. $1 billion dollar of revenue. It has a long a deep experience in information technology, communications, automation, logical and physical security, so we have quite a long experience to expand. I'm in charge of the security infrastructure business side. That is devoted to designing, delivering, managing, secure infrastructures services and secure by design solutions and platforms. Sunil: Gotcha. Alessandro R.: That is. Sunil: Gotcha. Some of your focus obviously in recent times has been delivering secure cloud services obviously. Alessandro R.: Yeah, obviously. Sunil: Versus traditional infrastructure, right. How did Nutanix help you in some of that? Alessandro R.: I can tell something about our recent experience about that. At the end of two thousand ... well, not so recent. Sunil: Yeah, yeah. Alessandro R.: At the end of 2014, we realized and understood that we had to move a step forward, a big step and a fast step, otherwise we would drown. At that time, our newly appointed CEO confirmed that the IT would be a core business to Leonardo and had to be developed and grow. So we decided to start our digital transformation journey and decided to do it in a structured and organized way. Having clear in mind our targets. We launched two programs. One analysis program and one deployments programs that were essentially transformation programs. We had to renew ourselves in terms of service models, in terms of organization, in terms of skills to invest upon and in terms of technologies to adopt. We were stacking a certification of technologies that adopted, companies merged in the years before and we have to move forward and to rationalize all these things. So we spent a lot of time analyzing, comparing technologies, and evaluating what would fit to us. We had two main targets. The first one to consolidate and centralize the huge amount of services and infrastructure that were spread over 52 data centers in Italy, for Leonardo itself. The second one, to update our service catalog with a bunch of cloud services, so we decided to update our data centers. One of our building block of our new data center architecture was Nutanix. We evaluated a lot, we had spent a lot of time in analysis, so that wasn't a bet, but you are quite pioneers at those times. Sunil: Yeah, you took a lot of risk right as an Italian company- Alessandro R.: At this time, my colleague used to say, "Hey, Alessandro, think it over, remember that not a CEO has ever been fired for having chose IBM." I apologize, Bob, but at that time, when Nutanix didn't run on [inaudible 01:29:27]. We have still a good bunch of [inaudible 01:29:31] in our data center, so that will be the chance to ... Audience Member: [inaudible 01:29:37] Alessandro R.: So much you must [inaudible 01:29:37] what you announced it. Sunil: So you took a risk and you got into it. Alessandro R.: Yes, we got into, we are very satisfied with the results we have reached. Sunil: Gotcha. Alessandro R.: Most of the targets we expected to fulfill have come and so we are satisfied, but that doesn't mean that we won't go on asking you a big discount ... Sunil: Sure, sure, sure, sure. Alessandro R.: On price list. Sunil: Sure, sure, so what's next in terms of I know there are some interesting stuff that you're thinking. Alessandro R.: The next- Section 9 of 13 [01:20:00 - 01:30:04] Section 10 of 13 [01:30:00 - 01:40:04] (NOTE: speaker names may be different in each section) Speaker 1: So what's next, in terms of I know you have some interesting stuff that you're thinking of. Speaker 2: The next, we have to move forward obviously. The name Leonardo is inspired to Leonardo da Vinci, it was a guy that in terms of innovation and technology innovation had some good ideas. And so, I think, that Leonardo with Nutanix could go on in following an innovation target and following really mutual ... Speaker 1: Partnership. Speaker 2: Useful partnership, yes. We surely want to investigate the micro segmentation technologies you showed a minute ago because we have some looking, particularly by the economical point of view ... Speaker 1: Yeah, the costs and expenses. Speaker 2: And we have to give an alternative to the technology we are using. We want to use more intensively AHV, again as an alternative solution we are using. We are selecting a couple of services, a couple of quite big projects to build using AHV talking of Calm we are very eager to understand the announcement that they are going to show to all of us because the solution we are currently using is quite[crosstalk 01:31:30] Speaker 1: Complicated. Speaker 2: Complicated, yeah. To move a step of automation to elaborate and implement[inaudible 01:31:36] you spend 500 hours of manual activities that's nonsense so ... Speaker 1: Manual automation. Speaker 2: (laughs) Yes, and in the end we are very interested also in the prism features, mostly the new features that you ... Speaker 1: Talked about. Speaker 2: You showed yesterday in the preview because one bit of benefit that we received from the solution in the operations field means a bit plus, plus to our customer and a distinctive plus to our customs so we are very interested in that ... Speaker 1: Gotcha, gotcha. Thanks for taking the risk, thanks for being a customer and partner. Speaker 2: It has been a pleasure. Speaker 1: Appreciate it. Speaker 2: Bless you, bless you. Speaker 1: Thank you. So, you know obviously one OS, one click was one of our core things, as you can see the tagline doesn't stop there, it also says "any cloud". So, that's the rest of the presentation right now it's about; what are we doing, to now fulfill on that mission of one OS, one cloud, one click with one support experience across any cloud right? And there you know, we talked about Calm. Calm is not only just an operational experience for your private cloud but as you can see it's a one-click experience where you can actually up level your apps, set up blueprints, put SLA's and policies, push them down to either your AWS, GCP all your [inaudible 01:33:00] environments and then on day one while you can do one click provisioning, day two and so forth you will see new and new capabilities such as, one-click migration and mobility seeping into the product. Because, that's the end game for Calm, is to actually be your cloud autonomy platform right? So, you can choose the right cloud for the right workload. And talk about how they're building a multi cloud architecture using Nutanix and partnership a great pleasure to introduce my other good Italian friend Daniele, come up on stage please. From Telecom Italia Sparkle. How are you sir? Daniele: Not too bad thank you. Speaker 1: You want an espresso, cappuccino? Daniele: No, no later. Speaker 1: You all good? Okay, tell us a little about Sparkle. Daniele: Yeah, Sparkle is a fully owned subsidy of Telecom Italia group. Speaker 1: Mm-hmm (affirmative) Daniele: Spinned off in 2003 with the mission to develop the wholesale and multinational corporate and enterprise business abroad. Huge network, as you can see, hundreds of thousands of kilometers of fiber optics spread between; south east Asia to Europe to the U.S. Most of it proprietary part of it realized on some running cables. Part of them proprietary part of them bilateral part of them[inaudible 01:34:21] with other operators. 37 countries in which we have offices in the world, 700 employees, lean and clean company ... Speaker 1: Wow, just 700 employees for all of this. Daniele: Yep, 1.4 billion revenues per year more or less. Speaker 1: Wow, are you a public company? Daniele: No, fully owned by TIM so far. Speaker 1: So, what is your experience with Nutanix so far? Daniele: Well, in a way similar to what Alessandro was describing. To operate such a huge network as you can see before, and to keep on bringing revenues for the wholesale market, while trying to turn the bar toward the enterprise in a serious way. Couple of years ago the management team realized that we had to go through a serious transformation, not just technological but in terms of the way we build the services to our customers. In terms of how we let our customer feel the Sparkle experience. So, we are moving towards cloud but we are moving towards cloud with connectivity attached to it because it's in our cord as a provider of Telecom services. The paradigm that is driving today is the on-demand, is the dynamic and in order to get these things we need to move to software. Most of the network must become invisible as the Nutanix way. So, we decided instead of creating patchworks onto our existing systems, infrastructure, OSS, BSS and network systems, to build a new data center from scratch. And the paradigm being this new data center, the mantra was; everything is software designed, everything must be easy to manage, performance capacity planning, everything must be predictable and everything to be managed by few people. Nutanix is at the moment the baseline of this data center for what concern, let's say all the new networking tools, meaning as the end controllers that are taking care of automation and programmability of the network. Lifecycle service orchestrator, network orchestrator, cloud automation and brokerage platform and everything at the moment runs on AHV because we are forcing our vendors to certify their application on AHV. The only stack that is not at the moment AHV based is on a specific cloud platform because there we were really looking for the multi[inaudible 01:37:05]things that you are announcing today. So, we hope to do the migration as soon as possible. Speaker 1: Gotcha, gotcha. And then looking forward you're going to build out some more data center space, expose these services Daniele: Yeah. Speaker 1: For the customers as well as your internal[crosstalk 01:37:21] Daniele: Yeah, basically yes for sure we are going to consolidate, to invest more in the data centers in