(bright upbeat music) >> Welcome back to theCube's coverage of HPE's Green Lake announcement. We've been following the caves of Green Lake's announcement for several quarters now, and even years. And we're going to look at cloud adoption and frameworks to help facilitate cloud adoptions. You know, in 2020, the world was on a forced march to digital and there was a lot that they didn't know. Big part of that was how to automate, how to reduce your reliance on physically, manually and plugging things in. And so, customers need an adoption framework to better understand and how to de-risk that journey to the cloud. And with me to talk about that are Alexia Clements, who's the Vice President at Worldwide go to market for GreenLake cloud services at HPE and Alexei Gerasimov who's the vice president of Hybrid Cloud Delivery advisory and professional services at Hewlett Packard Enterprise. Folks, welcome to theCube. >> Alexia: Thanks so much for having us. >> You're very welcome. So, Alexei, what is a cloud adoption framework? How does that all work? >> Gerasimov: Yeah, thanks Dave. So the framework is a structured approach to elevate the conversation, to help our customers get outcomes. So we've been helping customers adopt the benefits in the most of IT for a decade. And we've noticed that they basically focus on eight key areas as they transform to cloud-like capabilities. It's a strategy and governance, it's innovation, people, a dev ops applications, operations security, and data. So we've structured our framework around those core components to help our customers get value. Because end of the day, it's all about changing the way they operate. To get the advantage of all of it. >> Yes. So you can't just pave the cow path and kind of plug your existing process. There's a lot that's unknown, as I said up front. So, so Alexia, maybe you could talk a little bit more about some of the real problems that you're solving with customers that you see in the field. >> Alexey: Yeah, absolutely. So most customers are going through some form of digital transformation and these transformations are difficult and they need a structured approach to help them through that journey. I kind of like to think of it as a recipe to make a meal. So you need to know what ingredients to buy and what are the steps to perform to make that meal. >> Okay. So when you talk to customers, what do you, what do you tell them? That's in it for them after the, after you've actually successfully helped them deploy? What are they telling you? >> Yeah, well, they're telling they now have reached their business outcomes and they're, you know, they're a more agile organization. >> What's the experience look like when you, when you go through one of these journeys and you, you apply the adoption framework, can you sort of paint a picture for us? >> Yeah, absolutely. So every customer is in some sort of transformation, like Alexia said, that transformation implies you've got to know where you start and again, know where you're going. So the experience traditionally is customers need to understand what are my current hybrid cloud capabilities? What do I have, what am I missing? What's lacking and then determine where do you want to go? And in order to get from point A to point B, they have to get a prescriptive approach. So the framework sort of breaks down their path from where they are to their desired maturity. And it takes them in the very prescriptive path to get there. >> So you start with an assessment, you do a gap analysis based on their skill sets. I presume you identify what's possible, help them understand, you know, best practice, which they may not achieve, but this is kind of their north star. Right? And then do you help? How do you help them fill those gaps? Because are skills gaps. Everybody talks about that today. You guys presumably can provide additional services to do that, but so can you add a little bit color to that scope? >> Yeah, yeah, absolutely. And so to your point, the first is a maturity level. So once you figure out the maturity level, you understand what needs to be done. So if you look at our domain, the eight domains that I mentioned and the framework, people is a big one, right? Most of the folks are struggling with people's skills and organizational capabilities. And it's so because it's an operating model change, right? And people are the key component to this operating model change. So we help our customers figure out how do we achieve that optimal operating level and operating a model maturity. And that could be on-prem that could be on public cloud. That could be hybrid. That could be at the edge. And yeah, we, if we can HP, the framework, by the way is pretty, pretty open and pretty objective. If we can help our customers address and achieve their sales gaps great. If we can not directly, then we can have a partner that can help them, you know, plug in something that we don't have. >> Are you finding that, that in terms of the maturity that most people have some kind of experience with, with cloud, but they're struggling to bring that cloud experience to their on-premise state. They don't want to just shove everything into the cloud. Right. So, what does that kind of typical journey look like for folks? I know there's--it's a wide spectrum, or you've got people that are maybe more mature. Maybe some of the folks in financial services got more resources, but can you sort of give us a sense as to what the typical, the average. >> Oh yeah yeah yeah, absolutely. By the way. So that give you a customer example, perfect example of a large North American integrated energy company. They decided to go cloud fresh, like a lot of companies. that wants to do cloud first. And why? The reason was agility. So they started going to the cloud and they realized in order to get agility, you can't just go to you, pick your public CSP, you got to change the way to operate. So they brought us in and they asked, could you help me figure out how we can change the organization? So we actually operate on the proper level of maturity. So we brought our team in. We help them figure out what do we need to look at? We need to look at operations. We need to look at people. We need to look at applications, and we need to figure out what gives you the best value. So when all said and done, they realized that their initial desire of, you know, public first or cloud first, wasn't really public cloud first. It's a way to operate. So now the customer is in three different public CSPs. They're on-prem, there are at edge and everywhere. So that's the focus. Yeah. >> Is the scope predominantly the technical organization. How deep does it go into the, to the business? Is it obviously the application development team is involved, but how deep into the business does this go? The framework. >> Right, and it's absolutely not a technology focused, the whole concept areas, it's outcomes based, and it's a results based. So if you look at the framework, there's really not a single element of the framework that says tech, like storage or compute. No, it's its people, its data, it's business value, strategy and governance, because the goal for us is being objective is we're just trying to help them address the outcomes. Not necessarily to give them more tech. >> So Alexia, I like that answer because it's a wider scope as, I mean, if we just focused on the tech and that's the swim lane, it'd be a lot easier. But as we all know, it's the people in the process that are really the hard part. So that, that makes the challenge for customers greater. You're hurting more cats. So what are the, some of the obstacles that potentially you help customers before they dive in understand. >> Yeah. So we're giving them a roadmap on where they need to go. So we're like I mentioned that recipe, so we're really trying to identify what is their strategy and where do they, what are the outcomes that they're trying to drive and help them on a street, you know, with that path to meet those outcomes. So some of those, I mean, every customer's a little bit different. I mean, we had one customer, which was a, one of the largest hospitals in north America and they, they would needed to, they wanted to go to the cloud, but they realized they couldn't put all of their patient data on the cloud. So what we did was we helped them in changing their operating model and really look to see how does that, how do they need to what's that end game for them, and actually help redo their operating model to have some in the cloud and some on-prem and, and really identify, you know, where they needed to go for their roadmap. So that was an obstacle that they had, hey, we can't put all this stuff out there. How does that now need to work in this new world? >> I would think the data model is a big deal here. I mean, you just gave an example where there's a, there's a, there's a governance and compliance aspect to it. So thinking about that example, did they have to change the way in which they provided federated governance was that presumably identify whose whose responsibility that was to adjudicate, but also had to get the, the implementers to follow that's the, how does that all work? Is it just the deep conversations? And then you figure out how to codify it or. >> No. So what so we have, so through those eight domains that Alexia mentioned, we go through, step-by-step how they need to think about it. And within mind, what are their business outcomes and goals that they're trying to achieve? So really identifying how they need to change that operating model to meet those business outcomes. >> So what's the output, it's a plan, right. That's tailored to the customer. Is that, is that correct? And, and then sort of assistance in implementing downstream or what do they get? >> Yeah, yeah, absolutely. Just to piggyback to what Alexia said, the alignment, the early alignment, the strategy and governance, as you mentioned, this is probably the most important thing, because everybody says we want to be cloud first, but what does that mean? Cloud first means different things to everyone. So we said, give him a plan. The first we'll help with figure out is what does that mean for you? Because at the end of the day, you're not going to the cloud for the sake of cloud, or anywhere you go into the cloud to get some sort of value. So what's that alignment. So the plan is supposed to help you on your road to that value, right? So we'll help them figure out what I want to do, why, for what purpose, what's going to actually address my business value. So yes, they will get a plan as part of it. But more importantly, they get, they get a set of activities, communication plans, which by the way, another block that you got to address. >> Dave: Huge. >> Yeah. >> Yeah. I mean, a lot of executives tell me, look, if you don't change your operating model and go to the cloud, yeah. You're talking, you know, nickels and dimes. If you want to get telephone numbers, you know, big companies, you want to get into bees with billions, you have to change the operating model. And the problem that they tell me is a lot of times the corner offices, okay, we're doing this, but everybody in the fat middle says, what are we doing? >> Right. And now more than ever, I mean, customers need to look at that model like a more modern operating model to realize the benefits of cloud capabilities, whether that be at the edge, their data centers, their colos cloud. So they really need to look at that. And what we've seen is with our framework, we're really helping customers accelerate their business outcomes. De-risk their transformation, and really optimize that cloud operating model. >> It's that alignment you reducing friction within the organization, confusing confusion. When people don't know which direction they're going, they'll just going to go wherever they're pointed. Right. Right. >> And you back to the alignment. So you've got alignment and you mentioned communication. You have to communicate up and down and left and right across the organization because that's one of the most probably ignored elements of any transformation lots of people don't know. So you got to communicate. And then you have to actually measure and report on how they, you know, how the transformation is happening. So we can help in all three of those. >> Especially when everybody's remote. Yeah. Right. And then I said, hey, these digital transformations, there's so much, that's unknown. >> Alexia: Right. It's difficult. >> It's a lot of new. And so you also have to, I presume part of the plan is, Hey, you're not, it's not going to be a hundred percent perfect. So you have to have. >> Alexia: Right. And you're constantly iterating on that plan. >> What does this have to do with GreenLake? >> Alexia: Yeah. So, I mean, GreenLake is HPE's you know, cloud everywhere. And what we're really doing is this framework is helping customers with that path to get that cloud-like experience and as a service model. And so the framework is really helping clients understand where do they need to go and what GreenLake solutions can help them get there. >> So the fundamental assumption of not every cloud player necessarily bad, I would say most hyperscalers is, hey, ultimately, all the data and the workloads are going to go to the cloud, that's their operating premise. So they all have an operating framework to facilitate that. >> Alexia: Right. >> It's, it's tongue in cheek, but it's true. So, but everybody has one of these. How was yours different? >> Yeah. So like, like you said, there's lots of different, you know, frameworks out there, but what we're really focused on is meeting those business goals and outcomes for clients. So we didn't focus on the technology. Like we mentioned what we were really focusing around. I mean, we kind of learned early on that every customer has technical capabilities, applications, data in multiple clouds, on-prem in colos and at the edge. So we didn't focus on like just the technology. So it's really driving business outcomes and their goals and, and the tech, all those frameworks that we just mentioned, they're really specifically driving a particular technology tool or vendor implementing a particular technology or vendor. >> So we've talked about outcomes a lot, but I wonder if we could peel the onion on that. So, you know, the highest level outcome is I want to increase revenue, cut costs, drop to the bottom line, increase shareholder value, improve employee experiences and retention, make customers happier, grow my business. I mean, those are, I mean, I, I don't know a lot of businesses that don't... >> Alexia: Right. >> want to do that, So. Okay. That's cool. But then I'm imagining you really start to peel the layers and say, okay, this is how we're going to get there. And you get down to specific objectives as to the, how is that sort of how this works? >> Right, and that's due to echo at Alexia. So that's exactly why ours is different. We're not focusing on how to adopt Microsoft or AWS or Alibaba with focusing on how we can deliver the customer experience or a better revenue, you know, or, you know, increase the value for the consumer for whatever the company will help him. So the framework we'll look at that and figure out how do we actually address it, whether it's on public cloud, whether it's on prem, whether it's at the edge. >> You mentioned Alexia, that something, hey, if we don't have the skills, we can get a partner who does, a big company. You got a huge partner network. So for example, if you might not have necessarily a deep industry expertise, that's where you might lean on a partner or is that, is that a good example or is there a better one? >> Yes and we know. We're not going to just like you mentioned AWS or Microsoft, Alibaba thing that everything will go to public cloud. I don't believe so, but at the same time we know not everything will stay on-prem. So the combination of on-prem, the edge, you know, private cloud and public cloud is what the customers are after. So our partners could be either third party, system integrator that can help us implement something or even the public CSPs, because we know our customers have capabilities everywhere. So the question becomes, how can we holistically address their needs, whether it's on-prem, whether it's in public cloud. >> Great. Guys, thanks so much. >> Alexia: Thank you. Thanks for having us. Appreciate it. >> My pleasure and thank you for watching everybody's as theCube's continuous coverage of HPE's GreenLake announcement, keep it right there for more great content. (bright upbeat music)

(bright upbeat music) >> Okay, we're back with theCUBE's continuous coverage of HPE's latest GreenLake announcement, the continuous cadence that we're seeing here. You know, when you're trying to figure out how to optimize workloads, it's getting more and more complex. Data-driven workloads are coming in to the scene, and so how do you know, with confidence, how to configure your systems, keep your costs down, and get the best performance and value for that? So we're going to talk about that. With me are Chris Shin, who is the founder of CloudPhysics and the senior director of HPE CloudPhysics, and Sandeep Singh, who's the vice-president of Storage Marketing. Gents, great to see you. Welcome. >> Dave, it's a pleasure to be here. >> So let's talk about the problem first, Sandeep, if we could. what are you guys trying to solve? What are you hearing from customers when they talk to you about their workloads and optimizing their workloads? >> Yeah, Dave, that's a great question. Overall, what customers are asking for is just to simplify their world. They want to be able to go faster. A lot of business is asking IT, let's go faster. One of the things that cloud got right is that overall cloud operational experience, that's bringing agility to organizations. We've been on this journey of bringing this cloud operational agility to customers for their data states, especially with HPE GreenLake Edge-to-Cloud platform. >> Dave: Right. >> And we're doing that with, you know, powering that with data-driven intelligence. Across the board, we've been transforming that operational support experience with HPE InfoSight. And what's incredibly exciting is now we're talking about how we can transform that experience in that upfront IT procurement portion of the process. You asked me what are customers asking about in terms of how to optimize those workloads. And when you think about when customers are purchasing infrastructure to support their app workloads, today it's still in the dark ages. They're operating on heuristics, or a gut feel. The data-driven insights are just missing. And with this incredible complexity across the full stack, how do you figure out where should I be placing my apps, whether on Prim or in the public cloud, and/or what's the right size infrastructure built upon what's actually being consumed in terms of resource utilization across the board. That's where we see a tremendous opportunity to continue to transform the experience for customers now with data-driven insights for smarter IT decisions. >> You know, Chris, Sandeep's right. It's like, it's like tribal knowledge. Well, Kenny would know how to do that, but Kenny doesn't work here anymore. So you've announced CloudPhysics. Tell us more about what that is, what impact it's going to have for customers. >> Sure. So just as Sandeep said, basically the problem that exists in IT today is you've got a bunch of customers that are getting overwhelmed with more and more options to solve their business problems. They're looking at cloud options, they're looking at new technologies, they're looking at new sub-technologies and the level at which people are competing for infrastructure sales is down at the very, very, you know, splitting hairs level in terms of features. And they don't know how much of these they need to acquire. Then on the other side, you've got partners and vendors who are trying to package up solutions and products to serve these people's needs. And while the IT industry has, for decades, done a good job of automating problems out of other technology spaces, hasn't done a good job of automating their own problems in terms of what does this customer need? How do I best service them? So you've got an unsatisfied customer and an inadequately equipped partner. CloudPhysics brings those two together in a common data platform, so that both those customers and their partners can look at the same set of data that came out of their data center and pick the solutions that will solve their problems most efficiently. >> So talk more about the partner angle, because it sounds like, you know, if they don't have a Kenny, they really need some help, and it's got to be repeatable. It's got to be consistent. So how have partners reacting to this? >> Very, very strongly. Over the course of the four or five years that that CloudPhysics has been doing this in market, we've had thousands and thousands of VARs, SIs and others, as well as many of the biggest technology providers in the market today, use CloudPhysics to help speed up the sales process, but also create better and more satisfied customers. >> So you guys made... Oh, go ahead, please. >> Well, I was just going to chime into that. When you think about partners that with HPE CloudPhysics, where it supports heterogeneous data center environments, partners all of a sudden get this opportunity to be much more strategic to their customers. They're operating on real world insights that are specific to that customer's environment. So now they can really have a tailored conversation as well as offer tailored solutions designed specifically for the areas, you know, where help is needed. >> Well, I think it builds an affinity with the customer as well, because if the partners that trust advisor, if you give a customer some advice and it's kind of the wrong advice, "Hey, we got to go back and reconfigure that workload. We won't charge you that much for it". You're now paying twice. Like when an accountant makes a mistake on your tax return, you got to pay for that again. But so, you guys acquired CloudPhysics in February of this year. What can you tell us about what's transpired since then? How many engagements that you've done? What kind of metrics can you share? >> Yeah. Chris, do you want to weigh in for that? >> Sure, sure. The start of it really has been to create a bunch of customized analytics on the CloudPhysics platform to target specific sales motions that are relevant to HPE partners. So what do I mean by that? You'll remember that in May, we announced the Alletra Series 6,000 and 9,000. In tandem with that, CloudPhysics released a new set of analytics that help someone who's interested in those technologies figure out what model might be best for them and how much firepower they would need from one or the other of those solutions. Similarly, we have a bunch solutions and a market strength in the HCI world, hyper converged, and that's both SimpliVity and dHCI. And we've set up some analytics that specifically help someone who's interested in that form factor to accelerate, and again, pick the right solutions that will serve their exact applications needs. >> When you talk to customers, are they able to give you a sense as to the cost impacts? I mean, even if it's subjective, "Hey, we think we, you know, we save 10% versus the way we used to do it", or more or less. I mean, just even gut feel metrics. >> So I'll start that one, Sandeep. So there's sort of two ways to look at it. One thing is, because we know everything that's currently running in the data center - we discovered that - we have a pretty good cost of what it is costing them today to run their workloads. So anything that we compare that to, whether it's a transition to public cloud or a transition to a hosted VMware solution, or a set of new infrastructure, we can compare their current costs to the specific solutions that are available to them. But on the more practical side of things, oftentimes customers know intuitively this is a set of servers I bought four years ago, or this is an old array that I know is loose. It's not keeping up anymore. So they typically have some fairly specific places to start, which gives that partner a quick win, solving a specific customer problem. And then it can often boil out into the rest of the data center, and continual optimization can occur. >> How unique is this? I mean, is it, you know, can you give us a little glimpse of the secret sauce behind it? Is this kind of table stakes for the industry? >> Yeah. I mean, look, it's unique in the sense that CloudPhysics brings along over 200 metrics across the spectrum of virtual machines and guest OSs, as well as the overall CPU and RAM utilization, overall infrastructure analysis, and built in cloud simulators. So what customers are able to do is basically, in real time, be able to: A - be aware of exactly what their environment looks like; B - be able to simulate if they were going to move and give an application workload to the cloud; C - they're able to just right-size the underlying infrastructure across the board. Chris? >> Well, I was going to say, yeah, along the same lines, there have been similar technology approaches to different problems. Most notably in the current HPE portfolio, InfoSight. Best in class, data lake driven, very highly analytical machine learning, geared predominantly toward an optimization model, right? CloudPhysics is earlier in the talk track with the customer. We're going to analyze your environment where HPE may not even have a footprint today. And then we're going to give you ideas of what products might help you based on very similar techniques, but approaching a very different problem. >> So you've got data, you've got experience, you know what best practice looks like. You get a sense as to the envelope as to what's achievable, right? And that is just going to get better and better and better over time. One of the things that that I've said, and we've said on theCUBE, is that the definition of cloud is changing. It's expanding, it's not just public cloud anymore. It's a remote set of services, it's coming on Prim, there's a hybrid connection. We're going across clouds, we're going out to the edge. So can CloudPhysics help with that complexity? >> Yeah, absolutely. So we have a set of analytics in the cloud world that range from we're going to price your on-premise IT. We also have the ability to simulate a transition, a set of workloads to AWS, Azure, or Google Cloud. We also have the ability to translate to VMware based solutions on many of those public clouds. And we're increasingly spreading our umbrella over GreenLake as well, and showing the optimization opportunities for a GreenLake solution when contrasted with some of those other clouds. So there's not a lot of... >> So it's not static. >> It's not static at all. And Dave, you were mentioning earlier in terms such as proven. CloudPhysics now has operated on trillions of data points over millions of virtual machines across thousands of overall data assessments. So there's a lot of proven learnings through that as well as actual optimizations that customers have benefited from. >> Yes. I mean, there's benchmarks, but it's more than that because benchmarks tend to be static, okay. We consider rules of thumb. We're living in an age with a lot more data, a lot more machine intelligence. And so this is organic, it'll evolve. >> Sandeep: Absolutely. >> And the partners who work with their customers on a regular basis over at CloudPhysics, and then build up a history over time of what's changing in their data center can even provide better service. They can look back over a year, if we've been collecting, and they can see what the operating system landscape has changed, how different workloads have lost popularity, how other ones have gained. And they really can become a much better solution provider to that customer the longer CloudPhysics is used. >> Yeah, it gives your partners a competitive advantage, it's a much stickier model because the customer is going to trust your partner more if they get it right. So we're not going to change horses in the middle of the street. We're going to go back to the partner that set us up, and they keep getting better and better and better each time, we've got a good cadence going. All right. Sandeep, bring us home. What's your sort of summary? How should we think about this going forward? >> Well, I'll bring us right back to the way I started is, and to end, we're looking at how we continue to deliver best in class cloud operational experience for customers across the board with HPE GreenLake. And earlier this year, we unveiled this cloud operation experience for data, and for customers, that experience starts with a cloud consult where they can essentially discover services, consume services, that overall operational and support experience is transformed with HPE InfoSight. And now we're transforming this experience where any organization out there that's looking to get data-driven insights into what should they do next? Where should they place their workloads? How to right-size the infrastructure? And in the process, be able to transform how they are working and collaborating with their partners. They're able to do that now with HPE CloudPhysics, bringing these data driven insights for smarter IT decision-making. >> I like this a lot, because a lot of the cloud is trial and error. And when you try and you make a mistake, you're paying each time. So this is a great innovation to really help clients focus on the things that matter, you know, helping them apply technology to solve their business problems. Guys, thanks so much for coming to theCUBE. Appreciate it. >> Dave, always a pleasure. >> Thanks very much for having us. >> And keep it right there. We got more content from HPE's GreenLake announcements. Look for the cadence. One of the hallmarks of cloud is the cadence of announcements. We're seeing HPE on a regular basis, push out new innovations. Keep it right there for more. (bright upbeat music begins) (bright upbeat music ends)

(electronic music) >> Narrator: From the SiliconANGLE Media office in Boston Massachusetts, it's the CUBE. Now here are your hosts, Dave Vellante and Stu Miniman. >> Cloud is dead, it's all going to the edge. Or is it? Hi everybody, this is Dave Vellante and I'm here with Stu Miniman. Stu, where does this come from, this narrative that the cloud is over? >> Well Dave, you know, clouds had a good run, right? It's been over a decade. You know, Amazon's dominance in the marketplace but Peter Levine from Andreessen Horowitz did an article where he said, cloud is dead, the edge is killing the dead. The Edge is killing the cloud and really we're talking about IoT and IoT's huge opportunity. Wikibon, Dave we've been tracking for many years. We did you know the original forecast for the Industrial Internet and obviously there's going to be lots more devices at the edge so huge opportunity, huge growth, intelligence all over the place. But in our viewpoint Dave, it doesn't mean that cloud goes away. You know, we've been talking about distributed architectures now for a long time. The cloud is really at the core of this building services that surround the globe, live in just hundreds of places for all these companies so it's nuanced. And just as the cloud didn't overnight kill the data center and lots of discussion as to what lives in the data center, the edge does not kill the cloud and it's really, we're seeing some major transitions pull and push from some of these technologies. A lot of challenges and lots to dig into. >> So I've read Peter Levine's piece, I thought was very thought-provoking and quite well done. And of course, he's coming at that from the standpoint of a venture capitalist, all right. Do I want to start you know, do I want to pour money into the trend that is now the mainstream? Or do I want to get ahead of it? So I think that's what that was all about but here's my question Stu is, in your opinion will the activity that occurs at the edge, will it actually drive more demand from the cloud? So today we're seeing the infrastructure, the service business is growing at what? Thirty five percent? Forty percent? >> Sure, sure. Amazon's growing at the you know, 35 to 40 percent. Google, Microsoft are growing double that right now but overall you're right. >> Yeah, okay and so, and then of course the enterprise players are flat if they're lucky. So my question is will the edge actually be a tailwind for the cloud, in your opinion? >> Yeah, so first on your comment there from an investment standpoint, totally can understand why edge is greenfield opportunity. Lots of different places that I can place bets and probably can win as opposed to if I think that today I'm going to compete against the hyperscale cloud guys. You know, they're pouring 10 billion dollars a year into their infrastructure. They have huge massive employment so the bar to entry is a lot higher. I'm sorry, the second piece was? >> So will the edge drive more demand for the cloud? >> Yeah, absolutely. I think it does Dave because you know, let's take something like autonomous vehicles. Something that we talk about. I need intelligence of the edge. I can't wait for some instruction to go back to the cloud before my Tesla plows into an individual. I need to know that it's there but the models themselves, really I've got all the compute in the cloud. This is where I'm going to train all of my models but I need to be able to update and push those to the edge. If I think about a lot of the industrial applications. Flying a plane is, you know, things need to happen locally but all the anomalies and new things that we run into there's certain pieces that need to be updated to the cloud. So you know, it's kind of a multi-layer. If we look at how much data will there be at the edge, well there's probably going to be more data at the edge than there will be in the central cloud. But how much activity, how much compute do I need, how much things do I need to actually work on. The cloud is probably going to be that central computer still and it's not just a computer, as I said, a distributed architecture. That's where, you know. When we've looked at big data in the early days Dave, when we can put those data lines in the cloud. I've got thousands or millions of compute cycles that I can throw at this at such a lower price and use that there as opposed to at the edge especially. What kind of connectivity do I have? Am i isolated from those other pieces? If you go back to my premise of we're building distributed architectures, the edge is still very early. How do I make sure I secure that? Do I have the network? There's lots of things that I'm going to build in a tiny little component and have that be there. And there's lots of hardware innovation going on at that edge too. >> Okay, so let's talk about how this plays out a little bit and you're talking about a distributed model and it's really to me a distributed data model. The research analysts at Wikibon have envisioned this three-tier data model where you've got data at the edge, which you may or may not persist. You've got some kind of consolidation or aggregation layer where it's you know, it's kind of between the edge and the deep data center and then you've got the cloud. Now that cloud can be an on-prem cloud or it could be the public cloud. So that data model, how do you see that playing out with regard to the adoption of cloud, the morphing of cloud and the edge and the traditional data center? >> Yeah we've been talking about intelligent devices at the edge for a couple decades now. I mean, I remember I built a house in like 1999 and the smart home was already something that people were talking about then. Today, great, I've got you know. I've got my Nest if I have, I probably have smart assistants. There's a lot of things I love-- >> Alexa. >> Saw on Twitter today, somebody's talking like I'm waiting for my light bulbs to update their firmware from the latest push so, some of its coming but it's just this slow gradual adoption. So there's the consumer piece and then there's the business aspect. So, you know, we are still really really early in some of these exciting edge uses. Talk about the enterprise. They're all working on their strategy for how devices and how they're going to work through IoT but you know this is not something that's going to happen overnight. It's they're figuring out their partnerships, they're figuring out where they work, and that three-tiered model that you talked about. My cloud provider, absolutely hugely important for how I do that and I really see it Dave, not as an or but it's an and. So I need to understand where I collect my data, where it's at certain aspects are going to live, and the public cloud players are spending a lot of time working on on that intelligence, the intelligence layer. >> And Stu, I should mention, so far we're talking about really, the infrastructure as a service layer comprises database and middleware. We haven't really addressed the the SAS space and we're not going to go deep into that but just to say. I mean look, packaged software as we knew it is dead, right? SAS is where all the action is. It's the highest growth area, it's the highest value area, so we'll cover that in another segment. So we're really talking about that, the stack up to the middleware, the database, and obviously the infrastructures as a service. So when you think about the players here, let's start with AWS. You've been to I think, every AWS re:Invent maybe, with the exception of one. You've seen the evolution. I was just down in D.C. the other day and they have this chart on the wall, which is their releases, their functional releases by year. It's just, it's overwhelming what they've done. So they're obviously the leader. I saw a recent Gartner Magic Quadrant. It looked like, I tweeted it, it looked like Ronnie Turcotte looking back on Secretariat from the Belmont and whatever it was. 1978, I think it was. (laughs) 31 lengths. I mean, massive domination in the infrastructure as a service space. What do you see going on? >> Yeah so, Dave, absolutely. Today the cloud is, it's Amazon's market out there. Interestingly if you say, okay what's some of the biggest threats in the infrastructure as a service? Well, maybe China, Dave. You know, Alibaba was one that you look at there. But huge opportunity for what's happened at the edge. If you talk about intelligence, you talk about AI, talk about machine learning. Google is actually the company that most people will talk about it, can kind of have a leadership. Heck, I've even seen discussion that maybe we need antitrust to look at Google because they're going to lock things up. You know, they have Android, they have Google Home, they have all these various pieces. But we know Dave, they are far behind Amazon in the public cloud market and Amazon has done a lot, especially over the last two years. You're right, I've been to every Amazon re:Invent except for the first one and the last two years, really seen a maturation of that growth. Not just you know, devices and partnerships there but how do they bring their intelligence and push that out to the edge so things like their serverless technology, which is Lambda. They have Lambda Greengrass that can put to the edge. The serverless is pervading all of their solutions. They've got like the Aurora database-- >> And serverless is profound, not just that from the standpoint of application development but just an entire new business model is emerging on top of serverless and Lambda really started all that but but carry on. >> Yeah and when you look in and you say okay, what better use case than IoT for, well I need infrastructure but I only need it when I need it and I want to call it for when it's there. So that kind of model where I should be able to build by the microsecond and only use what I need. That's something that Amazon is at the forefront, clear leadership position there and they should be able to plug in and if they can extend that out to the edge, starting new partnerships. Like the VMware partnerships, interesting. Red Hat's another partnership they have with OpenShift to be able to get that out to more environments and Amazon has a tremendous ecosystem out there and absolutely is on their radar as to how their-- >> They're crushing it So we were at Google Next last year. Big push, verbally anyway, to the enterprise. They've been making some progress, they're hiring a lot of people out of formerly Cisco, EMC, folks that understand the enterprise but beyond sort of the AI and sort of data analytics, what kind of progress has Google made relative to the leader? >> So in general, enterprise infrastructure service, they haven't made as much progress as most of us watching would expect them to make. But Dave, you mentioned something, data. I mean, at the center of everything we're talking about is the data. So in some ways is Google you know, come on Google, they're smarter than the rest of us. They're skating to where the puck is Dave and infrastructure services, last decades argument if it's the data and the intelligence, Google's got just brilliant people. They're working at the some of these amazing environments. You look at things like Google's Spanner. This is distributed architecture. Say how do I plug in all of these devices and help the work in a distributed gradual work well. You know, heck, I'd be reading the whitepapers that Google's doing in understanding that they might be really well positioned in this 3D chess match that were playing. >> Your eyes might bleed. (laughs) I've read the Google Spanner, I was very excited about it. Understood, you know, a little bit of it. Okay, let's talk about Microsoft. They're really of the big cloud guys. They're really the one that has a partnership strategy to do both on-prem and public cloud. What are your thoughts on that now that sort of Azure stack is starting to roll out with some key partners? >> Yeah absolutely, it's the one that you know. Dave, if you use your analogy looking back, it's like well the next one, it's gaining a little bit, gaining a little bit but still far back. There is Microsoft. Where Microsoft has done best of course is their portfolio of business applications that they have. That they've really turned the green light on for enterprises to adopt SAS with Office 365. Azure stack, it's early days still but companies that use Microsoft, they trust Microsoft. Microsoft's done phenomenal working with developers over the last couple of years. Very prominent like the Kubernetes shows that I've been attending recently. They've absolutely got a play for serverless that we were talking about. I'm not as up to speed as to where Microsoft sits for kind of the IoT edge discussions. >> But you know they're playing there. >> Yeah, absolutely. I mean, Microsoft does identity better than anyone. Active Directory is still the standard in enterprises today. So you know, I worry that Microsoft could be caught in the middle. If Google's making the play for what's next, Microsoft is still chasing a little bit what Amazon's already winning. >> Okay and then we don't have enough time to really talk about China, you mentioned it before. Alibaba's you know, legit. Tencent, Baidu obviously with their captive market in China, they're going to do a lot of business and they're going to move a lot of compute and storage and networking but maybe address that in another segment. I want to talk about the traditional enterprise players. Dell EMC, IBM, HPE, Cisco, where do they stand? We talk a lot at Wikibond about true private cloud. The notion that you can't just stick all your data into the public cloud. Andy Jassy may disagree with that but there are practical realities and certainly when you talk to CIOs they they underscore that. But that notion of true private cloud hasn't allowed these companies to really grow. Now of course IBM and Oracle, I didn't mention Oracle, have a different strategy and Oracle's strategy is even more different. So let's sort of run through them. Let's take the arms dealers. Dell EMC, HPE, Cisco, maybe you put Lenovo in there. What's their cloud strategy? >> Well first of all Dave I think most of them, they went through a number of bumps along the road trying to figure out what their cloud strategy is. Most of them, especially let's take, if you take the compute or server side of the business, they are suppliers to all the service providers trying to get into the hyperscalers. Most of them have, they all have some partnership with Microsoft. There's a Assure stack and they're saying, okay hey, if I want an HPE server in my own data center and in Azure, Microsoft's going to be happy to provide that for you. But David, it's not really competing against infrastructure as a service and the bigger question is as that market has kind of flattened out and we kind of understand it, where is the opportunity for them in IoT. We saw, you know Dave. Last five years or so, can I have a consumer business and an enterprise business in the same? HPE tore those two apart. Michael Dell has kept them together. IBM spun off to Lenovo everything that was on the more consumer side of the business. Where will they play or will companies like Google, like Apple, the ones that you know, Dave. They are spending huge amounts of money in chips. Look at Google and what they're doing with TP use. Look at Apple, I believe it was, there was an Israeli company that they bought and they're making chips there. There's a different need at the edge and sure, company like Dell can create that but will they have the margin, will they have the software, will they have the ecosystem to be able to compete there? Cisco, I haven't seen on the compute side, them going down that path but I was at Cisco Live and a big talk there. I really like the opening keynote and we had a sit down on the CUBE with the executive, it said really if I look out to like 2030. If Cisco still successful and we're thinking about them, we don't think of them as a network company anymore. They are a software company and therefore, things like collaboration, things like how it's kind of a new version of networking that's not on ports and boxes. But really as I think about my data, think about my privacy and security, Cisco absolutely has a play there. They've done some very large acquisitions in that space and they've got some deep expertise there. >> But again, Dell, HPE, Cisco, predominantly arms dealers. Obviously don't have, HPE at one point had a public cloud, they've pulled back. HP's cloud play really is cloud technology partners that they acquire. That at least gives them a revenue stream into the cloud. Now maybe-- >> But it's a consultancy. >> It's a consultancy, maybe it's a one-way trip to the cloud but I will say this about CTP. What it does is it gives HPE a footprint in that business and to the extent that they're a trusted service provider for companies trying to move into the cloud. They can maybe be in the catbird seat for the on-prem business but again, largely an arms dealer. it's going to be a lower margin business certainly than IBM and Oracle, which have applications. They own their own public cloud with the Oracle public cloud and IBM cloud, formerly SoftLayer, which was a two billion dollar acquisition several years ago. So those companies from a participation standpoint, even a tiny market share is compared to Amazon, Google, and Microsoft. They're at least in that cloud game and they're somewhat insulated from that disruption because of their software business and their large install base. Okay, I want to sort of end with, sort of where we started. You know, the Peter Levine comment, cloud is dead, it's all going to the edge. I actually think the cloud era, it's kind of, it's here, we're kind of. It's kind of playing out as many of us had expected over the last five years. You know what blew me away? Is Alexa, who would have thought that Amazon would be a leader in this sort of natural language processing marketplace, right? You would have thought it would come from, certainly Google with all the the search capability. You would have thought Apple with Siri, you know compared to Alexa. So my point is Amazon is able to do that because it's got a data model. It's a data company, all these companies, including Apple, Google, Microsoft, Amazon, Facebook. The largest market cap companies in the world, they have data at the core. Data is foundational for those companies and that's why they are in such a good position to disrupt. So cloud, SAS, mobile, social, big data, to me still these are kind of the last 10 years. The next 10 years are going to be about AI, machine intelligence, deep learning, machine learning, cognitive. We're trying to even get the names right but it starts with the data. So let me put forth the premise and get your commentary. and tie it back in the cloud. So the innovation, in the next 10 years is going to come from data and to the extent that your data is not in silos, you're going to be in a much better position than if it is. Number two is your application of artificial intelligence, you know whatever term you want to use, machine intelligence, etc. Data plus AI, plus I'll bring it back to cloud, cloud economics. If you don't have those cloud economics then you're going to be at a disadvantage of innovation. So let's talk about what we mean by cloud economics. You're talking about the API economy, talking about global scale, always on. Very importantly something we've talked about for years, virtually zero marginal costs at volume, which you're never going to get on-prem because this creates a network effect. And the other thing it does from an innovation context, it attracts startups. Or startups saying, hey I want to build on-prem. No, they don't want to build in the cloud. So it's data plus artificial intelligence plus cloud economics that's going to drive innovation in the next ten years. What are your thoughts? >> Yeah Dave, absolutely. Something I've been saying for the last couple of years, we watched kind of the the customer flywheel that the public clouds have. Data is that next flywheel so companies that can capture that. You mentioned Amazon and Alexa, one of the reasons that Amazon can basically sell that as a loss is lots of those people, they're all Amazon Prime customers and they're ordering more things from Amazon and they're getting so much data that drive all of those other services. Where is Amazon going to threaten in the future? Everywhere. It is basically what they see. The thing we didn't discuss there Dave, you know I love your premise there, is it's technology plus people. What's going to happen with jobs? You and I did the sessions with Andy McAfee and Eril Brynjolfsson, it's racing with the machine. Where is, we know that people plus machines always beat so we spent the last five years talking about data scientist, the growth of developers and developers and the new king makers. So you know what are those new jobs, what are those new roles that are going to help build the solutions where people plus machine will win and what does that kind of next generation of workforce going to look like? >> Well I want to add to that Stu, I'm glad you brought that up. So a friend of mine David Michelle is just about to publish a new book called Seeing Digital. And in that book, I got an advance copy, in there he talks about companies that have data at their core and with human expertise around the data but if you think about the vast majority of companies, it's human expertise and the data is kind of bolted on. And the data lives in silos. Those companies are in a much more vulnerable position in terms of being disrupted, than the ones that have a data model that everybody has access to with human expertise around it. And so when you think about digital disruption, no industry is safe in my opinion, and every industry has kind of its unique attributes. You know, obviously publishing and books and music have disrupted very quickly. Insurance hasn't been disrupted, banking hasn't been disrupted, although blockchain it's probably going to affect that. So again, coming back to this tail-end premise is the next 10 years is going to be about that digital disruption. And it's real, it's not just a bunch of buzzwords, a cloud is obviously a key component, if not the key component of the underlying infrastructure with a lot of activity in terms of business models being built on top. All right Stu, thank you for your perspectives. Thanks for covering this. We will be looking for this video, the outputs, the clips from that. Thanks for watching everybody. This is Dave Vellante with Stu Miniman, we'll see you next time. (electronic music)