the markets on where we are leader. Italy, Turkey and Greece we are big data centers for [inaudible 01:37:33] and cloud, but we believe that the cloud with all the issues discussed this morning by Diraj, that our locality, customer proximity ... we think as a global player having more than 120 pops all over the world, which becomes more than 1000 in partnerships, that the pop can easily be transformed in a data center, so that we want to push the customer experience of what we develop in our main data centers closer to them. So, that we can combine traditional infrastructure as a service with the new connectivity services every single[inaudible 01:38:18] possibly everything running. Speaker 1: I mean, it makes sense, I mean I think essentially in some ways to summarize it's the example of an edge cloud where you're pushing a micro-cloud closer to the customers edge. Daniele: Absolutely. Speaker 1: Great stuff man, thank you so much, thank you so much. Daniele: Pleasure, pleasure. Thank you. Speaker 1: So, you know a couple of other things before we get in the next demo is the fact that in addition to Calm from multi-cloud management we have Zai, we talked about for extended enterprise capabilities and something for you guys to quickly understand why we have done this. In a very simple way is if you think about your enterprise data center, clearly you have a bunch of apps there, a bunch of public clouds and when you look at the paradigm you currently deploy traditional apps, we call them mode one apps, SAP, Exchange and so forth on your enterprise. Then you have next generation apps whether it be [inaudible 01:39:11] space, whether it be Doob or whatever you want to call it, lets call them mode two apps right? And when you look at these two types of apps, which are the predominant set, most enterprises have a combination of mode one and mode two apps, most public clouds primarily are focused, initially these days on mode two apps right? And when people talk about app mobility, when people talk about cloud migration, they talk about lift and shift, forklift [inaudible 01:39:41]. And that's a hard problem I mean, it's happening but it's a hard problem and ends up that its just not a one time thing. Once you've forklift, once you move you have different tooling, different operation support experience, different stacks. What if for some of your applications that mattered ... Section 10 of 13 [01:30:00 - 01:40:04] Section 11 of 13 [01:40:00 - 01:50:04] (NOTE: speaker names may be different in each section) Speaker 1: What if, for some of your applications that matter to you, that are your core enterprise apps that you can retain the same toolimg, the same operational experience and so forth. And that is what we achieve to do with Xi. It is truly making hybrid invisible, which is a next act for this company. It'll take us a few years to really fulfill the vision here, but the idea here is that you shouldn't think about public cloud as a different silo. You should think of it as an extension of your enterprise data centers. And for any services such as DR, whether it would be dev test, whether it be back-up, and so-forth. You can use the same tooling, same experience, get a public cloud-like capability without lift and shift, right? So it's making this lift and shift invisible by, soft of, homogenizing the data plan, the network plan, the control plan is what we really want to do with Xi. Okay? And we'll show you some more details here. But the simplest way to understand this is, think of it as the iPhone, right? D has mentioned this a little bit. This is how we built this experience. Views IOS as the core, IP, we wrap it up with a great package called the iPhone. But then, a few years into the iPhone era, came iTunes and iCloud. There's no apps, per se. That's fused into IOS. And similarly, think about Xi that way. The more you move VMs, into an internet-x environment, stuff like DR comes burnt into the fabric. And to give us a sneak peek into a bunch of the com and Xi cable days, let me bring back Binny who's always a popular guys on stage. Come on up, Binny. I'd be surprised in Binny untucked his shirt. He's always tucking in his shirt. Binny Gill: Okay, yeah. Let's go. Speaker 1: So first thing is com. And to show how we can actually deploy apps, not just across private and public clouds, but across multiple public clouds as well. Right? Binny Gill: Yeah, basically, you know com is about simplifying the disparity between various public clouds out there. So it's very important for us to be able to take one application blueprint and then quickly deploy in whatever cloud of your choice. Without understanding how one cloud is different. Speaker 1: Yeah, that's the goal. Binny Gill: So here, if you can see, I have market list. And by the way, this market list is a great partner community interest. And every single sort of apps come up here. Let me take a sample app here, Hadoop. And click launch. And now where do you want me to deploy? Speaker 1: Let's start at GCP. Binny Gill: GCP, okay. So I click on GCP, and let me give it a name. Hadoop. GCP. Say 30, right. Clear. So this is one click deployment of anything from our marketplace on to a cloud of your choice. Right now, what the system is doing, is taking the intent-filled description of what the application should look like. Not just the infrastructure level but also within the merchant machines. And it's creating a set of work flows that it needs to go deploy. So as you can see, while we were talking, it's loading the application. Making sure that the provisioning workflows are all set up. Speaker 1: And so this is actually, in real time it's actually extracting out some of the GCP requirements. It's actually talking to GCP. Setting up the constructs so that we can actually push it up on the GCP personally. Binny Gill: Right. So it takes a couple of minutes. It'll provision. Let me go back and show you. Say you worked with deploying AWS. So you Hadoop. Hit address. And that's it. So again, the same work flow. Speaker 1: Same process, I see. Binny Gill: It's going to now deploy in AWS. Speaker 1: See one of the keys things is that we actually extracted out all the isms of each of these clouds into this logical substrate. Binny Gill: Yep. Speaker 1: That you can now piggy-back off of. Binny Gill: Absolutely. And it makes it extremely simple for the average consumer. And you know we like more cloud support here over time. Speaker 1: Sounds good. Binny Gill: Now let me go back and show you an app that I had already deployed. Now 13 days ago. It's on GCP. And essentially what I want to show you is what is the view of the application. Firstly, it shows you the cost summary. Hourly, daily, and how the cost is going to look like. The other is how you manage it. So you know one click ways of upgrading, scaling out, starting, deleting, and so on. Speaker 1: So common actions, but independent of the type of clouds. Binny Gill: Independent. And also you can act with these actions over time. Right? Then services. It's learning two services, Hadoop slave and Hadoop master. Hadoop slave runs fast right now. And auditing. It shows you what are the important actions you've taken on this app. Not just, for example, on the IS front. This is, you know how the VMs were created. But also if you scroll down, you know how the application was deployed and brought up. You know the slaves have to discover each other, and so on. Speaker 1: Yeah got you. So find game invisibility into whatever you were doing with clouds because that's been one of the complaints in general. Is that the cloud abstractions have been pretty high level. Binny Gill: Yeah. Speaker 1: Yeah. Binny Gill: Yeah. So that's how we make the differences between the public clouds. All go away for the Indias of ... Speaker 1: Got you. So why don't we now give folks ... Now a lot of this stuff is coming in five, five so you'll see that pretty soon. You'll get your hands around it with AWS and tree support and so forth. What we wanted to show you was emerging alpha version that is being baked. So is a real production code for Xi. And why don't we just jump right in to it. Because we're running short of time. Binny Gill: Yep. Speaker 1: Give folks a flavor for what the production level code is already being baked around. Binny Gill: Right. So the idea of the design is make sure it's not ... the public cloud is no longer any different from your private cloud. It's a true seamless extension of your private cloud. Here I have my test environment. As you can see I'm running the HR app. It has the DB tier and the Web tier. Yeah. Alright? And the DB tier is running Oracle DB. Employee payroll is the Web tier. And if you look at the availability zones that I have, this is my data center. Now I want to protect this application, right? From disaster. What do I do? I need another data center. Speaker 1: Sure. Binny Gill: Right? With Xi, what we are doing is ... You go here and click on Xi Cloud Services. Speaker 1: And essentially as the slide says, you are adding AZs with one click. Binny Gill: Yeps so this is what I'm going to do. Essentially, you log in using your existing my.nutanix.com credentials. So here I'm going to use my guest credentials and log in. Now while I'm logging in what's happening is we are creating a seamless network between the two sides. And then making the Xi cloud availability zone appear. As if it was my own. Right? Speaker 1: Gotcha. Binny Gill: So in a couple of seconds what you'll notice this list is here now I don't have just one availability zone, but another one appears. Speaker 1: So you have essentially, real time now, paid a one data center doing an availability zone. Binny Gill: Yep. Speaker 1: Cool. Okay. Let's see what else we can do. Binny Gill: So now you think about VR setup. Now I'm armed with another data center, let's do DR