>> They are disruptive technologies. And if you think about the disruption that's happening in business, with IoT, with OT, and with big data, you can't get anything more disruptive to the whole of the business chain as this particular area. It's an area that I focused on myself, asking the question, should everything go to the cloud? Is that the new future? Is 90% of the computing going to go to the cloud with just little mobile devices right on the edge? Felt wrong when I did the math on it, I did some examples of real-world environments, wind farms, et cetera, it clearly was not the right answer, things need to be near the edge. And I think one of the areas to me that solidified it was when you looked at an area like video. Huge amounts of data, real important decisions being made on the content of that video, for example, recognizing a face, a white hat or a black hat. If you look at the technology, sending that data somewhere to do that recognition just does not make sense. Where is it going? It's going actually into the camera itself, right next to the data, because that's where you have the raw data, that's where you have the maximum granularity of data, that's where you need to do the processing of which faces are which, right close to the edge itself, and then you can send the other data back up to the cloud, for example, to improve those algorithms within that camera, to do all that sort of work on the batch basis over time, that's what I was looking at, and looking at the cost justification for doing that sort of work. So today, we've got a set people here on the panel, and we want to talk about coming down one level to where IoT and IT are going to have to connect together. So on the panel I've got, I'm going to get these names really wrong, Sanjeev Kumar? >> Yes, that's right. >> From FogHorn, could you introduce yourself and what you're doing where the data is meeting the people and the machines? >> Sure, sure, so my name is Sanjeev Kumar, I actually run engineering for a company called FogHorn Systems, we are actually bringing analytics and machine learning to the edge, and, so our goal and motto is to take computing to where the data is, than the other way around. So it's a two-year-old company that started, was incubated in the hive, and we are in the process of getting our second release of the product out shortly. >> Excellent, so let me start at the other end, Rohan, can you talk about your company and what contribution you're focusing on? >> Sure, I'm head product marketing for Maana, Maana is a startup, about three years old, what we're doing is we're offering an enterprise platform for large enterprises, we're helping the likes of Shell and Maersk and Chevron digitally transform, and that simply means putting the focus on subject matter experts, putting the focus on the people, and data's definitely an important part of it, but allowing them to bring their expertise into the decision flows, so that ultimately the key decisions that are driving the revenue for these behemoths, are made at a higher quality and faster. >> Excellent. Well, two software companies, we have a practitioner here who is actually doing fog computing, doing it for real, has been doing it for some time, so could you like, Janet George from Western Digital, can you introduce yourself, and say something from the trenches, of what's really going on? >> Okay, very good, thank you. I actually build infrastructure for the edge to deal with fog computing, and so for Western Digital, we're very lucky, because we are the largest storage manufacture, and we have what we call Internet of Things, and Internet of Test Equipment, and I process petabytes of data that comes out of the Internet of Things, which is basically our factories, and then I take these petabytes of data, I process them both on the cloud and then on the edge, but primarily, to be able to consume that data. And the way we consume that data is by building very high-profile models through artificial intelligence and machine learning, and I'll talk a lot more about that, but at the end of the day, it's all about consuming the data that you collect from anywhere, Internet of Things, computer equipment, data that's being produced through products, you have to figure out a way to compute that, and the cloud has many advantages and many trade-offs, and so we're going to talk about the trade-offs, that's where the gap for computing comes into play. >> Excellent, thanks very much. And last but not least, we have Val, and I can never pronounce your surname. >> Bercovici. >> Thank you. (chuckling) You are in the midst of a transition yourself, so talk about where you have been and where you're going. >> For the better part of this century, I've been with NetApp, working at various functions, obviously enterprise storage, and around 2008, my developer instinct kind of fired up, and this thing called cloud became very interesting to me. So I became a self-anointed cloud czar at NetApp, and I ended up initiating a lot of our projects which we know today as the NetApp Data Fabric, that culminated about 18 months ago, in acquisition of SolidFire, and I'm now the acting CTO of SolidFire, but I plan to retire from the storage industry at the end of our fiscal year, at the end of April, and I'm spending a lot of time with particularly the Cloud Native Compute Foundation, that is, the opensource home of Google's Kubernetes Technology and about seven other related projects, we keep adding some almost every month, and I'm starting to lose track, and spending a lot of time on the data gravity challenge. It's a challenge in the cloud, it's a particularly new and interesting challenge at the edge, and I look forward to talking about that. >> Okay, and data gravity is absolutely key, isn't it, it's extremely expensive and extremely heavy to move around. >> And the best analogy is workloads are like electricity, they move fairly easily and lightly, data's like water, it's really hard to move, particularly large bodies around. >> Great. I want to start with one question though, just in the problem, the core problem, particularly in established industries, of how do we get change to work? In an IT shop, we have enough problems dealing with operations and development. In the industrial world, we have the IT and the OT, who look at each other with less than pleasure, and mainly disdain. How do we solve the people problem in trying to put together solutions? You must be right in the middle of it, would you like to start with that question? >> Absolutely, so we are 26 years old, probably more than that, but we have very old and new mix of manufacturing equipment, it's a storage industry, and in our storage industry, we are used to doing things a certain way. We have existing data, we have historical data, we have trend data, you can't get rid of what you already have. The goal is to make connectors such that you can move from where you're at to where you're going, and so you have to be able to take care of the shift that is happening in the market, so at the end of the day, if you look at five years from now, it's all going to be machine learning and AI, right? Agent technology's already here, it's proven, we can see, Siri is out here, we can see Alexa, we can see these agent technologies out there, so machine learning is a getting a lot of momentum, deep learning and neural networks, things like that. So we got to be able to look at that data and tap into our data, near realistically, very different, and the way to do that is really making these connections happen, tapping into old versus new. Like for example, if you look at storage, you have file storage, you have block storage, and then you have object storage, right? We've not really tapped into the field of object storage, and the reason is because if you are going to process one trillion objects like Amazon is doing right now with S3, you can't do it with the file system level storage or with the blog system level storage, you have to go to objects. Think Internet of Things. How many trillions of objects are going to come out of these Internet of Things? So one, you have to be positioned from an infrastructure standpoint. Two, you have to be positioned from a use case prototyping perspective, and three, you got to be able to scale that very rapidly, very quickly, and that's how change happens, change does not happen because you ask somebody to change their behavior, change happens when you show value, and people are so eager to get that value out of what you've shown them in real life, that they are so quick to adapt. >> That's an excellent-- >> If I could comment on that as well, which is, we just got through training a bunch of OT guys on our software, and two analogies that actually work very well, one is sort of, the operational people are very familiar with circuit diagrams, and so, and sort of, flow of things through essentially black boxes, you can think of these as something that has a bunch of inputs and has a bunch of outputs. So that's one thing that worked very well. The second thing that works very well is the PLC model, and there are direct analogies between PLC's and analytics, which people on the floor can actually relate to. So if you have software that's basically based on data streams and time, as a first-class citizen, the PLC model again works very well in terms of explaining the new software to the OT people. >> Excellent, okay, would you want to come in on that as well? >> Sure, I think a couple of points to add to what Janet said, I couldn't agree more in terms of the result, I think Maana did a few projects, a few pilots to convince customers of their value, and we typically focus very heavily on operationalizing the output, so we are very focused on making sure that there is some measurable value that comes out of it, and it's not until the end user started seeing that value that they were willing and open to adopt the newer methodologies. A second point to that is, a lot of the more recent techniques available to solve certain challenges, there are deep learning neural nets there's all sorts of sophisticated AI and machine learning algorithms that are out there, a lot of these are very sophisticated in their ability to deliver results, but not necessarily in the transparency of how you got that, and I think that's another thing that Maana's learning, is yes, we have this arsenal of fantastic algorithms to throw at problems, but we try to start with the simplest approach first, we don't unnecessarily try to brute force, because I think an enterprise, they are more than willing to have that transparency in how they're solving something, so if they're able to see how they were able to get to us, how the software was able to get to a certain conclusion, then they are a lot happier with that approach. >> Could you maybe just give one example, a real-world example, make it a little bit real? >> Right, absolutely, so we did a project for a very large organization for collections, they have a lot of outstanding capital locked up and customers not paying, it's a standard problem, you're going to find it in pretty much any industry, and so for that outstanding invoice, what we did was we went ahead and we worked with the subject matter experts, we looked at all the historical accounts receivable data, we took data from a lot of other sources, and we were able to come up with models to predict when certain customers are likely to pay, and when they should be contacted. Ultimately, what we wanted to give the collection agent were a list of customers to call. It was fairly straightforward, of course, the solution was not very, very easy, but at least on a holistic level, it made a lot of sense to us. When we went to the collection agents, many of them actually refused to use that approach, and this is part of change management in some sense, they were so used to doing things their way, they were so used to trying to target the customers with the largest outstanding invoice, or the ones that hadn't paid for the longest amount of time, that it actually took us a while, because initially, what the feedback we got was that your approach is not working, we're not seeing the results. And when we dug into it, it was because it wasn't being used, so that would be one example. >> So again, proof points that you will actually get results from this. >> Absolutely, and the transparency, I think we actually sent some of our engineers to work with the collections agents to help them understand what approach is it that we're taking, and we showed them that this is not magic, we're actually, instead of looking at the final dollar value, we're looking, we're calculating time value lost, so we are coming up with a metric that allows us to incorporate not just the outstanding amount, or the time that they haven't paid for, but a lot of other factors as well. >> Excellent, Val. >> When you asked that question, I immediately went to more of a nontechnical business side of my brain to answer it, so my experience over the years has been particularly during major industry transitions, I'm old enough to remember the mainframe to client server transition, and now client server to virtualization and cloud, and really, sales reps have that well-earned reputation of being coin-operated, though it's remarkable how much you can adjust compensation plans for pretty much anyone, in a capitalist environment, and the IT/OT divide, if you will, is pretty easy to solve from a business perspective when you take someone with an IT supporting the business mentality, and you compensate them on new revenue streams, new business, all of a sudden, the world perspective changes sometimes overnight, or certainly when that contract is signed. That's probably the number one thing you can do from a people perspective, is incent them and motivate them to focus on these new things, the technology is, particularly nowadays is evolving to support them for these new initiatives, but nothing motivates like the right compensation plan. >> Excellent, a great series of different viewpoints. So the second question I have again coming down a bit to this level, is how do we architect a solution? We heard you got to architect it, and you've got less, like this, it seems to me that that's pretty difficult to do ahead of where you're going, that in general, you take smaller steps, one step at a time, you solve one problem, you go on to the next. Am I right in that? If I am, how would you suggest the people go about this decision-making of putting architectures together, and if you think I'm wrong and you have a great new way of doing it, I'd love to hear about it. >> I can take a shorter route. So we have a number of customers that are trying to adopt, are going through a phased way of adopting our technology and products, and so it begins with first gathering of the data, and replaying it back, to build the first level of confidence, in the sense that the product is actually doing what you're expecting it to do. So that's more from monitoring administration standpoint. The second stage is you should begin to capture analytical logic into the project, where it can start doing prediction for you, so you go into, so from operational, you go into a predictive maintenance, predictive maintenance, predictive models standpoint. The third part is prescriptive, where you actually help create a machine learning model, now, it's still in flux in terms of where the model gets created, whether it's on the cloud, in a central fashion, or some sort of a, the right place, the right context in a multi-level hierarchical fog layer, and then, you sort of operationalize that as close to the data again as possible, so you go through this operational to predictive to prescriptive adoption of the technology, and that's how people actually build confidence in terms of adopting something new into, let's say, a manufacturing environment, or things that are pretty expensive, so I give you another example where you have the case of capacitors being built on a assembly line, manufacturing, and so how do you, can you look at data across different stations and manufacturing on a assembly line? And can you predict on the second station that it's going to fail on the eighth one? By that, what you're doing is you are actually reducing the scrap that's coming off of the assembly line. So, that's the kind of usage that you're going to in the second and third stage. >> Host: Excellent. Janet, do you want to go on? >> Yeah, I agree and I have a slightly different point of view also. I think architecture's very difficult, it's like Thomas Edison, he spent a lot of time creating negative knowledge to get to that positive knowledge, and so that's kind of the way it is in the trenches, we spend a lot of time trying to think through, the keyword that comes to mind is abstraction layers, because where we came from, everything was tightly coupled, and tightly coupled, computer and storage are tightly coupled, structured and unstructured data are tightly coupled, they're tightly coupled with the database, schema is tightly coupled, so now we are going into this world of everything being decoupled. In that, multiple things, multiple operating systems should be able to use your storage. Multiple models should be able to use your data. You cannot structure your data in any kind of way that is customized to one particular model. Many models have to run on that data on the fly, retrain itself, and then run again, so when you think about that, you think about what suits best to stay in the cloud, maybe large amounts of training data, schema that's already processed can stay on the cloud. Schema that is very dynamic, schema that is on the fly, that you need to read, and data that's coming at you from the Internet of Things that's changing, I call it heteroscedastic data, which is very statistical in nature, and highly variable in nature, you don't have time to sit there and create rows and columns and structure this data and put it into some sort of a structured set, you need to have a data lake, you need to have a stack on top of that data lake that can then adapt, create metadata, process that data and make it available for your models, so, and then over time, like I totally believe that now we're running into near realtime compute bottleneck, processing all this pattern processing for the different models and training sets, so we need a stack that we can quickly replace with GPUs, which is where the future is going, with pattern processing and machine learning, so your architecture has to be extremely flexible, high layers of abstraction, ability to train and grow and iterate. >> Excellent. Do you want to go next? >> So I'll be a broken record, back to data gravity, I think in an edge context, you really got to look at the cost of processing data is orders of magnitude less than moving it or even storing it, and so I think that the real urgency, I don't know, there's 90% that think of data at the edge is kind of wasted, you can filter through it and find that signal through the noise, so processing data to make sure that you're dealing with really good data at the edge first, figuring out what's worth retaining for future steps, I love the manufacturing example, I have lots of customer examples ourselves where, for quality control in a high-moving assembly line, you want to take thousands of not millions of images and compare frame and frame exactly according to the schematics where the device is compared to where it should be, or where the components, and the device compared to where they should be, processing all of that data locally and making sure you extract the maximum value before you move data to a central data lake to correlate it against other anomalies or other similarities, that's really key, so really focus on that cost of moving and storing data, yeah. >> Yes, do you want the last word? >> Sure, Maana takes an interesting approach, I'm going to up-level a little bit. Whenever we are faced with a customer or a particular problem for a customer, we try to go over the question-answer approach, so we start with taking a very specific business question, we don't look at what data sources are available, we don't ask them whether they have a data lake, or we literally get their business leaders, their subject matter experts, we literally lock them up in a room and we say, "You have to define "a very specific problem statement "from which we start working backwards," each problem statement can be then broken down into questions, and what we believe is any question can be answered by a series of models, you talked about models, we go beyond just data models, we believe anything in the real world, in the case of, let's say, manufacturing, since we're talking about it, any smallest component of a machine should be represented in the form of a concept, relationships between people operating that machinery should be represented in the form of models, and even physics equations that are going into predicting behavior should be able to represent in the form of a model, so ultimately, what that allows us is that granularity, that abstraction that you were talking about, that it shouldn't matter what the data source is, any model should be able to plug into any data source, or any more sophisticated bigger model, I'll give you an example of that, we started solving a problem of predictive maintenance for a very large customer, and while we were solving that predictive maintenance problem, we came up with a number of models to go ahead and solve that problem. We soon realized that within that enterprise, there are several related problems, for example, replacement of part inventory management, so now that you figured out which machine is going to fail at roughly what instance of time from now, we can also figure out what parts are likely to fail, so now you don't have to go ahead and order a ton of replacement parts, because you know what parts are going to likely fail, and then you can take that a step further by figuring out which equipment engineer has the skillset to go ahead and solve that particular issue. Now, all of that, in today's world, is somewhat happening in some companies, but it is actually a series of point solutions that are not talking to each other, that's where our pattern technology graph is coming into play where each and every model is actually a note on the graph including computational models, so once you build 10 models to solve that first problem, you can reuse some of them to solve the second and third, so it's a time-to-value advantage. >> Well, you've been a fantastic panel, I think these guys would like to get to a drink at the bar, and there's an opportunity to talk to you people, I think this conversation could go on for a long, long time, there's so much to learn and so much to share in this particular information. So with that, over to you! >> I'll just wrap it up real quick, thanks everyone, give the panel a hand, great job. Thanks for coming out, we have drinks for the next hour or two here, so feel free to network and mingle, great questions to ask them privately one-on-one, or just have a great conversation, and thanks for coming, we really appreciate it, for our Big Data SV Event livestreamed out, it'll be on demand on YouTube.com/siliconangle, all the video, if you want to go back, look at the presentations, go to YouTube.com/siliconangle, and of course, siliconangle.com, and Wikibond.com for the research and content coverage, so thanks for coming, one more time, big round of applause for the panel, enjoy your evening, thanks so much.

>> Why don't I spend just a couple minutes talking about what we mean by digital enactment, turning data in models and models into action. And then we'll jump directly into, I'll introduce the panelists after that, and we'll jump directly into the questions. So Wikibon SiliconAngle has been on a mission for quite sometime now to really understand what is the nature of digital transformation, or digital disruption. And historically, when we've talked about digital, people talk about a variety of different characteristics of it, so we'll talk about new types of channels and activity on the web, and a many number of other things. But to really make sense of this, we kind of felt that we had to go to a set of basic principles, and utilize those basic principles to build our observations up. And so what we started with is a simple observation that, if it's not digital, or if it's not data, it ain't digital. By that we mean fundamentally the idea of digital business is how are we going to use data as an asset to differentially drive our business forward? And if we borrowed from Drucker, Drucker used to like to talk about the idea that business exists to create sustained customers, and so we would say that digital business is about applying data assets to differentially create sustained customers. Now to do that successfully, we have to be able to, as businesses, be able to establish a set of strategic business capabilities that will allow us to differentially use data assets. And we think that there are a couple of core strategic business capabilities required. One is human beings and most businesses operate in the analog world, so it's how do we take that analog data and turn it into digital data that we can then process. So that's the first one, the notion of an IOT as a transducer of information so that we can generate these very rich data streams. Secondly we have to be able to do something with those data streams, and that's the basis of big data. So we utilize big data to create models, to create insights, and increasingly through a more declarative style, actually create new types of software systems that will be crucial to driving the business forward. That's the second capability. The third capability is one that we're still coming to understand, and that is we have to take the output of those models, the output of those insights, and then turn them back into some event that has a consequential moment in the real world, or what we call systems of an action. And so the three core business capabilities that have to be built are this capture data through IOT, big data to process it, systems of an action also through IOT, through actuators, to actually that have a consequential action in the real world. So that's the basis of what we're talking about. We're going to take Flavio's vision that he just laid out, and then we, in this panel, are going to talk about some of the business capabilities necessary to make that happen, and then after this, David Foyer will lead a panel on specifically some of the lower level technologies that are going to make it work. Make sense guys? >> Sounds good (mumbles). >> Okay, so let me introduce the panelists. Over, down there on the end, Ted Connell. Ted is from Intel, I don't know if we can get the slide up that has their names and their titles. Ted, why don't you very quickly introduce yourself. >> Yeah, thank you very much. I run Solution Architecture for the manufacturing and industrial vertical, where we put together end to end ecosystem solutions that solve our clients business problems. So we're not selling silicone or semiconductors, we're solving our clients problems, which as Flavio said, requires ecosystem solutions of software, system integrators, and other partners to come together to put together end solutions. >> Excellent, next to Ted is Steve Madden of Equinix. >> Yeah, Steve Madden. Equinix is the largest interconnection, global interconnection company and a lot of the ecosystems that you'll be hearing about, come together inside our locations. And one of the things I do in there is work with our big customers on industry vertical level solutions, IOT being one of them. >> Phu Hoang, from Data Torrent. >> Hi, my name's Phu Hoang, I'm co-founder and chief strategy of a company called Data Torrent, and at Data Torrent, our mission is really to build out solutions to allow enterprises to process big data in a streaming fashion. So that whole theme around ingestion, transformation, analytics, and taking action in sub second on massive data is what we're focusing on. >> And you're familiar with Flavio. Flavio, will you take a second to introduce yourself. >> Yes, thank you, I am leading a company that is trying to manifest the vision highlighted here, building a platform. Not so much the applications, we are hosting the applications (mumbles) the data management and so forth. And trying to apply the industrial vertical first. Big enough to keep us busy for quite a while. >> So in case you didn't know this, we have an interesting panel, we have use case, application, technol infrastructure, and platform. So what' we'll try to do is over the next, say, 10 minutes or so, we're going to spend a little bit of time, again, talking about some of these business capabilities. Let me start off by asking each of you a question, and I will take, if anybody is really burning to ask a question, raise your hand, I'll do my best to see you and I'll share the microphone for just long enough for you to ask it. Okay, so first question, digital business is data. That means we have to think about data differently. Ted, at Intel, what is Intel doing when they think about data as an asset? >> So, Intel has been working on what is now being called Fog, and big data analytics for over a generation. The modern xeon server we're selling, the wire in the electronics if you will, is 10 silicon atoms wide. So to control that process, we've had to do what is called Industry 4.0 20 years ago. So all of our production equipment has been connected for 20 years, we're running... One of our factories will produce a petabyte of data a day, and we're running big data analytics, including machine learning on the stuff currently. If you look at an Intel factory, we have 2,000 fit clients on the factory floor supported by 600 servers in our data center at the factory, just to control the process and run predictive yield analytics. >> Peter: So that's your itch? >> Our competitive advantage at Intel is the factory. We are a manufacturer, we're a world class manufacturer. Our front end factories have zero people in it, not that we don't like people, but we had to fully automate the factory because as I speak, tens of thousands of water molecules are leaving my mouth, and if one of those water molecules lands on a silicon, it ain't going to work. So we had to get people physically out of the factory, and so we were forced by Moore's Law, and the product we build, to build out what became Fog, when they came up with the term seven years ago, we just came to that conclusion because of cost, latency, and security, it made sense to, you know, look, you got data, you got compute, there's a network between. It doesn't matter where you do the compute, bring the compute to the data, the data to the compute. You're doing a compute function, it doesn't matter where you do it. So Fog is not complicated, it's just a distributed data center. >> So when you think about some of the technologies necessary to make this work, it's not just batch, we're going to be doing a lot of stuff in real time, continuously. So Phu, talk a little bit about the system software, the infrastructure software that has to be put in place to ensure that this works for them. >> I think that's great. A little bit about our background, the company was founded by a bunch of ex-Yahoos that had been out for 12, 15 years from the early days. So we sort of grew up in that period where we had to learn about big data, learn about making all the mistakes of big data, and really seeing that nowadays, it's not good enough to get insight, you have to get insight in a timely fashion enough to actually do something about it. And for a lot of enterprise, especially with human being carrying around mobile phones and moving around all over the place, and sensors sending thousands, if not millions of events per second, the need for the business to understand what's going on and react, have insight and react sub second, is crucial. And what that means is the stuff that used to be batch, offline, you know, can kind of go down, now has to be continuous, 24 by seven. You can't lose data, you got to be able to recover and come back to where you were as if nothing has happened with no human intervention. There's a lot of theme around no human intervention, because this stuff is so fast, you can't involve human beings in it, then you're not reacting fast enough. >> Can I real quickly add one thing first? >> Peter: Sure. >> We think of data at Intel in half life terms. >> Yeah, that's exactly right. >> The data has valuable right now. If you wait a second, literally a second, the data has a little bit of value. You wait two second, it's historical data you can run regressions, and tell you why you screwed up, but you ain't going to fix anything. >> Exactly. >> If you want to do anything with your data, you got to do it now. >> So that, ultimately, we need to develop experience, a creed experience about what we're doing. And the stuff we're doing in applications will eventually find itself into platforms. So Flavio, talk to us a little bit about the types of things that are going to end up in the platform to ensure that these use cases are made available to, certainly, businesses that perhaps aren't as sophisticated as Intel. >> Yes, so in many ways, we are learning from what is going on in the Cloud, and has to come through this continuum, all the way into the machines. This break between what's going inside the machine, and old 1980 microprocessor and the server, and the Cloud server with virtualization on the other side cannot leave. So it has to be a continuum of computing so you can move the same function, the same container, all the way through first. Second, you really have to take the real time very, very seriously, particularly at the edge, but even in the back so that when you have these end to end continuum, you can decide where you do what. And I think that one of the models that was in that picture with a concentric circle is really telling what we need to learn first. Bring the data back and learn, and that can take time. But then you can have models that are lightweight, that can be brought down to the front, and impact the reaction to the data there. And we heard from a car company, a big car company, how powerful this was when they learned that the angle of a screwdriver, and a few other parameters, can determine the success of screwing something into a body of a car, that could go well, or could go very, very bad and be very costly. So all the learning, massive data, can come down to a simple model that can save a lot of money and improve efficiency. But that has to be hosted along this continuum. >> So from a continuum, it means we still have to have machines somewhere to do something. >> Touching the ground, touching the physical world requires machines, actuators. >> Peter: Absolutely, so Steve, what is Equinix doing to simplify the thinking through of some of these infrastructure issues? >> Yeah, I mean, the biggest thing that people find when they start looking at millions of devices, millions of data capture points, transferring those data real time and streaming it, is one thing hasn't changed and that's physics. So where those things are, where they need to go, where the data needs to move to and how fast, starts with having to figure out your own topology of how you're moving that data. As much as it's easy to say we're just going to buy a platform and choose a device, and we'll clink them together, there's still a lot of other things that need to be solved, physics being the first one. The second one, primarily, is volumes. So how much bandwidth and (mumbles) you're going to require. How much of that data are you going to back haul to centralized data center before you send it up to a Cloud? How much of it are you going to leave at the edge? Where do you place that becomes a bigger deal. And the third one is pretty much every industry has to deal with regulations. Regulations control what you can and can't do in terms of IT delivery, where you can place stuff, where you cannot place stuff, data that can leave the country, data that can't. So all these things mean that you need to have a thought through process of where you're placing certain functions, and what you're defining as your itch between the digital and physical world. And Equinix is an interconnection company that's sitting there as a neutral party across all the networks, all the clouds, all the enterprises, all the providers to help people figure that out. >> So before I ask the audience a question, now that I'm down here so I can see you so be prepared, I'm going to ask some of you a question. When you think about the strategic business capabilities necessary to succeed, what is the first thing that the business has to do? So why don't I just take Ted, and just go right on down the line. >> Yeah, so I think this is really, really important. I work with many, many clients around the world who are doing five, 10, 15 POCs, pilots, and the internet things, and they haven't thought through a codified strategy. So they're doing five things that will never fit together, that you will never scale, and the learnings you're using, you really can't do that much with. So coming up with what is my architecture, what is my stack going to look like, how am I going to push data, what is my data... You know, because when you connect to these things, I can't tell you how much data you're going to get. You're going to be overwhelmed by the data, and that's why we all go to the edge, and I got to process this data real time. And oh, by the way, if I only have one source of data, like I'm connecting to production equipment, you're not going to learn anything. 98% of that data's useless, you got to contextualize the data with either an inspection step, or some kind of contextualization that tells you if this then that. You need the then that, without that, your data is basically worthless. So now you're pulling multiple sources of data together in real time to make an understanding. And so understanding what that architecture looks like, spend the time upfront. Look, most of us are engineers, you know five percent additional work upfront saves you 95% on the backend, that's true here. So think through the architecture, talk to some of us who have been working in this area for a long time. We'll share our architecture, we have reference architecture that we're working with companies. How do you go from industry 2.0 or industry 3.0, to industry 4.0? And there is a logical path to do it, but ultimately, where we're going to end up is a software defined universe. I mean, what's a cloud? It's a software defined data center. Now we're doing software defined networks, software defined storages, ultimately we're going to be doing software defined systems because it's cheaper. You get better capital utilization, better asset utilization, so we will go there, so what does that mean for you infrastructure, and what are you going to do from an architectural perspective, and then take all of your POCs and pilots, and force them to do that specifically around security. People are doing POCs with security that they don't even have any protocols, they're violating all their industry standards doing POCs, and that's going to get thrown out. It's wasted time, wasted effort, don't do it. >> Steve, a couple sentences? >> Yeah, essentially it's not going to be any prizes for me saying think interconnection first. A lot of our customers, if we look at what they've done with us, everyone from GE to real time facial recognition at the edge, it all comes down to how are you wired, topology wise, first. You can't use the internet for risk reasons, you can't necessarily pay for multiple (mumbles) bandwidth costs, et cetera. So low latency, 80% lower latency, seven times of bandwidth at half the cost is a scalable infrastructure to move (mumbles) around the planet. If you don't have that, the rest of the stuff (mumbles) breakdown. >> Peter: Phu? >> Well I would say that analytics is hard, analytics in real time is even harder. And I think with us talking to our customers, I feel for them, they're confused. There's like a million solutions out there, everybody's trying to claim to do the same thing. I think it's both sides, consumers have to get more educated, they have to be more intelligent about their POCs, but as an industry, we also have to get better at thinking about how do we help our customer succeed. It's not about let me give you some open source, and then let me spend the next 10 months charging you professional services to help you. We ought to think about software tools and enterprise tools to really help the customer be able to think about their total cost (mumbles) and time to value to handle this thing, because it's not easy. >> Peter: Flavio. >> Yeah, we're facing an interesting situation where the customers are ready, the needs are there, the marketing is going to be huge, but the plot, the solution, is not trivial. It is maturing and we are all trying to understand how to do it. And this is the confusion that you see in many of these half baked solution (mumbles). Everything is coming together, and you have to go up the stalk and down the stalk with full confidence, that's not easy. So we all have to really work together. Give ourselves time, be feeling that we are in a competitive world, preparing for addressing together a huge market. And trying to mature these solutions that then will be replicated more and more, but we have to be patient with each other, and with the technologies that are maturing and they're not fully there and understood. But the market is amazing. >> Peter: So we have a Twitter question. >> Man: It's being live streamed, the audience is really engaged online as well, digital. So we have a question from Twitter from Lauren Cooney saying, "Would like to know what industries would "be most impacted with digitization "over the next five years." >> Which one won't be? (men laughing) All of them, what we've seen, the business model is the data. I mean, our CEOs calling data the new gold. I mean, it's the new oil. So I don't know of anything, unless you're doing something that is just physical therapy, but that even data, you can do data on that. So yeah, everything, yeah, I don't know of anything that won't be. >> I think the real question is how is it going to move through industries. Obviously it's going to start with some of the digital native, it's all ready deep into that, deep into media, we're moving through the media right now. Intel's clearly a digital company, and you've been working, you've been on this path for quite some time. >> Let me give you a stat. Intel has a 105,000 people, and 144,000 servers. So we're about 1.5 server to people, that's what kind of computation we're (mumbles). >> Peter: We can help you work on that. >> If you do like the networking started by (mumbles) the internet, then content delivery, and media, hard media, et cetera, is gone. Financial services and trading exchanges pretty much show what digital market's going to be in the future. Cloud showed up, and now, I think he's right, it's effecting every industry. Manufacturing, industrial, health professional services are the top three right now. But people who shop to ask for help went from every industry on every country, for that matter. >> Our customers are, you know, the top players in almost every vertical. You start out as a small company thinking that you're going to attack one vertical, but as you start to talk about the capability, everybody (mumbles) wait, you're solving my problem. >> Peter: (mumbles) are followers, is what you mean. >> Yeah, because what business would say, hey, I don't want to know what's going on with my business, and I don't want to take any action. >> Add to that it's an ecosystem of ecosystems. No one, by themselves, is going to solve anything. They have to partner and connect with other people to solve the solution. >> So I'll close the panel by making these kind of summary comments, the business capabilities that we think are going to be most important are, first off, when we talk about the internet of things, we like to talk about the internet of things and people. That the people equation doesn't go away. So we're building on mobile, we're building on other things, but if there's a strategic capability that's going to be required, it's going to be how is this going to impact folks who actually create value in the business. The second one, I'll turn it around, is that IT organizations have gone through a number of different range wars, if you will, over the past 20 years. I lived through IT versus telecom, for example. The IT, OT conflict, or potential conflict, is non trivial. There's going to be some serious work that has to be done, so I would add to the conversation that we've heard thus far, the answers that we've heard thus far, is the degree to which people are going to be essential to making this work, and how we diffuse this knowledge into our employees, and into our IT and professional communities is going to be crucial, especially with developers because Flavio, if we are, right now, trying to figure stuff out, it really matures when we think about the developer world. Okay, so I want to close the first panel and get ready for the second panel. So thank you very much, and thank you very much to our panelists. (audience applauding) And if we could bring David Foyer and the second panel up, we'll get going on panel two. Oh, we're going to get together for a picture. (exciting rhythmic music)