Center. Now DR set-up is going to be extremely simple. Speaker 1: Okay but it's also based because on the fact that it is the same stack on both sides. Right? Binny Gill: It's the same stack on both sides. We have a secure network lane connecting the two sides, on top of the secure network plane. Now data can flow back and forth. So now applications can go back and forth, securely. Speaker 1: Gotcha, okay. Let's look at one-click DR. Binny Gill: So for one-click DR set-up. A couple of things we need to know. One is a protection rule. This is the RPO, where does it apply to? Right? And the connection of the replication. The other one is recovery plans, in case disaster happens. You know, how do I bring up my machines and application work-order and so on. So let me first show you, Protection Rule. Right? So here's the protection rule. I'll create one right now. Let me call it Platinum. Alright, and source is my own data center. Destination, you know Xi appears now. Recovery point objective, so maybe in a one hour these snapshots going to the public cloud. I want to retain three in the public side, three locally. And now I select what are the entities that I want to protect. Now instead of giving VMs my name, what I can do is app type employee payroll, app type article database. It covers both the categories of the application tiers that I have. And save. Speaker 1: So one of the things here, by the way I don't know if you guys have noticed this, more and more of Nutanix's constructs are being eliminated to become app-centric. Of course is VM centric. And essentially what that allows one to do is to create that as the new service-level API/abstraction. So that under the cover over a period of time, you may be VMs today, maybe containers tomorrow. Or functions, the day after. Binny Gill: Yep. What I just did was all that needs to be done to set up replication from your own data center to Xi. So we started off with no data center to actually replication happening. Speaker 1: Gotcha. Binny Gill: Okay? Speaker 1: No, no. You want to set up some recovery plans? Binny Gill: Yeah so now set up recovery plan. Recovery plans are going to be extremely simple. You select a bunch of VMs or apps, and then there you can say what are the scripts you want to run. What order in which you want to boot things. And you know, you can set up access these things with one click monthly or weekly and so on. Speaker 1: Gotcha. And that sets up the IPs as well as subnets and everything. Binny Gill: So you have the option. You can maintain the same IPs on frame as the move to Xi. Or you can make them- Speaker 1: Remember, you can maintain your own IPs when you actually use the Xi service. There was a lot of things getting done to actually accommodate that capability. Binny Gill: Yeah. Speaker 1: So let's take a look at some of- Binny Gill: You know, the same thing as VPC, for example. Speaker 1: Yeah. Binny Gill: You need to possess on Xi. So, let's create a recovery plan. A recovery plan you select the destination. Where does the recovery happen. Now, after that Section 11 of 13 [01:40:00 - 01:50:04] Section 12 of 13 [01:50:00 - 02:00:04] (NOTE: speaker names may be different in each section) Speaker 1: ... does the recovery happen. Now, after that you have to think of what is the runbook that you want to run when disaster happens, right? So you're preparing for that, so let me call "HR App Recovery." The next thing is the first stage. We're doing the first stage, let me add some entities by categories. I want to bring up my database first, right? Let's click on the database and that's it. Speaker 2: So essentially, you're building the script now. Speaker 1: Building the script- Speaker 2: ... on the [inaudible 01:50:30] Speaker 1: ... but in a visual way. It's simple for folks to understand. You can add custom script, add delay and so on. Let me add another stage and this stage is about bringing up the web tier after the database is up. Speaker 2: So basically, bring up the database first, then bring up the web tier, et cetera, et cetera, right? Speaker 1: That's it. I've created a recovery plan. I mean usually it's complicated stuff, but we made it extremely simple. Now if you click on "Recovery Points," these are snapshots. Snapshots of your applications. As you can see, already the system has taken three snapshots in response to the protection rule that we had created just a couple minutes ago. And these are now being seeded to Xi data centers. Of course this takes time for seeding, so what I have is a setup already and that's the production environment. I'll cut over to that. This is my production environment. Click "Explore," now you see the same application running in production and I have a few other VMs that are not protected. Let's go to "Recovery Points." It has been running for sometime, these recover points are there and they have been replicated to Xi. Speaker 2: So let's do the failover then. Speaker 1: Yeah, so to failover, you'll have to go to Xi so let me login to Xi. This time I'll use my production account for logging into Xi. I'm logging in. The first thing that you'll see in Xi is a dashboard that gives you a quick summary of what your DR testing has been so far, if there are any issues with the replication that you have and most importantly the monthly charges. So right now I've spent with my own credit card about close to 1,000 bucks. You'll have to refund it quickly. Speaker 2: It depends. If the- Speaker 1: If this works- Speaker 2: IF the demo works. Speaker 1: Yeah, if it works, okay. As you see, there are no VMs right now here. If I go to the recovery points, they are there. I can click on the recovery plan that I had created and let's see how hard it's going to be. I click "Failover." It says three entities that, based on the snapshots, it knows that it can recovery from source to destination, which is Xi. And one click for the failover. Now we'll see what happens. Speaker 2: So this is essentially failing over my production now. Speaker 1: Failing over your production now. [crosstalk 01:52:53] If you click on the "HR App Recovery," here you see now it started the recovery plan. The simple recovery plan that we had created, it actually gets converted to a series of tasks that the system has to do. Each VM has to be hydrated, powered on in the right order and so on and so forth. You don't have to worry about any of that. You can keep an eye on it. But in the meantime, let's talk about something else. We are doing failover, but after you failover, you run in Xi as if it was your own setup and environment. Maybe I want to create a new VM. I create a VM and I want to maybe extend my HR app's web tier. Let me name it as "HR_Web_3." It's going to boot from that disk. Production network, I want to run it on production network. We have production and test categories. This one, I want to give it employee payroll category. Now it applies the same policies as it's peers will. Here, I'm going to create the VM. As you can see, I can already see some VMs coming up. There you go. So three VMs from on-prem are now being filled over here while the fourth VM that I created is already being powered. Speaker 2: So this is basically realtime, one-click failover, while you're using Xi for your [inaudible 01:54:13] operations as well. Speaker 1: Exactly. Speaker 2: Wow. Okay. Good stuff. What about- Speaker 1: Let me add here. As the other cloud vendors, they'll ask you to make your apps ready for their clouds. Well we tell our engineers is make our cloud ready for your apps. So as you can see, this failover is working. Speaker 2: So what about failback? Speaker 1: All of them are up and you can see the protection rule "platinum" has been applied to all four. Now let's look at this recovery plan points "HR_Web_3" right here, it's already there. Now assume the on-prem was already up. Let's go back to on-prem- Speaker 2: So now the scenario is, while Binny's coming up, is that the on-prem has come back up and we're going to do live migration back as in a failback scenario between the data centers. Speaker 1: And how hard is it going to be. "HR App Recovery" the same "HR App Recovery", I click failover and the system is smart enough to understand the direction is reversed. It's also smart enough to figure out "Hey, there are now the four VMs are there instead of three." Xi to on-prem, one-click failover again. Speaker 2: And it's rerunning obviously the same runbook but in- Speaker 1: Same runbook but the details are different. But it's hidden from the customer. Let me go to the VMs view and do something interesting here. I'll group them by availability zone. Here you go. As you can see, this is a hybrid cloud view. Same management plane for both sides public and private. There are two availability zones, the Xi availability zone is in the cloud- Speaker 2: So essentially you're moving from the top- Speaker 1: Yeah, top- Speaker 2: ... to the bottom. Speaker 1: ... to the bottom. Speaker 2: That's happening in the background. While this is happening, let me take the time to go and look at billing in Xi. Speaker 1: Sure, some of the common operations that you can now see in a hybrid view. Speaker 2: So you go to "Billing" here and first let me look at my account. And account is a simple page, I have set up active directory and you can add your own XML file, upload it. You can also add multi-factor authentication, all those things are simple. On the billing side, you can see more details about how did I rack up $966. Here's my credit card. Detailed description of where the cost is coming from. I can also download previous versions, builds. Speaker 1: It's actually Nutanix as a service essentially, right? Speaker 2: Yep. Speaker 1: As a subscription service. Speaker 2: Not only do we go to on-prem as you can see, while we were talking, two VMs have already come back on-prem. They are powered off right now. The other two are on the wire. Oh, there they are. Speaker 1: Wow. Speaker 2: So now four VMs are there. Speaker 1: Okay. Perfect. Sometimes it works, sometimes it doesn't work, but it's good. Speaker 2: It always works. Speaker 1: Always works. All right. Speaker 2: As you can see the platinum protection rule is now already applied to them and now it has reversed the direction of [inaudible 01:57:12]- Speaker 1: Remember, we showed one-click DR, failover, failback, built into the product when Xi ships to any Nutanix fabric. You can start with DSX on premise, obviously when you failover to Xi. You can start with AHV, things that are going to take the same paradigm of one-click operations into this hybrid view. Speaker 2: Let's stop doing lift and shift. The era has come for click and shift. Speaker 1: Binny's now been promoted to the Chief Marketing Officer, too by the way. Right? So, one more thing. Speaker 2: Okay. Speaker 1: You know we don't stop any conferences without a couple of things that are new. The first one is something that we should have done, I guess, a couple of years ago. Speaker 2: It depends how you look at it. Essentially, if you look at the cloud vendors, one of the key things they have done is they've built services as building blocks for the apps that run on top of them. What we have done at Nutanix, we've built core services like block services, file services, now with Calm, a marketplace. Now if you look at [inaudible 01:58:14] applications, one of the core building pieces is the object store. I'm happy to announce that we have the object store service coming up. Again, in true Nutanix fashion, it's going to be elastic. Speaker 1: Let's- Speaker 2: Let me show you. Speaker 1: Yeah, let's show it. It's something that is an object store service by the way that's not just for your primary, but for your secondary. It's obviously not just for on-prem, it's hybrid. So this is being built as a next gen object service, as an extension of the core fabric, but accommodating a bunch of these new paradigms. Speaker 2: Here is the object browser. I've created a bunch of buckets here. Again, object stores can be used in various ways: as primary object store, or for secondary use cases. I'll show you both. I'll show you a Hadoop use case where Hadoop is using this as a primary store and a backup use case. Let's just jump right in. This is a Hadoop bucket. AS you can see, there's a temp directory, there's nothing interesting there. Let me go to my Hadoop VM. There it is. And let me run a Hadoop job. So this Hadoop job essentially is going to create a bunch of files, write them out and after that do map radius on top. Let's wait for the job to start. It's running now. If we go back to the object store, refresh the page, now you see it's writing from benchmarks. Directory, there's a bunch of files that will write here over time. This is going to take time. Let's not wait for it, but essentially, it is showing Hadoop that uses AWS 3 compatible API, that can run with our object store because our object store exposes AWS 3 compatible APIs. The other use case is the HYCU backup. As you can see, that's a- Section 12 of 13 [01:50:00 - 02:00:04] Section 13 of 13 [02:00:00 - 02:13:42] (NOTE: speaker names may be different in each section) Vineet: This is the hycu back up ... As you can see, that's a back-up software that can back-up WSS3. If you point it to Nutanix objects or it can back-up there as well. There are a bunch of back-up files in there. Now, object stores, it's very important for us to be able to view what's going on there and make sure there's no objects sprawled because once it's easy to write objects, you just accumulate a lot of them. So what we wanted to do, in true Nutanix style, is give you a quick overview of what's happening with your object store. So here, as you can see, you can look at the buckets, where the load is, you can look at the bucket sizes, where the data is, and also what kind of data is there. Now this is a dashboard that you can optimize, and customize, for yourself as well, right? So that's the object store. Then we go back here, and I have one more thing for you as well. Speaker 2: Okay. Sounds good. I already clicked through a slide, by the way, by mistake, but keep going. Vineet: That's okay. That's okay. It is actually a quiz, so it's good for people- Speaker 2: Okay. Sounds good. Vineet: It's good for people to have some clues. So the quiz is, how big is my SAP HANA VM, right? I have to show it to you before you can answer so you don't leak the question. Okay. So here it is. So the SAP HANA VM here vCPU is 96. Pretty beefy. Memory is 1.5 terabytes. The question to all of you is, what's different in this screen? Speaker 2: Who's a real Prism user here, by the way? Come on, it's got to be at least a few. Those guys. Let's see if they'll notice something. Vineet: What's different here? Speaker 3: There's zero CVM. Vineet: Zero CVM. Speaker 2: That's right. Yeah. Yeah, go ahead. Vineet: So, essentially, in the Nutanix fabric, every server has to run a [inaudible 02:01:48] machine, right? That's where the storage comes from. I am happy to announce the Acropolis Compute Cloud, where you will be able to run the HV on servers that are storage-less, and add it to your existing cluster. So it's a compute cloud that now can be managed from Prism Central, and that way you can preserve your investments on your existing server farms, and add them to the Nutanix fabric. Speaker 2: Gotcha. So, essentially ... I mean, essentially, imagine, now that you have the equivalent of S3 and EC2 for the enterprise now on Premisis, like you have the equivalent compute and storage services on JCP and AWS, and so forth, right? So the full flexibility for any kind of workload is now surely being available on the same Nutanix fabric. Thanks a lot, Vineet. Before we wrap up, I'd sort of like to bring this home. We've announced a pretty strategic partnership with someone that has always inspired us for many years. In fact, one would argue that the genesis of Nutanix actually was inspired by Google and to talk more about what we're actually doing here because we've spent a lot of time now in the last few months to really get into the product capabilities. You're going to see some upcoming capabilities and 55X release time frame. To talk more about that stuff as well as some of the long-term synergies, let me invite Bill onstage. C'mon up Bill. Tell us a little bit about Google's view in the cloud. Bill: First of all, I want to compliment the demo people and what you did. Phenomenal work that you're doing to make very complex things look really simple. I actually started several years ago as a product manager in high availability and disaster recovery and I remember, as a product manager, my engineers coming to me and saying "we have a shortage of our engineers and we want you to write the fail-over routines for the SAP instance that we're supporting." And so here's the PERL handbook, you know, I haven't written in PERL yet, go and do all that work to include all the network setup and all that work, that's amazing, what you are doing right there and I think that's the spirit of the partnership that we have. From a Google perspective, obviously what we believe is that it's time now to harness the power of scale security and these innovations that are coming out. At Google we've spent a lot of time in trying to solve these really large problems at scale and a lot of the technology that's been inserted into the industry right now. Things like MapReduce, things like TenserFlow algorithms for AI and things like Kubernetes and Docker were first invented at Google to solve problems because we had to do it to be able to support the business we have. You think about search, alright? When you type in search terms within the search box, you see a white screen, what I see is all the data-center work that's happening behind that and the MapReduction to be able to give you a search result back in seconds. Think about that work, think about that process. Taking and pursing those search terms, dividing that over thousands of [inaudible 02:05:01], being able to then search segments of the index of the internet and to be able to intelligent reduce that to be able to get you an answer within seconds that is prioritized, that is sorted. How many of you, out there, have to go to page two and page three to get the results you want, today? You don't because of the power of that technology. We think it's time to bring that to the consumer of the data center enterprise space and that's what we're doing at Google. Speaker 2: Gotcha, man. So I know we've done a lot of things now over the last year worth of collaboration. Why don't we spend a few minutes talking through a couple things that we're started on, starting with [inaudible 02:05:36] going into com and then we'll talk a little bit about XI. Bill: I think one of the advantages here, as we start to move up the stack and virtualize things to your point, right, is virtual machines and the work required of that still takes a fair amount of effort of which you're doing a lot to reduce, right, you're making that a lot simpler and seamless across both On-Prem and the cloud. The next step in the journey is to really leverage the power of containers. Lightweight objects that allow you to be able to head and surface functionality without being dependent upon the operating system or the VM to be able to do that work. And then having the orchestration layer to be able to run that in the context of cloud and On-Prem We've been very successful in building out the Kubernetes and Docker infrastructure for everyone to use. The challenge that you're solving is how to we actually bridge the gap. How do we actually make that work seamlessly between the On-Premise world and the cloud and that's