>> It's a pleasure to be here with you and to tell you about something I've been dreaming about and working for for many years and now is coming to the surface quite powerfully and quite usefully in many areas. I apologize, sometimes this flickers for some reason but I hope it doesn't disturb the story. I'd like to give you a little touch of history since I was there at the beginning of this journey and give you a brief introduction to what we mean for Fog Computing. And then go quickly to three powerful application spaces for this technology, together with industrial internet and one is industrial automation. That's the focus of our activity as Nebbiolo Technologies. The other one is one of my favorite ones and we'll get there is the automotive that caught fire here in Silicone Valley in the last years, the autonomous car, the connected vehicle and so on. And this is related to also to intelligent transportation and Smart Cities. And then a little touch on what Fog Computing means for Smart grid energy but many, many other sectors will find the same usefulness, the same architecture dimensions of Fog Computing applicable. So this is the story that comes back hopefully, here, the day in 2010 when Fog Computing, the word started here, oh God, is this jumping around? I think it's the connector, this is the age of the connector, this is the age of the Dongles. This is not an Apple Dongle and so we are having troubles. And this is not yet one of the last machines that are out. Let's hope for, I never had this problem, okay. Alright, this date 2010 at the Aquarium Research Center in Monterey where I gave a talk about robots going down deep in the bottom of those big valleys under the ocean and when I finished, the lady, Ginny in the middle approached me and told me, look, why don't you call what you're talking about fog computing? Because it's cloud computing brought too close to the ground and I protested for about 15 minutes. And on the drive home, I thought that's really a good name for what we are doing, what we have been doing in the last years and I started trying it out and using it and more and more I found good response and so seven years later, I'm still here talking about the same thing. What's happening is Fog, the edge of the metric zone was very important but it was always very important in IT, is still very important in IT in mobile, in content distribution but when IOT came to the surface, it became even more relevant to understand the need of resources, virtualized real time capable, secure, trusted with storage computing and networking coming together at the edge. At the edge of the IT network, now they are calling this mobile edge, they realize we are realizing that mobile can benefit from local resources at the edge, powerful real time capable resources but also and more importantly for what we are doing in this space of operational technologies, this is the space, the other and the other side of the boundary between information technologies and operational technologies and here is where we are living with Fog Computing these days so, apologize, I apologize for this behavior that is, maybe I have another Dongle, Apple Dongle. Maybe I could look at that, maybe Morris can help me out here, anyway, so what is Fog Computing? Fog Computing is really the platform that brings modern, Cloud inspired Computing storage here is important here for our friends at Western Digital and networking functions closer to the data producing sources. In our case, machines, things, but not just bringing Cloud down, it's also bringing functions up from the machine world, the real time, the safety functions, the trusting and reliability functions required in that area and this is a unified solution at the edge that really brings together communication, device management, data harvesting, analysis and control. So this is kind of new except for our friends in Wall Street. The real time part was not as sensitive. Now we are realizing how important it is and how important the position of resources is in the future of solutions in this space and so it's not boxes. It's a distributed layer of resources, well managed at the edge of the network and really has a lot of potential across multiple industries. Here we see the progress also in the awareness of this topic with the open fog control room that is now a very active and even the Vcs. Peter Levine here is talking about the importance of the edge. What is really happening is the the convergence. I think we should probably stop and use a different Dongle. Is this the one, no, no, this is not the right Dongle. The world of Dongles, sorry. Oh boy. Oh you have the computer with the, okay, is the right Dongle with the right computer, okay. Here we are, okay. Alright, we're getting back there. This is the new Apple. Okay, we are here, this looks better, thank you. Alright, so this is to be understood. This is the convergence of IT functionality, the modern IT functionality with the OT requirements and this is fundamentally the powerful angle that Fog Computing brings to IOT and machine world so all the nice things that happened in the Cloud come down but meet the requirements of resources, the needs and the timing of the Edge. And so when you look at what is brought into particularly the world of operations, you see these kind of functions that are not usually there. In fact, when you meet this operational world, you find microprocessors, you find Windows machines, industrial Pcs and so on, not so much Linux, not so much the modern approaches to computing. These are the type of dimensions that you'll see have a particular impact on the pain points seen in the wold of applications. So now we go to the Use cases in, use cases in the internet of things. I think it's on your side, I'm sorry. Because it's the second machine. Okay, well, maybe here's the solution. So we have seen this picture of IOT multiple times. A lot of verticals, we are concentrating on this tree, one is the industrial, the second one is the autonomous vehicle in intelligent transportation, the third one, just touched upon is the Smart Grid. This is the area of activity for Nebbiolo Technologies. Those kind of body shops and industrial floors with large robots with a lot of activity around those robots with cells protecting the activities within each working space, this is the world PLCs, industrial Pcs controlling robots, very fragmented. Here we are really finding even more critical this boundary between operational and informational technologies. This is a fire wall, also a mental fire wall between the two worlds and best practice is very different in one place than the other particularly also in the way we handle data, security, and many other areas. In this space, which is also a little more characterized here with this kind of machines that you see in this ISA 99 or ISA 95 type of picture, you see the boundary between the two spaces, once more when we come back. And alright, so the key message here, very tough to go across, it's very complex, the interaction between the two worlds. And there is where deeply we find a number of pain points at the security level, at the Hardware architecture level, at the data analytics and storage level, at the networking, software technologies and control architecture. There's a lot happening there that is old, 1980's time frame, very stable but in need of new approaches. And this is where Fog Computing has a very strong impact And we'll see, sorry, this is a disaster here. Alright, what do we do, alright. Maybe I should go around with this computer and show it to you. Okay, now it's there for a moment. Now, this is, maybe you have to remember one picture of all this talk, look at this, what is this? This is a graphical image of a body shop of a an important car company, you see the dots represent computers within boxes, industrial Pcs, PLCs, controllers for welding machines, tools and so on. That is, if you sum up the numbers, it's thousands of computers, each one of them is updated through a UPC, USB stick, sorry and is not managed remotely. It's not secure because there's a trust that the whole area is enclosed and protected through a fire wall on the other side but it's very stable but very rigid. So this is the world that we are finding with dedicated, isolated, not secure computing, this is Edge Computing. But it's not what we hope to be seeing soon as Fog Computing in action there so this is the situation. Very delicate, very powerful and very motivating. And now comes IOT and this is not the solution. It's helping, IOT tries to connect this big region, the operational region to the back end to the Clouds, to the power of computing that is there, very important, predicting maintenance, many other things can be done from there but it's still not solving the problem. Because now you have to put little machines, gateways into that region, one more machine to manage, one more machine to secure and now you're taking the data out. You are not solving a lot of the pain points. There's some important benefits, this is very, very good. But it's not the story, the story is sold once you really go one step deeper, in fact, from connectivity between information technologies and informational technologies to really Convergence and you see it here where you're starting to replace those machines supporting each cell with a fog node, with a powerful convergent point of computing, real time computing that can allow control, analytics and storage and networking in the same nodes so now these nodes are starting to replace all the objects controlling a cell. And offer more functions to the cell itself. And now, you can imagine where this goes, to a convergent architecture, much more compact, much more homogeneous, much more like Cloud. Much more like Cloud brought down to the Edge. When this comes back, okay, almost there. So this is okay, this is now the image that you can image leads to this final picture that is now even not, okay, do you see it, okay. Now you're seeing the operational space with the fabric of computing storage and networking that is modern, that is virtualized, that supports an application store, now you have containers there. You can imagine virtual machines and dockers living the operational space. At the same time, you have it continuing from the Cloud to the network, the modern network, moving to the Edge into the operational space. This is where we are going and this is where the world wants us to go and the picture representing this transition and this application of Fog Computing in this area is the following, the triangle, the pyramid is now showing a layer of modern computing that allows communications analysis control application hosting and orchestration in a new way. This is cataclysmic, really is a powerful shift, still not fully understood but with immense consequences. And now you can do control, tight, close to the machines, a little slower through the Fog and a little slower through the Cloud, this is where we are going. And there's many, many used cases, I don't dwell on those. But we are proceeding with some of our partners exactly in this direction. Now the exciting topics if I can have five more minutes making up the time wasted. What's going on here, the connected vehicle, the autonomous vehicle, the electrification of automobile are all converging and I think it's very clear that the para dime of Fog Computing is fundamental here. And in fact, imagine the equivalent of a manufacturing cell with a converging capabilities into the Fog and compare it with what's going on with the autonomous vehicle. This is a picture we used a Sysco seven years ago. But this is now, a car is a set of little control loops, ECUs, little dispersed, totally connected computers. Very difficult to program, same as the manufacturing cell. And now where are we going, we are going towards a Fog node on wheels, data center on wheels but better a Fog node on wheels with much better networking between, with a convergence of the intelligence, the control, the analytics, the communications in the middle and a modern network deterministic internet called TSN is going to replace all these CAN boxes and all these flakey things of the past. Same movement in industrial and in the automobile and then you look at what's going on in the intelligent transportation, you can imagine Fog Computing at the edge, controlling the junctions, the traffic lights, the interactions with cars, cars to cars and you see it here, this is the image, again where you have the operational space of transportation connected to the Clouds in a seamless way which these nodes of computing storage and networking at the junctions inside the cars talking to each other, so this is the beautiful movement coming to us and it requires the distribution of resources with real time capabilities, here you see it. And now, the Smart Grid, again, it cannot continue to go the same way with a utility data center controlling everything one way, it has to have and this is from Duke and a standardization body, you can see that there's a need of intelligence in the middle, Fog nodes, distributed computing that are allowing local decisions. Energy coming from a microcell into the grid and out, a car that wants to sell it's energy or buy energy doesn't need to go slowly to a utility data center to make decisions so again, same architecture, same technologies needed, very, very, very powerful. And we could go on and on and on, so what are we doing? We won't advertise here but the name has to be remembered. The name comes from a grape that grows in the Fog in Northern Italy, it's in Piedmont, my home town is behind that 13th century castle you see there. Out there is Northern Italy close to Switzerland. That vineyard is from my cousin, it's a good Nebbiolo, starting to be sold in California too. So this is the name Nebbia Fog comes to, Nebbiolo Technologies, we are building a platform for this space with all the features that we feel are required and we are applying it to industrial automation. And our funders are not so much from here, are from Germany, Austria, KUKA Robotics, TTTech, GiTV from Japan and a few bullets to complete my presentation. Fog Computing is really happening. There's a deep need for this converged infrastructure for IOT including Fog or Edge as someone calls it. But we need to continue to learn, demonstrate, validate through pilots and POCs and we need to continue to converge with each other and with the integrators because these solutions are big and they are not from a little start up. They are from integrators, customers, big customers at the other end, an ecosystem of creative companies. No body has all the pieces, no Sisco, no GE and so on. In fact, they are all trying to create the ecosystem. And so let's play, let's enjoy the Cloud, the Fog and the machines and try to solve some of the big problems of this world. >> Okay, Flavio, well done. >> Sorry for that. Sorry for the hiccups. >> Now we do that on purpose to see how you'd react and you're a pro, thank you so much for the great presentation. >> Alright. >> Alright, now we're going to get into panel one, looking at the data models and putting data to work.

>> (soft music) >> Presenter: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Welcome back everyone to the CUBE's coverage of IBM Think 2021. I'm your host, John furry with theCUBE. Our next guest Manish Chawla who's the industry general manager of energy, resources and manufacturing, a great guest to break down this next generation of infrastructure modern applications and changing the business in the super important areas he's regulated verticals. Manish, it's great to see you. Thank you for coming back on theCUBE. >> Thank you John. Good to meet you. >> You know, this is the area where I've been saying for years the cloud brings great scale horizontally scalable data, but at the end of the day, AI and automation really has to be specialized in the verticals. In this we're going to see the action the ecosystems for connecting. This is a big deal here I think this year, transformation is the innovation, innovation at scale. This seems to be the underlying theme that we've been reporting on. So I'd love to get your thoughts on how you see this Fourth Industrial Revolution as you say, coming about. Can you define for us what that means? And when you say that, what does it mean for customers? >> Yeah, sure, sure. So, you know, in sort of simple terms all the technologies that we see around us, whether it's AI we talk about AI, we talk about 5G, we talk about Edge Cloud Robotics. So the application of those to the physical world in some sense in the industrial world is what we define as the Fourth Industrial Revolution. Essentially, it's the convergence between the humans, the physical aspect, like the machines and the cyber either digital aspects, bringing that together. So companies can unlock the value from the terabytes and petabytes of data that our connected world is now able to produce. >> How does the IOT world come in? We've been again, I did a panel I think two years ago called you know the industrial IOT Armageddon. And it was really kind of pointing. It was kind of provocative title, but the point was you know, the industrial connections are all devices now and they're connected to the network security super important. This industrial revolution includes this new edge. >> It's got to be smarter and intelligent. What's your take on that? >> Yeah, absolutely. It is about the edge. It's about devices. It's about delivering capturing the data from the umpteen devices. You know, we've recently heard about the chip shortage which gives you an idea that there is so much utilization of compute power everywhere in the world. And the world is becoming very software defined. So whether it's software defined machines software defined products, the washing machines that we use at home, the cars we use home, everything is gradually becoming, not gradually I'd say rapidly becoming intelligent. And so that edge or IOT is the foundation stone of everything we're talking about. >> Well, you mentioned software on a chip SOC that's a huge mega wave coming. That's going to bring so much more compute into smaller form factors which leads me to my next question, which kind of, I'm kind of answering for myself but I'm not a manufacturing company but why should they care about this trend from a business perspective besides the obvious new connection points? What's really in it for them? >> Yeah. So big topic right now is this topic of resilience, right? So that's one aspect. This, the pandemic has taught us that resilience is a core objective. The second objective, which is front and center of all CEOs or CEOs is out-performance. And so what we're seeing is out-performance are investing in technology for many goals, right? So it's either sustainability which is a big topic these days, and a huge priority. It's about efficiency. It's about productivity. It's also now more and more about delivering a much stronger customer experience, right? Making your products easier to use much easily consumable as well. So if you, when you pull it all together it's an end to end thinking about using data to drive those objectives of out-performance as well as resilience. >> What's the progress being made so far on the manufacturing industry on this front? I mean, is it moving faster or you mentioned accelerating but where is the progress bar right now? >> So I think as we came into 2020, I would have described it as we were starting to enter the chapter two where companies were moving from experimentation to really thinking of scaling this. And what we found is the pandemic really caused a big focus on these, as Winston Churchill has been attributed the quote "Never waste a good crisis." A lot of CEOs, a lot of executives and leadership really put their energy into accelerating digital transformation. I think we really, two thirds have been able to accelerate their digital transformation. So the good news is, you know companies don't have to be convinced about this anymore. They're really, their focus is on where should I start? Where should I focus? And what should I do next? Right, is really the focus. And they are investing in sort of two types of technologies is the way we see it. What I would call foundational technologies because there's a recognition that to apply the differentiating technologies like AI and capturing and taking value of the data you need a strong architectural foundation. So whether it's cybersecurity, it's what we call ITOT integration connecting the devices back to the mothership. And it's also applying cloud but cloud in this context is not about typically what we think as public cloud or a central spot. It's really bringing cloud-like technologies also to the edge or to the plant or to the device itself whether it's a mobile device or a physical device. And that foundation is that recognition that you've got to have the foundation that you can build your capabilities on top. Whether it's for customers or clients or colleagues. >> That's a great insight on the architecture. I think that's a successful playbook. It sounds so easy. I do agree with you. I think people have said this is a standard now hybrid cloud, the edge pretty clear visibility on the architecture of what to do or what needs to be done, how to do it, all other story. So I have to ask you, we hear of these barriers. There's always blockers. I think COVID's released some of those relieved some of those blockers because people have to force their way into the transformation but what are those barriers that are stopping the acceleration for customers to achieve the benefits that they need to see? >> Yeah. So I think one or one key barrier is a recognition that most of our plants or manufacturing facilities or supply chains really run in a brownfield manner. I, there's so many machines so many facilities that have been built over decades. So there's a proliferation of different ages of devices, machines, et cetera. So making sure that there is a focus on laying out a foundation, that's a key barrier. There is also a concern that, you know the companies have around cybersecurity. The more you connect the more you increase the attack surface. And we know that that hacks and so on are, are a dominant issue now whether it's for ransomware or for other malicious reasons. And so modernizing the foundation and making sure you're doing it in a secure way those are the key concerns that executives have. And then another key barrier I see is making sure that you have a key, key core objective and not making too many different varied experimentation beds. So keeping a focus on what's the core use case of benefit you're after and then what's the foundation to make sure that you're going after it. Like I said, whether it's quality or productivity or such like. >> So the keys to success, if I get this right is you have the right framework for this as you say, industry 4.0 you got to understand the collaborative dynamics and then have an ecosystem. >> Yeah. Can you unpack those three things? Because take me through that. You got to the framework, the collaboration and the ecosystem. What does that mean specifically? >> So the way I take the simplest way to think of it is the amount of work and effort that all companies have to put in, is so great in front of them. The opportunities are so great as well that nobody can hire all the smart people that are needed to achieve the goals. Everybody has their own specific I would say focus and capabilities they bring to bear. So the collaboration between manufacturers the collaboration between operational technology companies like the Siemens, ABB, Schlumberger, et cetera and IT technology companies like ourselves that three-part collaboration is sort of the heart of what I see as ecosystems coming together. The other dimensionality of ecosystems is also looking at it from a supply chain or a value chain perspective cause how something becomes more intelligent or smarter or more effective is also being able to work across the supply chain or value chain. So those are our key focus areas make sure we are collaborating across value chains and supply chains, as well as collaborating with manufacturers and OT, operational technology companies to be able to bring these digital capabilities with the right capabilities of operational technology companies into the manufacturers. >> If I asked you, how are you doing that? What specifically would you say? I mean, how are you collaborating? What's some examples give some examples of this enaction. >> Certainly. So we recently announced over the last say, nine months or so three strategic, very transformative partnerships. The first one I'll share with you is with Schlumberger. Schlumberger is the world's largest oil field services company. And now also the world's largest distill technology company for the oil and gas industry. So we've collaborated with them to bring hybrid cloud to the digital platform so they now can deploy their capabilities to any customer regardless of whether they want it in country or on a public cloud. Another example is we've established a data platform with Schlumberger for the oil and gas industry, to be able to bring again that data platform to any location around the world. The advantage of hybrid, the advantage of AI. With EVB, what we've done is we've taken our smart sync IT security connected with their products and capabilities for operational systems. And now are delivering an end to end solution that you can get cyber alerts or issues coming from manufacturing systems dry down to right up to an IT command center where you're seeing all the events and alerts so that they can be acted upon right away. So that's a great example of collaborating with IT from a security point of view. The third one is industrial IOT with Siemens and we've partnered with Siemens to deliver the MindSphere private cloud edition. Delivered on our red hat hybrid cloud. So this is an example where we are able to take our horizontal technologies, apply it with their verticals smarts and deep industry context put our services capabilities on top of it so they can deliver their innovations anywhere >> Manish is such an expert on this such a great leader on this area and I have to ask you you know, you've been in this mode of evangelizing and leading teams and building solutions around digital re platforming or whatever you want to call it, renovations. >> Manish: Right >> What's the big deal now, if you had to, I mean, it seems like it's all coming together with red hat under the covers, you get distributed networks with the Edge. It's all kind of coming together now for the verticals because you got the best of both worlds. Programmable scalable infrastructure with modern software applications on top. I mean, you've been even in the industry for many many waves, why is this wave so big and important? >> So I think there is no longer the big reason why it's important is I think there's no reason why companies have to be convinced now that the clarity is there that this needs to happen so that's one. The second is, I think there's a high degree of expectation among consumers, among employees and among customers as well, that everything that we touch will be intelligent. So these technologies really unlock the value unlock the value, and they can be deployed at scale that's really, I think what we're seeing as the focus now. And being able to deliver the innovation anywhere whether someone wants it at the Edge next to a machine that's operating, or be able to look at how a manufacturing facility or different product portfolio is doing in the boardroom. It's all available. And so that shop floor, the top floor connection is what everybody's aiming for but we also now call it Edge to enterprise. >> And everything works better, the employees are happy people are happy, stakeholders are happy. Manish great insight. Thank you for sharing here on theCUBE for Think 2021. Thanks for coming on theCUBE. >> Absolutely thanks John for having me. >> Okay. I'm John Furry host theCUBE for IBM Think 2021. Thanks for watching. (soft 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.