where our partnership, I think, is so valuable. It's cuz you're bringing the secret sauce to be able to make that happen. Speaker 2: Gotcha, gotcha. One last thing. We talked about Xi and the two companies are working really closely where, essentially the Nutanix fabric can seamlessly seep into every Google platform as infrastructure worldwide. Xi, as a service, could be delivered natively with GCP, leading to some additional benefits, right? Bill: Absolutely. I think, first and foremost, the infrastructure we're building at scale opens up all sorts of possibilities. I'll just use, maybe, two examples. The first one is network. If you think about building out a global network, there's a lot of effort to do that. Google is doing that as a byproduct of serving our consumers. So, if you think about YouTube, if you think about there's approximately a billion hours of YouTube that's watched every single day. If you think about search, we have approximately two trillion searches done in a year and if you think about the number of containers that we run in a given week, we run about two billion containers per week. So the advantage of being able to move these workloads through Xi in a disaster recovery scenario first is that you get to take advantage of the scale. Secondly, it's because of the network that we've built out, we had to push the network out to the edge. So every single one of our consumers are using YouTube and search and Google Play and all those services, by the way we have over eight services today that have more than a billion simultaneous users, you get to take advantage of that network capacity and capability just by moving to the cloud. And then the last piece, which is a real advantage, we believe, is that it's not just about the workloads you're moving but it's about getting access to new services that cloud preventers, like Google, provide. For example, are you taking advantage like the next generation Hadoop, which is our big query capability? Are you taking advantage of the artificial intelligence derivative APIs that we have around, the video API, the image API, the speech-to-text API, mapping technology, all those additional capabilities are now exposed to you in the availability of Google cloud that you can now leverage directly from systems that are failing over and systems that running in our combined environment. Speaker 2: A true converged fabric across public and private. Bill: Absolutely. Speaker 2: Great stuff Bill. Thank you, sir. Bill: Thank you, appreciate it. Speaker 2: Good to have you. So, the last few slides. You know we've talked about, obviously One OS, One Click and eCloud. At the end of the day, it's pretty obvious that we're evaluating the move from a form factor perspective, where it's not just an OS across multiple platforms but it's also being distributed genuinely from consuming itself as an appliance to a software form factor, to subscription form factor. What you saw today, obviously, is the fact that, look you know we're still continuing, the velocity has not slowed down. In fact, in some cases it's accelerated. If you ask my quality guys, if you ask some of our customers, we're coming out fast and furious with a lot of these capabilities. And some of this directly reflects, not just in features, but also in performance, just like a public cloud, where our performance curve is going up while our price-performance curve is being more attractive over a period of time. And this is balancing it with quality, it is what differentiates great companies from good companies, right? So when you look at the number of nodes that have been shipping, it was around ten more nodes than where we were a few years ago. But, if you look at the number of customer-found defects, as a percentage of number of nodes shipped it is not only stabilized, it has actually been coming down. And that's directly reflected in the NPS part. That most of you guys love. How many of you guys love your Customer Support engineers? Give them a round of applause. Great support. So this balance of velocity, plus quality, is what differentiates a company. And, before we call it a wrap, I just want to leave you with one thing. You know, obviously, we've talked a lot about technology, innovation, inspiration, and so forth. But, as I mentioned, from last night's discussion with Sir Ranulph, let's think about a few things tonight. Don't take technology too seriously. I'll give you a simple story that he shared with me, that puts things into perspective. The year was 1971. He had come back from Aman, from his service. He was figuring out what to do. This was before he became a world-class explorer. 1971, he had a job interview, came down from Scotland and applied for a role in a movie. And he failed that job interview. But he was selected from thousands of applicants, came down to a short list, he was a ... that's a hint ... he was a good looking guy and he lost out that role. And the reason why I say this is, if he had gotten that job, first of all I wouldn't have met him, but most importantly the world wouldn't have had an explorer like him. The guy that he lost out to was Roger Moore and the role was for James Bond. And so, when you go out tonight, enjoy with your friends [inaudible 02:12:06] or otherwise, try to take life a little bit once upon a time or more than once upon a time. Have fun guys, thank you. Speaker 5: Ladies and gentlemen please make your way to the coffee break, your breakout sessions will begin shortly. Don't forget about the women's lunch today, everyone is welcome. Please join us. You can find the details in the mobile app. Please share your feedback on all sessions in the mobile app. There will be prizes. We will see you back here and 5:30, doors will open at 5, after your last breakout session. Breakout sessions will start sharply at 11:10. Thank you and have a great day. Section 13 of 13 [02:00:00 - 02:13:42]

[Narrator] Live from Las Vegas, it's theCUBE. Covering VMworld 2017. Brought to you by VMware, and it's ecosystem partner. >> Hi, I'm Stu Miniman, here with my cohost Keith Townsend. You're watching theCUBE's coverage of VMworld 2017 here in Las Vegas. Happy to welcome to the program two guests who are going to dig into what's happening in the cloud space. A big, big hot topic of the show. Dave Shacochis, who is the vice president of product management at CenturyLink, Ajay Patel, SVP/GM of now Cloud Provider Software at VMware. Gentlemen, thanks so much for joining us. >> Thank you Stu. >> Nice to see you again Stu. >> Alright, so Dave. Here's a question we've asked coming into this week. VMware was doing this vCloud Air for a bunch of years. They're a competitor, no they're a partner with the vCloud network ... vCloud air now went over to OVH, and I think they waited 48 hours before they made this big deal with AWS so, tell us how the relationship has been not just one of the 4,500 service providers, but you're sitting on panels with VMware, you're one of the larger partners. >> We were on a panel discussion and we were talking about this earlier today. I think when vCloud Air launched we had some of these same conversations, and there were probably cube discussions where almost the same question was asked. What I said back then, and what a lot of us in the service provider community said back then, and we say it again now, is that ... And this is true, not just of VMware, but this is true of any enterprise architect, you run a better system, you build better software when you're running it 24-7 as a live service. It's just better. The software is better. The user experience is better. You're thinking about integration angles, and availability issues. The software gets better when you run it operationally, and VMware's technology got better when they launched vCloud Air and figured out that their virtualization technology, what they had been working with the service provider community around for years, it improved when they went and launched it and lived the life of a service provider. So we're actually excited about that. We're aligning to the same architecture. What's nice is that what they're running in the cloud, in the VMware cloud foundation, is the same thing we're running in our cloud-neutral facilities inside of the CenturyLink data center footprint. So, it's very interoperable. >> Ajay please ... >> So my response would be there are a few things that I've changed. One is, there wasn't a Cloud provider software business unit. I am dedicated to making the likes of David successful. Taking that IP and commercializing that, that's fundamental to our strategy. Second one is, we rebranded this to VMware cloud providers. The idea is you can get VMware cloud in one of three ways. You can build it yourself, get it on VMware cloud or AWS, more importantly but get it through our partners. Your choice based on the best cloud that fits your needs. So it's that level playing field, both on go to market, in terms of Geoff Waters, now the cloud sales leader over all of the different programs, technology, IP being made available, compensation neutrality ... These are all the things we "learn" from our VCM experience, if you will to do this right. So that we continue driving multi-cloud strategy, and certainly about centered around customer choice. >> Can we talk about the basic difference between those three delivery methods? From a customer's perspective, what's the difference in the look and feel of those? >> I think at the end of the day it's about getting VMware value in an integrated fashion. But that's not just sufficient, so when you go to cloud it's no longer just say, "Give me a virtualized environment." That's the "hard bit" of packaging stuff infrastructure, but that's not enough value. On top of that is the application is really the value. Managing that application, and the life cycle of the value. This