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019 brought to you by IBM Wear and its ecosystem partners. >> Welcome back to the Cube. It's the Emerald 2019 our 10th year water wall coverage. Three days, two sets, lots of content. Instrument of my co host is Justin Warren. And one of the big stories coming into the show is VM Wear actually went on an acquisition spree. A hold number of acquisitions. Boston based Carbon Black over $2 billion Pivotal brought back into the fold for also, you know, around that ballpark of money on Happy to Welcome to the program. One of those acquisitions, such and Conti, is sitting to my right. He's the co founder of Hana is also a professor at Stanford University. Thank you so much for joining us and joining us. Also for the segment. Shakeri Air, the executive vice president general manager of Telco Edge Cloud at VM Wear, Shaker said, Yes, there's a lot of acquisitions not to play favorites, but maybe this is his favorite. No question about it. All right. Eso such in, you know, boy, you know the Paolo Alto Stanford connection. We were thinking back, You know, the Founders Of'em where, of course, you know came from Stanford. Many acquisitions over the year, including the mega next era acquisition. You know, quite a few years ago, I came out of Stanford. Give us what was the genesis in the Why of Hana. >> It's actually interesting Stanford Connection to So I've been a faculty at Stanford for the last 10 years on dhe. I have seen the SD and moment very close on up front on one of the dirty secrets of S. T M says it makes the netbooks programmable, but someone still has to write the programs on. So that's usually a very complex task on the pieces beyond the company was, Can we use the eye to learn how to program the network rather than humans having to program the network to do management or optimization? So the division really waas can be built? A network that learned how to optimize itself learns how to manage itself on the technology we're building. Is this a pipeline that basically tries to deliver on that for mobile? >> It's great, Sachin, you know, my background is networking and it feels like forever. We've been hooking well. We need to get people from the cli over to the gooey. But we know in today's rightly complex world, whether it's a I or just automation, humans will not be able to keep up with it. And, you know, we know that that's where a lot of the errors would happen is when we entered humans into doing some of this. So what are some of the key drivers that make this solution possible today that, you know, it might not have been able to do done when when one train was first rolling out the first S t n? >> Yeah, talk about it in three dimensions. The one is, Why do we need it today? Right on. Then what is being what is happening that is enabling this today, right? So, apart from what I talked about Stu and I think the other big driver is, the way I like to think about it is that the Internet is going from a means of consumption to a means of control and interaction. So, increasingly, the application to BC driving the next big decade, our very way of controlling things remotely or the network like a self driving car, or be in interacting but very highly rich visual content like E. R. India. So the applications are becoming a lot more demanding on the Net. At the same time, the network is going through a phase off, opening up on becoming disaggregated network complexity is increasing significantly. So the motivation behind the company and why I thought that was the right time to start the company was these two friends are gonna collide with five coming along the applications that are driving five g and then at the complexity increasing our five. So that's why we started the company. What actually is enabling. This is the fact that we have seen a lot of progress with the eye over the last few years. It hasn't really. It hadn't really been applied at scale to networks and specifically mobile that book. So we definitely saw no, actually there, but increasingly, ah, lot of the infrastructure that is being deployed there was more and more telemetry available. There was more and more data becoming available and that also obviously feet this whole engine. So I think the availability of all of these Big Data Technologies Maur data coming in from the network and the need because of these applications and that complexity. I think there's a perfect confluence >> that there's lots of lots of II floating around at the moment, and there's different flavors of it as well. So this machine learning there's Aye aye, sir. When when you say that there's there's a I behind this What? What particular kind of machine learning or a Y you're using to drive these networks? >> This a few different techniques because the problems we solve our anomaly detection off. Then problems are happening in the network predicting how network conditions are going to evolve. For example, predicting what your devices throughput is gonna be the next 30 seconds. We're also learning how to control the knobs in the neck using AI ai techniques. So each of these has different classes of the eye techniques. So, for example, for control we're using reinforcement learning, which is the same technique that Google used to kind of been on alphago. How do you learn how to play a game basically, but area the game you're playing it optimizing the network. But for the others, it's a record of neural networks to do predictions on Time series data. So I think it's a combination of techniques I wouldn't get to wherever the techniques. It's ultimately. But what is the problem you're trying to solve? And then they picked the right technique to solve it, >> and so on that because the aye aye is actually kind of stupid in that it doesn't know what they wouldn't. What an optimized network looks like. We have to show it what that is. So what? How do you actually train these systems to understand? But what is an optimized network? What? How does how does that tell you? Define this is what my network optimal state should be. >> So that's a great question, because in networking like that, any other discipline that wants to use the eye. There's not a lot of label data. What is the state I want to end up at what is a problem state or what is a good state? All of this is labels that someone has to enter, and that's not available axe kid, and we're never gonna be able to get it at the scale we wanted. So one of our secret sauce is if you will, is semi supervised learning but basic ideas that we're taking a lot of domain knowledge on using that domain knowledge to figure out what should be the right features for these models so that we can actually train these models in a scalable fashion. If you just throw it a lot of data any I model, it just does not converge. Hardly constructive features on the other thing is, how do I actually define what are good kind of end state conditions? What's a good network? And that's coming from domain knowledge to That's how we're making I scale for the stomach. >> I mean, overall, I would say, as you look at that, some of the parameters in terms of what you want to achieve are actually quite obvious things like fewer dropped calls for a cellular network. You know, that's good. So figuring out what the metrics need to be and what the tuning needs before the network, that's where Hana comes in in terms of the right people. >> All right, so shake her. Give us a little bit of an understanding as to where this fits into the networking portfolio. You know, we heard no we heard from Patty or two ago. You know what would have strong push? Networking is on the NSX number. Speaks for itself is what's happening with that portfolio? >> No, absolutely. In fact, what we're doing here is actually broader than networking. It's sort off very pertinent to the network off a carrier. But that is a bulk off their business, if you will. I think if you sort of go back and look at the emirs of any any, any vision, this is the notion of having any cloud in any application land on any cloud and then any device connected to those applications on that any cloud side we are looking at particularly to cloud pools, one which we call the Telco cloud and the other is the edge cloud. And both of these fortuitously are now becoming sort of transforming the context of five G. So in one case, in the telco cloudy or looking at their core and access networks, the radio networks, all of this getting more cloud ified, which essentially leads toe greater agility in service deployment, and then the edge is a much more distributed architecture. Many points over which you can have compute storage network management and security deployed. So if you now think about the sort of thousands off nodes on dhe virtualized clouds, it is just impossible to manage this manual. So what you do need is greater. I mean, orders of magnitude, greater automation in the ability to go and manage and infrastructure like this. So, with our technology now enhanced by Johanna in that network portfolio in the Telco Edge Cloud portfolio, were able to go back to the carriers and tell them, Look, we're not just foundational infrastructure providers. We can also then help you automate help you get visibility into your networks and just help you overall manager networks better for better customer expedience and better performance. >> So what are some of the use coasters that you see is being enabled by five G? There's a lot of hype about five short the moment and not just five jail. So things like WiFi six. Yeah, it would appear to me that this kind of technique would work equally well for five g Your wife. I short a WiFi six. So what are some of the use cases? You see these thieves service providers with Toko Edge clouds using this for? Yeah, So I think overall, first of all, I'd >> say enterprise use cases are going to become a pretty prominent part off five, even though a lot off the buzz and hype ends up being about consumers and how much bandwidth and data they could get in or whether five chicken passing preys or not. But in fact, things like on premise radio on whether that is private. Lt it's 40 or five t. These are the kinds of Uschi cases that were actually quite excited about because these could be deployed literally today. I mean, sometimes they're not regulated. You can go in with, like, existing architectures. You don't need to wait for standardization to break open a radio architecture. You could actually do it, Um, and >> so this sort off going in and >> providing connectivity on an enterprise network that is an enhanced state off where it is today. We've already started that journey, for example, with yellow cloud and branch networking. Now, if we can take that toe a radio based architecture for enterprise networking, So we think, ah, use case like that would be very prominent. And then based on edge architectures distributed networks now becoming the next generation Cdn is an example. That's another application that we think would be very prominent. And then I think, for consumers just sort of getting things like gaming applications off on edge network. Those are all the kinds of applications that would consume this sort off high skill, reliability and performance. >> Can you give a little sketch of the company pre acquisition, you know, is the product all g eight? How many customers you? Can you say what you have there? Sure >> it does us roughly three years old. The company itself so relatively young. We were around 33 people total. We had a product that is already deployed with chairman Telcos. So it is in production deployment with Chairman Telco Ondas in production trials with a couple of other tier one telcos. So we built a platform to scale to the largest networks in the world on If I, if I were to summarize it, be basically can observe, makes sense or in real time about every user in the network, what their experiences like actually apply. I modeled on top of that to optimize each user's expedience because one of the vision bee had was the network today is optimized for the average. But as all off our web expedience personalized netbook experiences, not personalized can be build a network Very your experiences personalized for you for the applications, your running on it. And this was kind of a foundation for that. >> I mean, we In fact, as we've been deploying our telco Cloud and carrier networks, we've also been counting roughly how many subscribers are being served up. Today we have over 800 million subscribers, and in fact, I was talking to someone and we were talking about that does. Being over 10% off the population of the world is now running on the lack of memory infrastructure. And then along comes Johanna and they can actually fine tune the data right down to a single subscriber. Okay, so now you can see the sort of two ends of the scale problem and how we can do this using a I. It's pretty powerful. Excellent. >> So So if we have any problems with our our service fighters, b tech support and I love to hear from both of you, you know what this acquisition position means for the future of the places and obviously VM wear global footprint. A lot of customers and resource is. But you know what I mean to your team in your product. >> I mean, definitely accelerating how quickly we can now start deploying. This and the rest of the world be as a small company, have very focused on a few key customers to prove the technology we have done that on. I think now it's the face to scale it on. Repeat it across a lot of other customers, but I think it also gives us a broader canvas to play that right. So we were focused on one aspect of the problem which is around, if you will, intelligence and subscriber experience. But I think with the cloud on but the orchestration products that are coming out of the ember, we can now start to imagine a full stock that you could build a network of full carrier network code off using using remote technology. So I think it's a broad, more exciting, actually, for us to be able to integrate not just the network data but also other parts of the stock itself. And >> it strikes me that this probably isn't just limited to telcos, either. The service providers and carriers are one aspect of this bit particularly five G and things like deployments into factory automation. Yes, I can see a lot of enterprise is starting to become much in some ways a little bit like a tell go. And they would definitely benefit from this >> kind of thing. Yeah, I mean, in fact, that's the basis of our internal even bringing our telco and EJ and I ot together and a common infrastructure pool. And so we're looking at that. That's the capability for deploying this type of technology across that. So you're exactly right, >> Checker want to give you the last word, you know, Telco space, you know? And then, obviously the broader cloud has been, you know, a large growth area. What, you want people taking away from the emerald 2019 when it comes to your team? >> Yeah, I think. To me, Calico's have a tremendous opportunity to not just be the plumbing and networking providers that they can in fact, be both the clowns of tomorrow as well as the application providers of tomorrow. And I think we have the technology and both organically as well as through acquisitions like Ohana. Take them there. So I'm just super excited about the journey. Because I think while most of the people are talking about five D as this wave, that is just beginning for us, it's just a perfect coming together on many of these architectures that is going to take telcos into a new world. So we're super excited about taking them. >> Shaker. Thank you so much for joining against auction. Congratulations and good luck on the next phase of you and your team's journey along the way. Thank you. Thank you for Justin. Warren comes to Minutemen, Stay with us. Still a bit more to go for VM World 2019 and, as always, thank you for watching the Cube.

>> 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)

>> Announcer: Live from Vancouver, Canada, it's theCUBE, covering OpenStack North America 2018. Brought to you by Red Hat, the OpenStack Foundation, and its ecosystem partners. >> Welcome back to theCUBE's coverage of OpenStack Summit 2018 in Vancouver. I'm Stu Miniman with my cohost for the week, John Troyer. Happy to welcome to the program first-time guest Ildiko Vancsa, coming off the edge keynote presentation this morning. She is the ecosystem technical lead with the Edge Computing Group as part of the OpenStack Foundation. Thanks so much for joining us. >> Thank you. >> Coming into this show, edge is one of those things that it was actually pretty exciting to talk about because edge is not only super hot, but when I thought back to previous shows, this is the sixth year we've had theCUBE here and my fifth year doing it, it's like, wait, I've been talking to all the Telcos for years here. NFV was one of those use cases, and when you connect the dots, it's like oh, edge, of course. I said this conference is actually hipster when it comes to edge. We were totally covering it well before we called it that. So, explain to us your role in the foundation and what led to the formation of this track. >> Yeah, so I'm the ecosystem technical lead within the foundation, which is basically a role that belongs under the business development team. So, I'm basically building connections with our ecosystem members. I'm trying to help them succeed with OpenStack, both as software package and as a community. We are embracing open source, of course, so I'm also trying to advocate for involvement in open source because I think that's a key. Like, you know, picking up an open source software component and use it, that's a great start, but if you really want to be successful with it and you want to be able to successfully build it into your business model, then getting involved in the community, both enhancing the software and maintaining of the software, that's really key. So, my role is also onboarding companies as well to be active members of the community, and my focus is shifting toward edge computing. The history of edge computing in OpenStack basically started last May when Beth Cohen from Verizon described their use case, which is OpenStack in a tiny box in production cycle, wow. So that was also a little bit of an eye-opener for us as well, that yes, it's telecom. It's 5G, but this is the thing that's called edge, and maybe this is something that we should also look deeper into. So, we went to San Francisco last September, OpenDev, 200 people, architects, software developers trying to figure out what edge computing is. I think we had the question at every single session, someone asked that, okay, yeah so, what did you mean exactly when you said edge? Because from the nature of the architecture, like, you have the central cloud and then the sides on the different-- >> John: There are several edges depending on how far you want to go. >> Exactly. >> For you and OpenStack, what does edge mean, or all the above? >> With OpenStack, so after OpenDev when we realized that it's not really a well-defined term, we wrote up a white paper. It's at OpenStack the role/edge. It's a short one, really to just set the ground for what edge computing is. And what we came up with is, so don't imagine like a two-sentence definition for edge computing because I still strongly believe that doesn't exist, and anyone who claims it, that's not true. What we did with the white paper is basically we set characteristics and criteria that defines cloud edge computing per se, like what people are talking about when you're moving out the compute and then working closer to the edge. Like what that means from the bandwidth perspective, from how you will manage it, what that means for security, and all these sort of things. And you can basically characterize what edge means. So we rather described these layers and how far we go, and as far as like, you know, the very end edge device and like the IOT sensors, that's not a target of OpenStack. So, OpenStack itself is infrastructure as a service, so our Edge Computing Group is still staying on that layer. The Edge Computing Group itself is focusing on the angles, what edge brings onto the table, all these requirements, you know, collecting the use cases and trying to figure out what's missing, what we need to implement. >> If can repeat and maybe I'll get it right or wrong. The idea is at a cell tower or at a remote office or branch office or some closet somewhere, there is a full set of OpenStack running, maybe a minimal set of OpenStack, but it's live, it's updatable. You can update services on it. You can update the actual OpenStack itself, and it doesn't need the spoke hardware necessarily, but it's now updatable and part of a bigger multi-cloud infrastructure from some sort of service entity or enterprise. >> Yeah. >> Is that fair? >> I think that's fair. So, there's OpenStack itself that people know very well, a lot of projects. So when we talk about edge, obviously we don't want to say that, okay, pick the whole thing and install all the 60 projects because that's really not suitable for edge. So what, for example, the group is looking into, that which OpenStack components are essential for edge. And also the group is defining small edge, medium edge, what that means from hardware footprint perspectives, so just to figure out what the opportunities are there, what will fit, what will not fit. OpenStack itself is very modular by today, so you can pick up the services that you need. So what we discussed, for example, this week is Keystone, identity, you need it of course. So how much that fits into the edge scenarios. And I think the main conclusion of the forum session yesterday was that, yeah, Keystone supports Federation. We talked through the cases, and it seems like that it's kind of there. So, we now need a few people who will sit down, put together the environment, and start testing it because that's when it comes out that, you know, almost there, but there a few things to tweak. But basically the idea is what you described, pick up the component, put it there, and work with it. We also have another project called Cyborg, which is fairly new. That's for hardware acceleration, so it is providing a framework to plug in GPUs, FPJs, and these sort of, a bit more specialized hardware which will be really useful for edge use cases to OpenStack. So that's for example something that China Mobile and the OPNFV Edge Cloud Group is looking into to use, so I really hope that we will get there this year to test it in the OPNFV Pharos Labs in action. So we also have pretty great cross-community collaboration on trying to figure this whole thing out. >> Yeah, it often helps if we have examples to talk about to really explain this. Beth Cohen, we spoke with her last year and absolutely caught our attention. Got a lot of feedback from the community on it. Had Contron on earlier this week talking about, John was saying, here's some small device there with a little blade and is running pieces of OpenStack there to be able to run. Anything from the keynote or, boy, I think there's 40 sessions that you've got here. If you can, give us a couple of examples of some of the use cases that we're seeing to kind of bring this edge to reality. >> Example use cases is, we just heard this morning, for example, someone from the textile industry like how to detect issues with the fabric. So this is like one new manufacturing use case. I also heard another one, which is not checking the fabric itself, but basically the company who manufactures those machines that they are using to create the fabric, so they would like to have a central cloud and have it connected to the factories. So, being able to monitor how the machines are doing, how they can improve those machines, and also within the factory to monitor all the circumstances. Because for all the chemical processes, it's really important that the temperature and everything else is just, you know, clicks because otherwise all your fabrics will have to go to trash. So, that's manufacturing. A lot of telecom 5G, obviously that is really, really heavy because that's the part of the industry which is there today, so with 5G, all those strict requirements. This is really what we are mainly focusing on today. We are not specializing anything for telecom and in 5G use cases, but we want to make sure that all our components fit into that environment as well. In the white paper, for example, you also could see the retail use case. I'm not sure whether that will be exactly on stage this week, but that is also a great example on like Walmart with the lot of stores around, so how you manage those stores because they're also not wanting to do everything centrally. So, they would like to move the functionality out. What if the network connectivity is cut? They still have to be able to operate the store as nothing happened. So, there are a lot of segments of the industry who already have kind of really well-defined use cases. And what we see is that there's many overlapping between the requirements from the different segments that we're going to address. >> Are we seeing things like AI and ML coming up in these conversations also? >> Yes, like I think it was the manufacturing use case when I heard that they are planning to use that, and it's popping up. I think as far as our group is concerned, we are more looking into, I don't know, let's say lower-level requirements like how you maintain and operate the hundreds and thousands of edge sites, what happens with security, what happens with monitoring, what happens with all these sort of things. Like we have a new project rolling in under the foundation umbrella called Airship, which is basically deployment and lifecycle management, which is supposed to address one of the aspect that you were talking about on, okay, so how you manage this, how you upgrade this. And upgrade is, again, a really interesting question because I think I talked to someone yesterday who was like, yes, the Contron guys, they were saying that yeah, upgrade, it's really ambitious. So let say that maybe 18, 24 month or something like some kind of tech operator will decide to upgrade something out in the edge because it's out there, it's working, let's not touch this. So when we talk about upgrade, even that, I think, will depend on the bits of the industry that, what pace they will decide to take. >> Are there any particular surprises or learnings that you've had this year after talking with this community for a week now? You said, well, last year, I was very impressed last year when they got up on stage and talked about that. That kind of expanded my mind a little bit. You've been working with this now for a year, this whole track and forum sessions. Anything you're excited about taking to the future or learnings or surprises that, oh, this is really going to work or anything like that? Any parts of it that are really interesting? You talked about security upgrades. We've talked about a lot of the technical components, but it seems like it's working. >> I think at this point, at least on my end, I think I'm over the surprise phase. So what surprises me the most is how many groups there are out there who are trying to figure out what this whole edge thing is. And what we need to really focus on among the technical requirements is that how we are working together with all these groups just to make sure that the integration between the different things that we are all developing and working on is smooth. So like, we've been working together with the OPNFV community for a while now. It's a really fruitful relationship between us. Like seeing OpenStack being deployed in a full-stack environment and being tested, that's really priceless. And we are planning to do the same thing with edge as well, and we are also looking into ONAP, Aquino, Et-see-mac, so looking into the open source groups, looking into the standardization and really just trying to ensure that when we talk about open infrastructure, that that really is designed and developed in a way that integrates well with the other components. It's synchronized with the standardization activities because I think especially in case of edge, when we say interoperability, that's a level higher than what we call the interoperability on the telecom level I think. Like when you just imagine one operator network and applications from other providers popping up in that network, and components that just realizing the network popping up from different vendors. And this whole thing has to work together. So, I think OpenStack and open infrastructure has a really big advantage there compared to any proprietary solution because we have to address this, I think, really big challenge, and it's also a really important challenge. >> Ildiko, really appreciate you giving us all the updates here on the edge track, the keynote, definitely one of the areas that is capturing our attention and lots of people out there. So, thanks so much for joining us. >> Thank you for the opportunity. >> All right, for John Troyer, I'm Stu Miniman. Lots more coverage here from the OpenStack Summit 2018 in Vancouver. Thanks for watching theCUBE.

>> Announcer: Live from Vancouver, Canada, its theCUBE! Covering OpenStack Summit North America 2018. Brought to you by Red Hat, the OpenStack foundation, and its ecosystem partners. >> Welcome back, I'm Stu Miniman, and you're watching theCUBE's live coverage of OpenStack Summit 2018, in Vancouver. My co-host John Troyer is here, happy to welcome back to the program, Mark Baker who's a Product Manager with Canonical. Mark, how's the show treating you so far? >> Show's been going very well. So, we've seen people coming to us on the show floor, coming to the sessions. We're seeing really interesting building, scalable production Clouds, and so and coupling that with all the container technologies and a lot of other complimentary technology by machine-learning. So, a lot of the discussion is, can we build Cloud? But also, much more about the workloads and the kind of integration with, parallel if you like, or adjoining technologies. >> Great, want to talk about the customers really, Mark. So as you said, you've been to a few of these shows, we've been to a few of these also and, the makeup of the attendees has changed a bit, one of the things I heard, it is 2X the number of Cloud architects, with their title, compared just to last year, little bit of a broadening into the scope, what do you hear from customers, what brings them here, what's exciting them, in this environment? >> So, I mean yes certainly Cloud architects, and at Canonical we regularly talk to Cloud architects, because architecture with the Cloud is something that evolves, it's not something that's pinned. As workloads evolve, and new technologies come along you need to be able to evolve that architecture, and therefore people that understand that are important. I think it's also noticeable, I'm sat here wearing my blazer, is there's noticeable seeing quite a few people round the show, wearing blazers. So, you go back a couple years ago, or even a year or so ago, it was very much a sort of developer centric type of event. We're seeing more business conversations now, and even discussing things such as money, and economics, which weren't necessarily conversations that we were going too heavily in a couple of years ago. >> There's still a bunch of the hoodies set here, lots of cool T-shirts and, yeah, ironic facial hair and the like, so, maybe from your standpoint at Canonical, talk a little bit about those constituencies of who to sell with. We've got the operators, you've got the developers, you've got the C suite, I'm sure the answer is yes, but who you find yourself maybe, help walk us through some of those roles that you're talking to, some of the biggest concerns they're having and how you're helping them. >> So in most enterprises that we go and talk to we're typically talking to, initially operations, because they know that they need to be able to ride services to, Cloud services, and container services, to their customers internally, or within the business, and they're looking at okay how can we operate this, how can we secure it, how can we scale it, in smart ways, they're looking for our help and assistance doing that. Very soon after that we'll need to go and talk to developers, or engage line of business developers, primarily because we need to, this represents change to them, moving into a Cloud or, moving their applications to containers represents change, and we want to get them onboarded into this environment and to start to begin that change as quickly as possible. The Cloud, to succeed, it needs to have many running workloads on it, and so engaging with the developers, to take advantage of the capabilities the platform can provide is really important. We'd love to be able to go and talk to at that sea level, and we are starting to have more of those conversations, but I think the type of infrastructure, the OpenStack and container technologies provides, it's the initial interest is very much coming from those operators, from the architects, and from the developers. >> Well lets talk about operators for a minute, I mean, once upon a time there was a tribe of people called sisbits, they were kind of surly, and they took care of things like Linux, right, and now, out of that Linux framework, there's a huge set of technologies, that have grown all based on Linux, on all that Canonical works with, and there's a new set of skills required. Can you talk a little bit about what the new operator needs to know, and how you can help train people and Canonical help train people that you're assistant men working with Linux, what different things do I need to care about now in the Cloud management world, Cloud operator world? >> Yeah sure so, you're right, it used to be relatively simple, and you would run a VM or you'd run an application on top of bare metal and, there'd be certain things you'd need to be able to tweak to scale it and up the performance, but, we're running an, as we say, more agile infrastructure, so whether it's Cloud or containers or combinations of both, there are very many different variables, and how an application's able to take advantage of the storage or the capabilities that a platform provides, there's many different nobs and dials that you can turn. We tend to be advising right now, people on bringing in services such as CICD, Continuous Integration Continuous Deployment, so that they can start to adopt some of these newer ways of working. Operators now need to, they need to be much more aware of okay, what the workload characteristics are, and how that might behave on a hyper vise, or how it might behave within a containerized environment. I just came out of a conversation with a customer for example, who was asking detailed questions about storage performance, right? They have applications that require certain levels of storage performance and different types of storage that we can bring to bare, in conjunction with an OpenStack, which is going to be the appropriate one, and how do they segment them and so, it's definitely become more complex, but I think, through collaboration events like this, we're actually getting much better at being able to provide them with the information and the choices they need to make. >> Mark, speak to us a little bit about the community. OpenStack started heavy users in the community, contributed the community, how do you see that dynamic playing out today? >> Well there's still lots of contribution coming into OpenStack, and that's good to see. We are starting to see, as OpenStack has matured, as the market place has matured, some of the focus no longer being purely on contributing code, but now sharing experiences around operations, and that's starting to move into this area of people use this phrase, "Infrastructure as code", to be able to access infrastructure programmatically. I think we're seeing collaboration now in the OpenStack community and adjacent communities around collaborating on the operations, especially when those operations themselves are encapsulated in code. So, very simple thing, sounds simple, not necessarily easy to do but, being able to upgrade, update and place, how you would sort of suspend the system whilst you perform some maintenance and evacuating the workloads and bring them back in and those kinds of very common tasks for Cloud operators. We saw, even just a few years ago, how operators would each have their own way of doing it, their own preferred methods, and this was generally not so efficient so, collaborating on those and sharing best practices is one of the really interesting things to see within this community today. >> John: Sure, sure, I mean you, I think the evolution goes, everybody then starts to write scripts, which you all write scripts in your own way, and eventually you have to come up with a framework. And you all have developed a couple different frameworks in terms of installation and upgrades and things like that. >> Absolutely, and one of the things that once the customer start to understand that we've developed a framework around operations, those operations are encapsulated within code, and it means that if we have a customer, dodgy telecom, for example one of our customers that is understandably very security conscious, 'cause they run the telco network, has best practices around the security of their Cloud, and we're able, when they start to make recommendations or updates to that, we're able to take those and share them with a broad audience, and get that sort of collaborative spirit around what's the best way to be able to do this. >> So, you mentioned security there, any other kind of key pinpoints, what are you hearing out in the market place, is GDPR something that a lot of your customers are beaten on you and, what's the Canonical decision there? >> Yeah, absolutely, so, GDPR has been a real catalyst for people to look at areas for security that they probably meant to get round to at some point but never had, so. >> Some people said it's the Y2K of this generation >> Yes, exactly, definitely a forcing function. And so one of the areas we've seen a lot of activity around and solely we've committed resources to it within the last couple of months has been around encryption of data at rest. So, obviously in the Cloud, you're going to have a lot of data that's there with the relevant workloads, and some of that regulations in GDPR regulation is about what happens if somebody removes a disk from the server, does that mean that they have access to the data? As we start looking at things such as Edge Cloud, so very many Clouds close to the customer or close to the edge, which don't necessarily have the same data center infrastructure around them, how do we secure the data there, right? So, encryption of data, but doing it in a way that doesn't require to manually typed passwords in to be able to access them all of the time, is not a simple problem and, we've spent quite a few resources, working out how do we address that, how can we do it in a way that's going to allow it to be dealt with economically, and scalably. >> There's been a lot of talk about open infrastructure in general here at the show, and OpenStack obviously is designed to manage infrastructure, but we've already talked about containers here, with you in this segment, there's a lot of container news, Kubernetes news, OpenDev Summit going on at the same time, so how do you as a Product Manager, you can't just be worried about one part of the stack, how do you and your team worry about that integration and that unified platform and bring together these interactions will all these different OpenSource projects? >> Oh yes, for sure, and that's, it certainly is one of the things Canonical has been cognoscente on and focusing on, or working on for quite a long time is a Linux distribution at it's heart is really the integration of very many different components, from a kernel, and libraries, and pilots and all the various other pieces that go with that. So, understanding how these components plug together, whether it's OpenStack, with containers, and open V switch for the networking, and set for storage for example, that's very much part of what we've been doing. We're learning with customers as we go, very much, that how they want to plug these things together with Kubernetes, Kubernetes running alongside OpenStack, Kubernetes running on top of OpenStack, OpenStack even running on Kubernetes, some of them are looking at, so understanding how they, people want to be able to plug technologies together, and we'd standardized very much on sort of reference architectures of combination of OpenStack plus Kubernetes as a really simple example, but then as part of our QA process, testing process, all this reference architectures that we build with hardware partners and other partners too, is ensuring that we're able to deliver that as a stand-alone product as required, but also as effectively solutions together, that are fully integrated, fully supportable and they're going to deliver the capability that the customer needs. >> First of all, the OpenStack on top of Kubernetes, really? Is that something you'd recommend to customers or? Or is it a specific use case for that? >> It's not something that we recommend today. So, there's been certainly a lot of discussion in the OpenStack community around the control plane, and what's the best way to deliver the control plane. Canonical made a very strategic or specific choice several years ago that actually, containerizing the services is the right way to do this, so we containerized basically all of the control plane services apart from Neutron Gateway which would be a little tricky to do that but, so we containerized all of those services, and it gives us flexibility when we want to perform updates and migrate services between different systems, for example. How do you manage those containerized services though? There's lots of diversity of opinion. Some people want to be able to do that with Kubernetes, and that's great, then we certainly track those efforts and work with those people, if they're using a (mumbles) or some of our technologies, but I think, it's still yet to be decided, what's the best way to be able to do that. >> So you must, you have an interest in Java as a Product Manager, you always want to productize in general, standardize as much as possible, in the needs communities you have the diversity of opinions, oh I'll take this piece, I'll get rid of the core, I'll do something over here, I'll flip it upside down, how do you balance that, giving customers choice, but making sure you can deliver solid offerings that you can support? >> And so, that's very much it. It's a choice and we can say, look, we can deliver a robust, high performing Cloud, with these reference architectures, we've learned that through experience with customers, and working with our partners. We understand that customers all believe they're special and they all have their own special requirements, often with good valid reason, so, but we'll always try and start from a base, and then say let's start to iterate through that, adding in additional capabilities or, maybe tweaking something for your particular use case if you do that, and see how it impacts the Cloud. Because, for us to be successful, us, the OpenStack community to be successful, we need to ensure that those Clouds can live and breathe and evolve over time, and if they're making too many or too heavy customization of that Cloud, then it can start to impact their ability to do that. So, it's, we'll offer that choice. >> Speaking a little bit on the line of standardized services, I'm really intrigued by managed OpenStack, from Canonical. Can you talk a little about what customers it's right for, and when it comes into the conversation and then where in the lifecycle, 'cause I guess then it can also eventually go as as the control container back over to the customers when they don't, when they're finished with managed. >> Absolutely, so we started providing what we call boot stack, as fully managed OpenStack service, primarily to address the skills gap within the OpenStack community. So, we saw a lot of companies interested in deploying OpenStack, a lot of enterprises looking for OpenStack, but they couldn't find the talent, or the people with the experience of deploying a managing OverStack. Just, there weren't the people around, right? Hiring was hard. So, and that was becoming a blocker for us to be able to deliver Clouds to those customers, so we started to offer a managed service, we had a lot of the reference architectures and best practices pretty well nailed down, but it was a facilitator for them to get up and running with the Cloud and there's a point where they, that they became comfortable operating it, managing themselves, hand control back. We've seen, that is a very popular model, and that period where they're having us manage it, can be six months or 12 months or 18 months, but the customers know that they have the reassurance that they can take it back, control and house, they can operate it themselves, and they can manage their own environment, they become self sufficient, but they're not doing that from day one. We're holding their hand, and taking them along that path. So, that's been a very popular offer. >> Mark Baker, really appreciate you giving us an update on really the broad spectrum of customer use cases and all the updates from Canonical. For John Troyer, I'm Stu Miniman. Back with more coverage here from the OpenStack Summit 2018, in Vancouver. Thanks for watching theCUBE. (electronic music)

(soft electronic music) >> Announcer: Live from Vancouver, Canada, it's theCUBE. Covering OpenStack Summit North America 2018. Brought to you by Red Hat, the OpenStack Foundation, and it's ecosystem partners. >> Welcome back, I'm Stu Miniman here with my cohost John Troyer and you're watching theCUBE's exclusive coverage of OpenStack Summit 2018 in Vancouver. Happy to welcome you back to the program, off the keynote stage this morning, Mark Shuttleworth, the founder of Canonical. Thank you so much for joining us. >> Stu, thanks for the invitation. >> Alright, so you've been involved in this OpenStack stuff for quite a bit. >> Right, since the beginning. >> I remember three years ago we were down in the other hall talking about the maturity of the platform. I think three years ago, it was like this container thing was kind of new and the basic infrastructure stuff was starting to get, in a nice term, boring. Because that meant we could go about business and be on the buzz of there's this cool new thing and we're going to kill Amazon, kill VMware, whatever else things that people thought that had a misconceived notion. So bring us forward to where we are 2018, what you're hearing from customers as you look at OpenStack and this community. >> Well, I think you pretty much called it. OpenStack very much now is about solving a real business problem, which is the automation of the data center and the cost parody of private data centers with public data centers. So I think we're at a time now where people understand the public cloud is a really good thing. It's great that you have these giant companies dueling it out to deliver better quality infrastructure at a better price. But then at the same time, having your own private infrastructure that runs cost-effectively is important. And OpenStack really is the only approach to that that exists today. And it's important to us that the conversation is increasingly about what we think really matters, which is the economics of owning it, the economics of running it, and how people can essentially keep that in line with what they get from the public cloud providers. >> Yeah, one of the barometers I use for vendors these days is in this multi-cloud world, where do you sit? Do you play with the HyperScalers? Are you a public cloud denier? Or, like most people you're, most people are somewhere in-between. In your keynote this morning, you were talking a bit about all of the HyperScalers that use your products as well as-- >> Ubuntu is at the heart of all of the major public cloud operations at multiple levels. So we see them as great drivers of innovation, great drivers of exposure of Ubuntu into the enterprise. We're still, by far, the number one platform used in public cloud by enterprises. It's hard to argue that public cloud is testing Dev now. It really, really isn't and so most of that is still Ubuntu. And now we're seeing that pendulum swing, all of those best practices, that consumption of Ubuntu, that understanding of what a leaner, meaner Enterprise Linux looks like. Bringing that back to the data center is exciting. For us, it's an opportunity to help enterprises rethink the data center to make it fully automated from the ground up. OpenStack is part of that, Kubernetes is part of that and now the cherry on top is really AI where people understand they have to be able to do it on public cloud, on private infrastructure and at the Edge. >> Mark, I wanted to talk about open source. Marketing open source, for a minute. We are obviously here, we're part of an open source community. Open source, defacto, has won the cloud technology stack wars. So there's one way of selling OpenStack where you pound on open a lot. >> I'm always a bit nervous about projects that put open. It sounds like they're sort of trying to gloss over something or wash over something or prove a point. They shouldn't have to. >> There's one about the philosophy of open source, which certainly has to stay there, right. Because that's what drove the innovation but I was kind of impressed about on the stage today, you talked about the benefits. You didn't say, well the venture's open. You said, well, we're facilitating these benefits. Speed to market, cost, et cetera. Can you talk about your approach, Canonical's approach to talking about this open source product in terms of its benefits? >> Sure, look, open source is a license. Under that license, there's room for a huge spectrum of interest and opinions and approaches. And I'd say that I certainly see an enormous amount of value in what I would call the passion-based open source story. Now, OpenStack is not that. It's too big, too complicated, to be one person's deep passion. It really isn't. But there's still a ton of innovation that happens in our world, across the full spectrum of what we see with open source, which is really experts trying to do something beautiful and elegant. And I still think that's really important in open source. You also have a new kind of dimension, which is almost like industrial trench warfare with open source. Which is huge organizations leveraging effectively their ability go get something widespread, widely adopted, quickly and efficiently by essentially publishing it as open source. And often, people get confused between these two ends of the spectrum. There's a bunch in between. What I like about OpenStack is that I think it's over the industrial trench warfare phase. You know, you just don't see a ton of people showing up here to throw parties and prove to everyone how cool they are. They've moved on to other open source projects. The people who are here are people who essentially have the real problem of I want to automate my data center, I want to have, essentially, a cloud that runs cost-effectively in my data center that I can use as part of a multi-cloud strategy. And so now I think we're in to that sort of, a more mature place with OpenStack. We're not either sort of artisan or craftsmen oriented, nor are we a guns blazing brand oriented. It's kind of now just solving the problems. >> Mark, there's still some nay-sayers out in the marketplace. Either they say that this never matured, there's a certain analyst firm that put out a report a couple of months ago that, it kind of denigrated what's happening here. And then there's others that, as you said, off chasing that next big wave of open source. What are you hearing from your customers? You've got a good footprint around the globe. >> So that report is nonsense, for a start. They're always wrong, right. If they're hyping something, they're wrong and if they're dissing something then they're usually wrong too. >> Stu: They have a cycle for that, I believe. (chuckling) >> Exactly. Selling gold at the barroom. Here's how I see it. I think that enterprises have a real problem, which is how do they create private cloud infrastructure. OpenStack had a real problem in that it had too many opinions, too many promises. Essentially a governing structure not a leadership structure. Our position on this has always been focus on the stuff that is really necessary. There was a ton of nonsense in OpenStack and that stuff is all failing. And so what? It was never essential to the mission. The mission is stand up a data center in an automated way, provide it, essentially, as resources, as a service to everybody who you think is authorized to be there, effectively. Segment and operate that efficiently. There's only a small part of OpenStack that was ever really focused on that. That's the stuff that's succeeding, that's the stuff we deliver. That's the stuff, we think very carefully about how to automate it so that, essentially, anybody can consume it at reasonable prices. Now, we have learned that it's better for us to do the operations almost. It's better for us actually to take it to people as a solution, say look, explain your requirements to us then let us architect that cloud with you then let us build that cloud then let us operate that cloud. Until it's all stable and the economics are good, then you can take over. I think what we have seen is that you ask every single different company to build OpenStack, they will make a bunch of mistakes and then they'll say OpenStack is the problem. OpenStack's not the problem. Because we do it again and again and again, because we do it in many different data centers, because we do it with many different industries, we're able to essentially put it on rails. When you consume OpenStack that way it's super cheap. These aren't my numbers, analysts have studied the costs of public infrastructure, the cost of the established, incumbent enterprise, virtualization solutions and so on. And they found that when you consume OpenStack from Canonical it is much, much cheaper than any of your other options in your own private data center. And I think that's a success that OpenStack should be proud of. >> Alright, you've always done a good job at poking at some of the discussions happening in the industry. I wouldn't say I was surprised but you were highlighting AI as something that was showing a lot of promise. People have been a little hot and cold depending on what part of the market you're at. Tell us about AI and I'd love to hear your thoughts in general. Kubernetes, Serverless, and ask you to talk about some of those new trends that are out there. >> Sure, the big problem with data science was always finding the right person to ask the right question. So you could get all the data in the world in a data lake but now you have to hire somebody who instinctively has to ask the right question that you can test out of that data. And that's a really hard problem. What machine learning does is kind of inverts the problem. It says, well, why don't we put all that data through a pattern matching system and then we'll end up with something that reflects the underlying patterns, even if we don't know what they are. Now, we can essentially say if you saw this, what would you expect? And that turns out to be a very powerful way to deal with huge amounts of data that, previously, you had to kind of have this magical intuition to kind of get to the bottom of. So I think machine learning is real, it's valuable in almost every industry, and the challenges now are really about standardizing underlying operations so that the people who focus on the business problems can, essentially, use them. So that's really what I wanted to show today is us working with, in that case it was Google, but you can generalize that. To standardize the experience for an institution who wants to hire developers, have them effectively build machine-driven models if they can then put those into production. There's a bunch of stuff I didn't show that's interesting. For example, you really want to take the learnings from machine-learning and you want to put those at the Edge. You want to react to what's happening as close to where it's happening as possible. So there's a bunch of stuff that we're working on with various companies. It's all about taking that AI outcome right to the Edge, to IOT, to Edge Cloud but we don't have time to get in to all of that today. >> Yeah, and Ubuntu is at the Edge, on the mobile platform. >> So we're in a great position that we're on the Cloud. Now you see what we're doing in the data center for enterprises, effectively recrafting the data