is where the likes of CenturyLink really come into play. So we believe we're kind of democratizing in terms of the consumption of a cloud stack in one of three ways. It's really customer preference, and really how much burden they want to take on. On the private cloud side they're building it instead of buying it as a service. They prefer to go on AWS for whatever reason for their cloud strategy. They now have a VMware choice. Or they can go to a partner like CenturyLink to help them manage the entire journey including managing multiple clouds. So it's really about the customer choice, what's right for them versus putting them in a silo. >> What's really been good for us especially around the VMware cloud foundation reference architecture is that it starts to make the private clouds react predictably. Our offer net has now been architected and based around VMware Cloud Foundation. It stands up with the software defined data center architecture at each layer of the stack. We don't have to orchestrate nearly as many technology sets in order to make a private cloud app. We've been running hosted private cloud for as long as there have been hosted private clouds. CenturyLink has been managing as part of the cloud service provider program and all its earlier naming variances. But what this latest architecture allows us to do is not only remove the number of things that we need to integrate against, the integration code we need to write and all the different vendor technologies we need to orchestrate against it, it pulls it all into one scale out software, a divine stack, which makes our customer experience better. It drives better self-service, more reliable self-service, into the hands of our customers so that they can move faster. It allows our private cloud to become more predictable so that we can start managing it with our multi-cloud cloud application manager product. So we launched that earlier this year. It was a combination of some of the managed hosting tools and capabilities that we've had back in the days. It combines in the abstraction software we got from a company called ElasticBox that we acquired last year. We weave that together into one multi-cloud layer, so it now looks at private clouds and other public clouds as just another deployment destination on that multi-cloud managing journey. >> Effectively competition moving above the SVC layer. We're kind of making SVC common. Let's compete on the value, and the solution that we both want. >> Ironically this was the promise of open source projects to make this common platform across private, public, and multi-clouds. You use the term that a lot of people may not be familiar with, cloud neutral facilities. What is that term? >> A cloud neutral facility is one that can basically get you connected to a number of different cloud deployment form factors. It's not a one note show, a one approach kind of model. It's really about a service provider that from... When you said the term facility, that can really just be a service provider environment that basically gets the particular workload to the best execution venue for that individual set of run time conditions. To us, being in more of a cloud neutral posture, certainly means we're bringing some parts of our hosted environment, whether it's private or We have a multi-tenant environment that we can provision to as well. We use that multi-tenant environment to actually speed up our own development of higher level services. And then we partner across the different cloud service providers like AWS and Microsoft Azure. We tie into that. It's really about looking at the data center as an extension of all the potential run time venues, both ones that you might build on your own, and then ones that are available to you. >> Dave, I want you to expand on that. One of the things I've been getting out of this week is that maturation of how we've been talking about clouds. A couple years ago I was critical of VMware. It was like, any device, any application, one cloud. I was like "Wrong". No. Amazon. Absolutely, 100 percent public cloud ... I think they understand, if not 100 percent, we'll see where Amazon goes in the future. You said you're tying into the likes of Amazon and Azure. I'm assuming that's direct connect, and those kinds of services. How do we think of CenturyLink? Where do you add value? How do you make money in these various pieces? I remember (old company name) was one of the vCloud era data centers, and boy margins were going to be real tight on something like that. >> Our multi-cloud posture and the direction we see things going is really one that starts and the largest anchor point for CenturyLink's strategy is the strength of our network. It's all the places that that network can take us. A lot of the investments that we've made in virtualization management, a lot of the investments we've made around managing workloads inside data centers we control has really been a precursor to how we need to evolve the core of our network, and how our networking is becoming more software defined. We built and we launched, as I said before, CenturyLink Cloud which is a multi-tenant hosting environment. That has been a huge IT accelerator for us. As we've started to advance and start to figure out how do we manage virtualization inside the core of our points of presence on the network, and as our network starts to expand, as most folks know, we're in the closing stages of the announced acquisition of level three, as that transaction completes and the whole network gets even stronger, and now we have more software assets to be able to drive even further into the core of that network. So it starts from the network and everything we do from either a cloud neutral or multi-cloud perspective is really around helping customers at the workload layer to really thicken that network value proposition. >> I'm also excited about the whole notion of competing on the edge. And once you have a network of this scale, and the ability to then distribute, compute, either on the edge, consult in the back, or even leverage third party probably clouds, seamlessly with a high bandwidth, low jitter network. I think that's a foundational infrastructure that's needed. These guys have really done a good job of kind of bringing that to bear. Pretty excited about that opportunity. >> Ajay, wondering if you can give us a little color on service providers. When I go to most service providers, most of them, networking key strength, obviously we know CenturyLink, Telco, all that kind of background. Management layer. Most service providers build their own. So there's a lot of pieces now, when I see the cloud foundation suite and they're embracing it. How did you work through some of those, "Hey, no, we've got our way of doing things. We know better." As opposed to embracing them. Where is that give and take? >> I think what's happening is, depending on the sophistication of the service provider, the larger ones have the ability to kind of create a bare metal service, kind of drive higher automation, have the infrastructure spend to drive that. As you go a little bit down the market, they're really looking for "a cloud in a box". You and I spoke about this last year, right? They want an easy to type experience for the end customers without the cost and the complexity of building one. So my opportunity as a service provider business is, how do I give them that platform? That multi-tenant platform that can cover resources? But in the future, elastically leverage a VMware cloud on AWS, right, as an endpoint that they can start to use for geo distribution, DR, or simply new capacity. So we're going to see a world where they're going to start mixing and matching what they build, what they buy and how they drive that. And the management solution around that, around a high performance network, is going to be the future that I see together. >> So one of the buzzwords over the past few year in the industry has been the invisible infrastructure. This concept that infrastructure should be something that people use and don't see. How does CenturyLink help support, not necessarily making an invisible infrastructure, but this concept that this is something we use and don't see. From the network, to the software layer that we're now talking about. Where's the differentiating value that CenturyLink brings versus me rolling my own? >> Yeah, I think where we've been making most of our investments, and where we've been driving and focusing on success for our customers has been up at that managed services and application layer. The way we view the infrastructure layer of the stack ... When we think of stacks, we think of the network at the base level of the foundation, data center infrastructure at the next tier up and then workloads and applications. It's not a groundbreaking tiered model, but it's helped me kind of think and organize a lot of what's in our business. When it comes to the infrastructure layer, as I said before, we're in a highly interoperable posture with a lot of the other partner clouds, because our network can link us there pretty seamlessly, and because we still know how to orchestrate enough at the infrastructure layer. But the investment has really been inside the core of the network, as we start driving that virtualization capabilities into the core, and then up at the workload layer, what we're really trying to work around is creating, as in all computer science problems, an abstraction layer. The trick about an abstraction layer in our part of the world, and in our part of the industry is not creating one that creates a new layer of lock in. That allows each of the individual underpinning infrastructure venues to do their thing, and do what they're good at. We build that abstraction layer with the idea of a best execution venue mindset that lets each of those individual underpinning infrastructure offerings, whether its the VCF architecture or hosted up on AWS, or whether it's one of the other particular software platforms because of geography or performance, or service capabilities that they're good at. The trick of creating an abstraction layer is not locking anybody in or reducing those platforms to lowest common denominator. So what our cloud application manager offering being able to manage our private cloud based on VCF, as well as manage other environments down the road ... That's really where we try to make that infrastructure invisible is to sort of create a lightweight abstraction layer that they can think more at the workload layer than at the individual nuts and bolts layer. >> The great thing about creating an abstraction layer, when you own the underlying infrastructure, it makes it a lot easier to support. So I want to make sure that I understand this concept from the ground up. You talked about the network as being the glue or the foundation that ties all this together, especially with the level three acquisition. From an ILT perspective, if I need those far flung services I have the physical network capability to get it there. If I need to put (data terminology) in at the edge, we just had a guest on talking about (data terminology), and at the edge. And get that data into a CenturyLink data center using VCF to get it there and consistently have that same level of abstraction, and then I can build cloud native applications on Azure, Google Compute... (cross talking) and it's a consistent experience across that whole abstraction layer. >> Right. Right. Going back to that idea that, what we call the hybrid IT stack of network infrastructure and workloads, what we're trying to build is a platform that spans those layers, that doesn't try to own or be one or indifferentiate at one of those layers, is build a connective tissue that spans them, so a workload running on the right infrastructure venue connected to the right networks. We're investing in orchestration that crosses all of that, and it's really some of the great conversations we've been having this week with VMware about what they're thinking, we think PTS is interesting because container based deployment models are going to be what makes the most sense as you get further into the core of the network and out towards the edge. We think Pulse is interesting. As we start to do more things in our smart cities, and smart venue type of initiatives, that we're doing at the Internet Of Things solutions base as well. >> Ajay, last thing I want to get to is when you look at your partners, how do you see them? Both that similarity that they're going to have, but how do they differentiate, and also how will they participate in the VMware on AWS piece that we've been talking about? >> Yes, so I think I'll break it into two parts. As I talk to customers, the consistent feedback I get is we made resource consumption ubiquitous. And we're hoping to standardize that with VMware Cloud Foundation and other approaches. What's hard is the experienced skillset and knowledge of how to use this technology. So increasingly we're constrained with the folks who know how to take this complexity, put an organized plan together, and drive the set of value in our own applications. So I believe the cloud provider program and the partnership is really about moving up from trying to build infrastructure, to build solutions, and offer value to our partners. And the differentiation is really moving up stack in terms that manage services value. The second part is- They themselves now have a choice. If I'm a regional player, or customer who, everyone's a multinational nowadays, you always have some customer who happens to reach beyond the boundaries ... How do I now go into a new market? How can I leverage VMware Cloud on AWS as another data center? So the management technology we're trying to provide is we will priority manage your endpoint, customer endpoint, or even VMware Cloud. You mix and match what makes business sense. Then abstract the complexity. As we talked about the cloud as a new hardware. How do we take that infrastructure and really make it easy? And the issues are on security, management, are going to be different ... So, application usage, value added services, being able to leverage resources, build or buy is really the basis of our strategy. >> Yep. So we're excited to ... As we know that that program starts to expand a little bit more in 2018 and we've had some early discussions with the VMware team around what that starts to look like, but at our most foundational level, because what we're already launching and what we launched here this week at VMware is just what we call our dedicated cloud compute product, which is now based on the VMware Cloud Foundation reference architecture. It's going to look the exact same as the VMware Cloud Foundation architecture that runs in AWS. Our approach towards managing both is to let their own individual control panels do what they do best, but then manage over the top of it with our cloud application manager service. >> Dave and Ajay. Thank you so much for sharing with us all the updates. Look forward to watching the continued maturation and development of what's happening in the cloud environment. >> Great chat, thank you. >> Thank you. >> Keith Townsend and I will be back with lots more coverage here of VMworld 2017. You're watching theCUBE. (electronic music)

(exciting upbeat music) >> Announcer: Live from Las Vegas. It's theCUBE. Covering VMworld 2017, brought to you by VMware, and its ecosystem partner. >> Okay, we're back in Las Vegas. This is VMworld 2017, and this is theCUBE. This is Dave Vellante with Peter Burris, and this is the second segment with Chad Sakac who's the president of Dell EMC. We're going to dig into what the cloud looks like in the next decade, you know, 2022 time frame. Chad, again, welcome back. Thanks for spending some time. >> It's great to be back. No one's got a crystal ball a decade out, but I think we've got a pretty good idea of what we think the next five years look like. >> Well, you know, we do like at Wikibon to look further out, and say okay, what are your assumptions about how the business is going to evolve, knowing that any kind of ten year forecast is going to be wrong. But it does shape your thinking and your assumptions. >> Yep. >> So what's your vision? What's Dell EMC's vision for how the cloud is going to evolve and shape, and look like in the next five years? >> I think the following things are a near certainty and they're driving our strategy. Basically customers will consume platforms. They will pick the best platform on a temporal basis and on a space basis. So time and space (chuckles) right? So I'll give you an example. Today if you said, "What is the best place and time "to do AI and machine learning "for work that is against data that is not in-house?" The answer would be Google cloud platform in a heartbeat. Their core capabilities as a platform around AI and machine learning are head and shoulders above everything else. Right? >> Yep. >> That's a platform that people consume. Likewise, if you said, "Okay, what's the platform that I use for my "applications that basically need a little more "traditional care and feeding around them?" That's going to be an evolution of the VMware stack that the customers are using today. It powers 80% of what they do today. It's the platform that runs the core of their business today, and that platform, as you can see this week, is expanding and expanding and expanding. Now what'll happen over the next decade is that platform will be independent of place. So if you imagine what we're going to do with that capability now, it's not an announcement, it's a platform that customers can buy around VMware cloud on AWS, you can see that we just broke the "Is something on or off?" is now not the question. The question is what's the right platform and services to use for a given set of workloads? >> I want to build on that for a second Chad, if I can. So the vision that I think you articulated the core experience is Look that what you love about the cloud is you love the get in small, grow fast, or grow according to the workload needs, >> Chad: Yep, elasticity. >> Yeah, don't lock in a whole bunch of financial assets. Lower assets, specificity, be able to apply it to a lot of different things. You love that. But the problem is, the physical, legal, and IP realities of your business dictate that you're not going to put it all in there. So the common experience is, get that dependent upon the workload, and have it all run simply in a straightforward manner that serves the business. Right? >> Bingo. So the word platform is independent of space. >> Right? >> Right. The other thing that I think we'll see over the next decade is that any technologies that bind multiple platforms together are incredibly compelling. And you can actually see this driving both the R and D strategy and the M and A strategy of the leaders, right? So let me give you an example of things that bind together platforms, and themselves are platforms. Cloud Foundry is one of the best binders and spanners that exists. Because people use Cloud Foundry on Azure, on AWS, on their own private cloud all day long. In fact, it won the award for basically, at Microsoft Ignite, for the most popular used thing on Azure outside Microsoft's own core services. So it's a binder. It gives customers mobility and flexibility across these different platforms. Another example, we're going all in on Kubernetes. We think that Kubernetes as the container abstraction that spans these different clouds is in essence, game over of chaos, and game beginning of standardization and movement forward. I'll give you another example. I think that ten years from now the debates that we're having around SDN today will be so over, and everyone will go, "Of course you're going to have a software-defined "network that abstracts," because networking is something that needs to span platforms. So, core idea number one, people will make platform choices and there will be multiple platforms. Those platforms will be independent of on off prem, independent of Capex, Opex choices. Those platforms will exist in all of those modes. >> But be tied to the characteristics, the benefits that they provide to workloads. >> Bingo. The library of connectors, of things that span and bind these platforms, will grow in value and importance to the customers. I'll give you another example of a binding thing that links together multiple platforms. And you can see its success even today. ServiceNow is the thing that binds and connects at the ITSM layer, all of these different topologies. So it's not just things that are all just in our family (chuckles) right? But you can see these ideas continuing to march forward. The thing that I think you'll also see is the explosion of the edge is going to create this whole world that is the opposite pendulum swing of centralization that you can kind of already see happening. The number of edge devices that will exist, the amount of data that they're going to need to process locally, and the amount of data that they're going to need to process that's centralized in one of these platforms is going to be immense. >> So the edge, does the edge create a new cloud? >> Yes. You know, people are already talking about that like it's the fog or whatever. Again, buzz words can sometimes make people underestimate very important things that are actually happening in an industry right now. The last thing I'll say is, and this is a dream and an aspiration, and a vision, but a dream and an aspiration. There are amazing problems to crack in the domain of security. And that itself needs to become a core platform element that transits all of these other platforms. >> Peter: That's a key binder. >> It's a binding element that has to transit all these different platforms that people consume. And I think you can see the edges of the industry, us tackling these problems in new ways, and I'm very hopeful about that actually. >> So the infrastructure requirements of that new cloud, customers have to make bets. We were talking about that earlier. There's new stack choices that are emerging. What's your point of view there and how does it all relate to bring it back to how you get from point A to point B? >> There's a great risk in saying stuff on camera Dave. (men chuckle) But you know-- >> Peter: But take the risk Chad. >> But to hell with it (laughs). See it here on theCUBE first. >> So look, I think that we're entering into an era of stack wars. And that sounds too militaristic. That's not what I mean. >> Peter: Let's call it stack competition. >> I think that what is happening is that the need for customers to choose platforms and make platform level bets in exchange for simplification and speed is basically forcing them, and it's forcing the market and everyone in it, including us, to think, what is our opinionated stack? That doesn't mean closed, right? However, even though there's open connections all over the place, increasingly you're seeing people take the Lego components and go (makes building sounds) This works with this, which works with this, which works with this, and they're built all together. And the thing that I'm finding, and I don't know whether you guys see this in your customer conversation. It's weird, people are schizophrenic. They're really worried about what that means for them on premises. Because they're used to hand-assembling everything under the sun, and then are frustrated it doesn't all work together easily, right? And yet, they have no issue at all about saying "I'm putting everything in, you know, "in Office 365." I was talking with a customer, with the procurement person, and you can imagine the procurement person's reaction when I say, "I think that the world is moving "towards vertically integrated stacks." And there is decidedly an open ecosystem, but also an opinionated, pivotal, VMware, Dell, EMC stack. A Dell technology stack. The procurement guy lost his mind. He did not like to hear that from me. >> Of course. >> He started to get angry. >> Well, would you rather have what occurred with the Dupe? >> Yeah, and-- (Dave laughs) >> Well, what he wants is he is being told, "You got to take "five points off of every transaction." >> Yeah, of course. >> And he wants to see all these transactions be distinct, and what you're saying, Chad, is that we're moving where the transactions start to accrete value, accrete strategic importance, >> Yep. >> and accrete risk. And the procurement guy's looking at that saying (makes terrified sound) But it requires hard core, realistic vendor management that's well-defined and treated by the business as an asset. >> I think that we're entering into an era of consolidation. Customers are going to have to make platform bets that are business bets. >> For themselves. >> That's right. >> So bring it back to a topic that is more 2017, hyperconverged infrastructure. >> Chad: Yep. >> Is that the model for the future cloud? Or does it need to go beyond that? Beyond the virtual machine parlance that we tend to talk about? >> So, we have years of experience working with customers, trying to build clouds out of traditional infrastructure stacks. >> Dave: Right. >> And we're there as their partner to make it work. It is freaking hard! Frankly, nearly impossible. And again, they talk to vendor after vendor who's like, "Buy our new cloud management platform "and we'll be able to automate all of your crapola." >> Buy our hammer, and we'll fix all your cloud nails. >> And the reality of it is that every layer that you build one of these stacks on, the more variation that you have at this layer, it complicates the life cycle management of this layer. And then the more variation you have at this layer, the more it complicates the life cycle management of this layer. And that's what I mean about the stackification where the stacks are starting to bowl together. Driven not by vendor, but driven by customer need for simplification and speed. >> Peter: And workload. >> They're just not consciously making the connection yet that says it's time for me to make strategic choices. Right? So hyperconverge infrastructure has proven an ability. It's no longer in weirdo VDI only use cases (laughs). It is now proving itself to be a material simplification at the bottom layer of the stack. And it's not rocket science. It is basically the same lesson that hyperscalers and SASS startups realized, which is that you need to have something which is much more industry standard, much more software defined, much more rigid in a sense about how it's constructed so you actually life cycle it and make the next stack up simpler. >> All right, so we got to wrap. Let me summarize what I heard, and maybe you guys can fill in any gaps. So platforms essentially be products is what I heard. Those are my words not yours. >> Totally, yeah. >> And platforms will be place-independent, and a key value creator will be this binding platforms together. >> Chad: Yes. >> Which is going to become very very compelling. You gave the example of Cloud Foundry, Kubo, Kubernetes. >> I'll give you one more, Boomi. >> Boomi, and even SDN which is basically a fait de complis >> Yeah. >> is essentially what you're saying. An explosion at the edge will create a new cloud. The infrastructure requirements are going to evolve to support that cloud. And security is going to be a core platform element, a key binder as you said. Anything I missed? >> And that literally, customers have to be as simple as they can. And what they need to accept, and make choices, I'm not forcing them down the path with us or whomever. They need to accept that simplicity and speed means choosing platforms and platform partners. >> So here's what I'd add. 'Cause I think you're right Chad. I would add just a couple of refinements, that the quality of the platform is going to be a function of how well it binds. >> Chad: Yep. >> And that that security becomes a crucial binder. And the other thing that I'd say is that the edge, it's not so much a new cloud. I hate the term fog. >> Yeah. >> Because if there's anywhere where business is going to need clarity, it's going to be at the edge. >> I totally agree. >> That's a vendor way of looking at things. The customer way is, "I need clarity here you guys. "Don't talk to me about cloud." In fact, we like to say that when Andreessen said, "Software's going to eat the world," the right way of saying it, "Software's going to eat the edge." >> Right. >> That the edge is going to make a lot more of these choices clear. >> And just, again, I know we got to go but, It always sounds like hyperbole. The amount of stuff that we're doing around trying to make the edge clear, like basically the EdgeX Foundry, which is basically trying to standardize this mess of proprietary protocols and devices. That stuff is happening like now. The Pulse IoT stuff that we talked about, that's happening now. But those are just in early, early days. If you look out over a few years, that stuff will be a new platform. >> That's absolutely right. >> Yeah. And Dell hasn't fully played it's edge card, I suspect. >> We will see more there. >> Yeah. >> All right, Chad, first of all, awesome content. Peter, thank you very much. Virtual Geek is Chad's blog. If you're into this stuff, go subscribe to that. It's a fantastic resource. >> Thanks man. >> So thanks again. Really appreciate it. >> My pleasure guys. >> All right, keep right there everybody. We'll be back with our next guest. This is theCUBE. We're live from VMworld 2017 from Las Vegas. We'll be right back. (electronic music)