center has a much leaner, more automated machine. Really driving down the cost of the data center. And yes, we're on the higher-end things. We're never going to be on the LightBulb. We're a full general-purpose operating system. But you can run Ubuntu on a $10 board now and that means that people are taking it everywhere. Amazon, for example, put Ubuntu on the DeepLens so that's a great example of AI at the edge. It's super exciting. >> So the Kubernetes, Serverless-type applications, what are your thinkings around there? >> Serverless is a lovely way to think about the flow of code in a distributed system. It's a really nice way to solve certain problems. What we haven't yet seen is we haven't seen a Serverless framework that you can port. We've seen great Serverless experiences being built inside the various public clouds but there's nothing consistent about them. Everything that you invest in a particular place is very useful there but you can't imagine taking that anywhere else. I think that's fine. >> Stu: Today's primarily Lando. >> And I think the other clouds have done a credible job of getting there quickly. But kudos to Amazon for kind of pioneering that. I do think we'll see generalized Serverless, it just doesn't exist at the moment and as soon as it does we'll be itching to get it into people's hands. >> Okay, yeah? >> Well, I just wanted to pull out something that you had said in case people miss it, you talked about managed OpenStack. And that, I think, managed Kubernetes has been a trend over the last year. Managed OpenStack now. Has been trans-- >> With these complex pieces of infrastructure, you could easily drown in learning it all and if you're only ever going to do one, maybe it makes sense to have somebody else do it for a while. You can always take it over later. So we're unusual in that we will essentially standup something complex like an OpenStack or a Kubernetes, operate it as long as people want and then train them to take over. So we're not exclusively managed and we're not exclusively arms-length. We're happy to start the one way and then hand over. >> I think that's an important development, though, that's been developing as the systems get more complicated. One UNIX admin needs a whole new skill set or broader skill set now that we're orchestrating a whole cloud so that's, I think that's great. And that's interesting. Anything else you're looking forward to, in terms of operation models. I guess we've said, Ubuntu everywhere from the edge to the center and now managed, as well. Anything else we're looking at in terms of operators should be looking at? >> Well, I think it just is going to stay sort of murky for a while simply because each different group inside a large institution has a boundary of their authority and to them, that's the edge. (chuckling) And so the term is heavily overloaded. But I would say, ultimately, there are a couple of underlying problems that have to be solved and if you look at the reference architectures that the various large institutions are putting out, they all show you how they're trying to attack these patterns using Ubuntu. One is physical provisioning. The one thing that's true with every Edge deployment is there are no humans there. So you can't kind of Band-Aid over the idea that when something breaks you need to completely be able to reset it from the ground up. So MAAS, Middle as a Service, shows up in the reference architectures from AT&T and from SoftBank and from Dorich Telecom and a bunch of others because it solves their problem. It's the smallest piece of software you can use to take one server or 10 servers or 100 servers and just reflash them with Windows or CentOS or whatever you need. That's one thing. The other thing that I think is consistently true in all these different H-Cloud permutations or combinations is that overhead's really toxic. If you need three nodes of overhead for a hundred node OpenStack, it's 3%. For a thousand node OpenStack, it's .3%. It's nothing, you won't notice it. If you need three nodes of OpenStack for a nine node Edge Cloud, well then that's 30% of your infrastructure costs. So really thinking through how to get the overhead down is kind of a key for us. And all the projects with telcos in particular that we're working, that's really what we bring is that underlying understanding and some of those really lightweight tools to solve those problems. On top of that, they're all different, right. Kubenetes here, Lixti there, OpenStack on the next one. AI everywhere. But those two problems, I think, are the consistent things we see as a pattern in the Edge. >> Alright, so Mark, last question I have for you. Company update. So last year we talked a little bit about focusing, where the company's going, talked a bit about the business model and you said to me, "Developers should never have to pay for anything." It's the governance people and everything like that. Give us the company update, everything from rumors from hey, maybe you're IPO-ing to what's happening, what can you share? >> Right, so the twin areas of focus, IOT and cloud infrastructure. IOT continues to be an area of R and D for us so we're still essentially underwriting an IOT investment. I'm very excited about that. I think it's the right thing to be doing at the moment. I think IOT is the next wave, effectively, and we're in a special position. We really can get down, both economically and operationally, into that sort of small itch kind of scenario. Cloud, for us, is a growth story. I talked a little bit about taking Ubuntu and Canonical into the finance sector. In one year, we closed deals with 20% of the top 20 banks in the world to build Ubuntu base and open infrastructure. That's a huge shift from the traditional dependence exclusively on VMware Red Hat. Now, suddenly, Ubuntu's in there, Canonical's in there. I think everybody understands that telcos really love Ubuntu and so that continues to grow for us. Commercially, we're expanding both in Emir and here in the Americas. I won't talk more about our corporate plans other than to say I see no reason for us to scramble to cover any other areas. I think cloud infrastructure and IOT is plenty for one company. For me, it's a privilege to combine that kind of business with what happens in the Ubuntu community. I'm still very passionate about the fact that we enable people to consume free software and innovate. And we do that without any friction. We don't have an enterprise version of Ubuntu. We don't need an enterprise version of Ubuntu, the whole thing's enterprise. Even if you're a one-person startup. >> Mark Shuttleworth, always a pleasure to catch up. Thank you so much for joining us. >> Mark: Thank you, Stu. >> For John Troyer, I'm Stu Miniman. Back with lots more coverage here from OpenStack Summit 2018 in Vancouver. Thanks for watching theCUBE. (soft electronic music)

>> Narrator: Live from San Juan, Puerto Rico It's theCUBE. Covering Blockchain Unbound. Brought to you by Blockchain Industries. (latin music) >> Hello and welcome back to theCUBE exclusive coverage in Puerto Rico for Blockchain Unbound I'm John Furrier, your host, here covering all the action in Puerto Rico as the global society and industry come together. Our next guest is Marhall Taplits he's the Chief Strategy Officer and Co Founder of Nynja.biz, check out their site, Nynja.biz. Marshall, thanks for joining me. >> Thank you. >> So tell about what you guys do. You guys are doing some disruptive stuff, tell us about what you guys do, then it will jam into a conversation. >> Sure, so are you familiar with WeChat in China, for example? >> Yeah. >> Okay great. So I've personally been living in China 15 years, so we've watched kind of the birth of the Chinese internet, which as we know, is a little different than the regular internet. >> A lot of mobile users. >> A lot of mobile users, 800 million China mobile subscribers alone. WeChat, basically, is a platform that started off as just a messenger but basically what it's done is it's integrated into every facet of Chinese society. To give you an example, you go to a restaurant, you scan the QR code, the menu comes up, you pick the food, you pay for the food, it comes, you walk out. Everything like that is in China. Everything like that is in Wuzhen China. So what we've done is we've kind of taken this concept, and we're working on a global version of it, that's cryptocurrency based, and we are working specifically with Chinese companies in order to help them go global as part of the China One Belt One Road program and working with companies like Alibaba, what have you, in order to help Chinese companies go overseas and take what they've built in China but operate globally with cryptocurrency. >> Are you guys in China? Cause it's been hard for companies to start companies in China. So you're living in China or you're working in China? >> Yeah so because we live in Shenzhen, right next to it is Hong Kong. Hong Kong is where our company is based. Hong Kong, as you know, previous British colony, the legal system, and the financial system-- >> And you domicile in Hong Kong, that's where you're based? >> Me personally in Shenzhen, but the company is in Hong Kong. So we also have a Wyoming corporation in the US. >> That's where all the action is. >> That's right >> That's where WeChat is >> That's right >> Alibaba's got Alipay and then there's more business to business with their app. So I get that WeChat's been highly successful. In fact we have a huge following on WeChat, Sou Kanai, Niki Bond, free content. But that brings up the question of Chinese kind of showing the way with mobile expansion, so their users are heavily mobile savy. >> Marshall: That's right. >> This is pretty obvious when you think about it, but in America and around the world, that's going to translate to the new user experience. So in your opinion, how would you describe the expectations that users have? Because you're living on the front end of the wave of what mobile's doing, I mean there's a lot of gamification going on, some if it's kind of creepy, but what is your view of the expectations that users have and what's different about what's currently available in the webstac, and the 20 year old e-commerce stacks, that are out there? >> Sure, I think the most important thing is reducing friction, all right. You don't want to be using platforms where you can not do it wherever you are whenever you are, you don't want to have to go through payment processes, you don't want to have to re-authenticate yourself across whatever platforms you use. And interestingly, when I first went to China, it was all about copying what was in the west over to there, but actually it's kind of the opposite now, right, so we basically want to take this concept of the frictionless digital life, and make it a global opportunity. And especially with BlockChain and cryptocurrency you have that really as an opportunity, because if you look at all the apps that are out there, and the platforms that are out there, the only ones that have gone past a billion users, WhatsApp, Instagram, whatever are the free ones. But as soon as you layer in payment, it becomes very locked. And as big as WeChat is, and as big as LINE is, but ultimately it's locked into the Rem and B system or Reo in Korea, what have you, so the cryptocurrency is really the first opportunity that the world's had to create platforms that can get up to a billion, two billion, three billion users that are able to pay. And we just think that's a once in a lifetime opportunity and we want to be part of it. >> So I got to ask you about the impact that cloud computing has had on this, obviously we've seen cloud computing destroy the data center model, allow people to get time to value faster, mobile on top, big data analytics using data, all this stuff's awesome stuff. So the question is, is that, that's kind of a horizontally disruptive view, so these stacks that are built old way where I got to own the stack end to end, yeah there's some standardization on the lower end of the stack. But now you're thinking about more of a horizontal, I got jurisdictions, I got regions, I got countries, I got sovereignty, all these things are in the melting pot of the cryptocurrency BlockChain, de-centralized applications, are major impacts to all those things. How do you see that playing out because, that's kind of what developers worry about, oh shit will this work on that chain? I got Neo I got this I got that, so the plumbing is totally a moving train right now. >> Marshall: That's right. >> But the business models are pretty obvious. So there's like a business ops thing going on. What Dev opts did for Cloud, you got this new abstraction thing going on with this world. What's your view on that, do you agree? Or what's your take? >> Yeah well you pretty much nailed it. I mean basically what's happening is over the last 10 or 15 years people have finally accepted that having your own server is kind of silly, you know, and most people now will just spin up whatever they need in terms of resources on TheCloud. But over the last couple years, you're really going more toward Edge Cloud, where the way the clouds work, is that basically it's pushing to get the least amount of latency and store the data as close to the user as possible. And then there's also regulatory in some countries now in terms of, if your users are from this country, you have to legally store the data in this area. So this is all kind of evolving. And if you look at the BlockChain technology, I think it's the payment version of that. So for example, everyone's always concerned about getting in and out of Fiat Currency, and how am I going to get back to dollars, and this and that, but I think what's going to wind up happening, is this is going to get pushed towards the edges and there will be opportunities and ways with exchanges and what have you to get in and out. But more importantly, it's going to be like, just other currencies, so for example, I live in China but I come to the US a few times a year, I also travel to Europe, I have some dollars, I have some Euros, I have some Rem and B, when I leave China, I don't immediately sell all of my Rem and B, I just keep it because at some point I'm going to need it. And I think what's going to happen in the cryptocurrency space is, especially on the larger BlockChains, like Ethereum and Neo and what have you, is people are just going to get used to keeping some of it and they're going to stop worrying about what the exact exchange rate is and how am I going to get in and out, and this and that, and they're just going to start treating it as part of their currency stack that they keep. >> Yeah as long as there's some level of stability. It's just like, I remember when I was growing up, there was no Euro, every country had their own currency. You had the French Franc, the Swiss Francs, the Deutsche Mark, Lira, etc, etc. But you're seeing that the viability of the money aspect, cause at the end of the day there's two things that we've identified in analysis, and I was talking about it last night, talked about it this morning on theCUBE, is the killer apps for BlockChain cryptocurrency, these sorts of apps is two things, money and marketplaces. >> Marshall: That's right. >> Everything else is just kind of circling around those two. >> Well there's more but certainly that's the main part of it >> Money, moving around. So the UK just announced with coin based, the Financial Conduct Authority, reading the news yesterday, has essentially said we're going to allow for the fast payment system to convert to Fiat. This is a government, the UK is a nation. This is the beginning, to your point, that if they don't get up to speed, the edge of the network will democratize them and kind of circle the wagons, if you will, so it's already happening. >> Yeah and I think what governments are starting to realize is hey guys this is just a technology and not only do you don't really have jurisdiction to control it, but also that you don't even have the technical means. So Wyoming is a good example of regulation coming into play, that just kind of accepts the presence that this now exists, right. And they're not going to try to make it something and fit it into the old way. So, and in terms of the stability of these coins, I think it is important because people want stability, but in other ways, if you don't look at the exchange rate, it's actually way more stable than the current system, and I'll give an example. In the last month or two, the prices of cryptocurrency have dropped almost 40%. Now if the stock markets and the global affects markets drop 40%, you'd have blood in the streets. But the crypto market is asset based instead of debt based and because it's so structurally sound it's able to handle these wild swings without actually collapsing the system, so in may ways, it's way more stable, and then as the market gaps and the buy in of these currencies get bigger and bigger, of course it's going to be more stable over time. >> Well I mean its stable from a fail standpoint, but a lot of emotional instability. People losing money for the first time. >> But that's just because they're-- >> That's a lot of speculation, right? >> There's a lot of speculating and then if they're down they feel like they lost but, that's life. >> People that are into the game, like you, were long on this. So what would you explain to someone, cause I have two, a lot of friends that have two schools of thought, that's a total scam, don't associate with that, to oh my god, that's the next biggest wave, lets get our surfboards out there and lets get on this, there's a multiple set coming in, it's the biggest thing we've seen, and everything in between. How do you explain it to people for the first time? >> It's just your traditional curve, there's early adopters and what have you, and if you were one of the guys buying up domaine names in the early 90s, you know some people would say I can't believe you're spending $100,000 buying up domaine names, but some of them now are worth, you know, tens of millions of dollars. But again, this is the speculatory piece of it. And there's no shortage of opportunities for speculation and I encourage everybody to speculate a little bit because what it does is it gets you a taste of the technology. And usually, when you have some money on the line, you pay more attention, so if speculation is what gets people interested, and it gets them watching it and understanding the technology and using it, then I'm all for it, but people shouldn't be speculating with money they don't have. Anything could happen in the short term. Nobody knows what's going to happen with any specific currency. But in terms of the technology itself, this is a revolution way bigger than the internet itself. This is where you're getting, not only, communications like the internet, but financing governance and all as one. Programmable money, programmable contracts, that wipes out finance, it wipes out legal, it whites out governance in many ways. So this is a huge evolution in human society, and we've termed this Open Unity actually. And so we believe that society has to reach a state of open unity in order to go into the singularity as we would envision it wanting to be, as something that's under our control. >> Yeah and I think one of the things, first of all that's a great statement, well said. I'll just kind of put some reality on that, connect the dots, is that if you look at the trajectory of cloud computing, Amazon Web Services was laughed at years ago. S3 came out, compute storage building, basic building blocks and a slew more services. What Cloud did for software developers, and what they've disrupted from a business standpoint, dev ops, it's proven. What open source has done, even going back to the old red-hat days and linux, is that now a tier one global citizen in software, you look at those two trends, you can connect that dots to what you just said. And what made Cloud great was they made application developers have access to programmable infrastructure. >> Marshall: Exactly. >> You're talking about a whole nother level of software programmability, money, marketplace, society, >> Yeah you hit it on the head. >> We're there right? >> That's exactly right, so when a programmer wants to start a business, instead of going to create an LLC, and getting their EIN Tax ID or whatever, and when they want to go into Europe, and dealing with that and then trying to open a bank account, which is almost impossible, internationally now, instead of that, you just have your SDKs and your APIs or whatever and you've got access to money, program adding, you can take money, you can move money around, globally, frictionless, permissionless, with governancy, smart contracts-- >> They might not not need an SDK dashboard, its a console, click, click, click, smart contracts, governance, turn key. >> And one of the things we're working on with Nynja in particular, is this kind of on-demand marketplace and putting together a de-centralized teams for work. And this is all driven by smart contracts. So one of the issues with the economy is the huge booms and busts that people have in the economy. And if you look at the root cause of that, my personal opinion, is that it's because of payment terms. So for example, if I do work for you, and then there's an invoice, but it's not due for 30 days, now your business may be structurally sound, but the truth is your cashflow is all over the place. With BlockChain technology, we can actually do real time payments. You could be paid minute by minute, hour by hour. Real time, program, contract. So we're going to create very flat even money flows through the entire economy globally, and we're going to just completely remove these booms and busts that are really nothing more than just cashflow issues that are compounded and compounded at a global level. >> I mean I lived through the dot com bubble, I was actually part of it on the front end, on the euphoria side, as well as on the crash. Part of the whole search paradigm, google right there. Key words, all that stuff happening, growth, massive growth. So I saw that, the scammers in there, or the bubble people, that's what we called them. But the reality is, everything happened. It was pet foods online, you could get shopping delivered to your house. So again, to your point, it's a little euphoric right now, but what's different is, is you have now, community data. See what I see happening is, it's not a major bubble crash, because self government, self governing, self governance, is a community dynamic. So I think there's going to be a lot of self healing, inside the networks themselves. You're already seeing it here, a lot of people, bad act is being identified, investors flight to quality, looking at quality deals. Interesting times, your thoughts? >> Well I mean you know, we've been through many evolutions of society, we've had surf-dom, we've had monarchies, we've had representative democracies, we have all these things, and I just think the next evolution is decentralized governance. And we don't even know what that means yet, because it's just starting, but I think we can all, if we can close our eyes and really think about it. I think it's pretty obvious what the issues are with our current system and not just the US, but globally, and I think we have an opportunity here to build in organic program governance. And what's really special about BoxChain technology is if I program it to do X, it's going to do X. So we don't need to, I don't need to know who you are to trust you. I don't need to worry about where we're going to sue each other, or we're going to have arbitration if things go wrong. We're just going to make an agreement, and we're going to program it that way, and that's it. And now the next phase is, I could build on top of that trusting that that's just going to happen. So you can create these chains of trust, and that can happen anywhere in the world. So I think this is a whole nother-- >> Sounds like a bunch of web services. >> Well in many ways, in terms of the architecture, sure you could absolutely think of it like that. >> The reusability, the leverage is amazing. All right, so I want to just end the segment Marshall, take a minute to end the segment, to talk about what you're working on, Nynja coin, Nynja, N-Y-N-J-A .biz, you guys have a product, you got a BlockChain enabled platform, you got a coin, take a minute to explain what you're working on. >> Basically we want to provide the tools and services to help people live in this new reality. So in order to basically function in the world that we're entering into, we're going to need tools that far surpass what's currently available in terms of the messengers, the web sites, all these things. We need to be operating at a level that takes communication completely frictionless, payment completely frictionless, and governance completely frictionless. And we have to put this all together, and that's what we're doing with Nynja. We're staring with a global communicator, which is basically, if you want to take WeChat, telegram, whatever, but we have about 50 additional features that really take communications to the next level. And then on top of it, creating the baseline with cryptocurrency payment, and also smart contract wizards and helping people kind of get these teams going and get paid and organize their financial life in a de-centralized way. So we're just basically going to be the next generation of these messenger type platforms with BlockChain integrated. And what you're going to see is that over the next couple years you're going to get to the first companies that are achieving not just a billion or two billion or three billion users, but paying users, and we're going to be one of the probably three to five platforms that are offering tools at the global level like this. >> And have you got an IC already or not? >> We've just started our private ICO about two weeks ago. We're getting tremendous support in Asia. Quite frankly, the US is not seeing it as much-- >> Is it a utility token or security? >> Utility Token, and I think it's really telling, interesting, coming here. It's the first time I've been doing the presenting. We spoke yesterday at the d10e and we also spoke at d10e in Korea a week or two ago, and the response is incredible. And I think the reason is because-- >> The Asian market gets it. >> Well they're already living in this world within their own confines in terms of the messenger with their payment and governance built in, so when I tell them that we're going to do this globally with crypto, immediately they get it. I'm having trouble here, especially in these five minute pitches which is ridiculous, it's like a chop shop, I don't know how to communicate the idea within this short time frame, so, what I'm looking for while we're here this week is just to find people who really want to take an hour or two or even people like yourself who want to do interviews and just kind of really talk to people and really explain-- >> Well platform is complex, a lot of pieces to it. It's a system, but the value you offer is essentially offering developers, who are building products, for tools that you've built so they can scale faster. That sounds like your value. >> That's right and although I can't say specifically, we're also working on a deal that's going to get us started with about 15 million active users on day one, so that's very exciting and we're really really excited about that. >> And the coins will be utility of measures, what? >> Sorry? >> Well your utility coins going to be measuring what, what's the main token economics that drives the-- >> For the ICO economics? >> Your Nynja Coin. >> So basically we're releasing 5 billion tokens, 45% of them will be sold. There's five cents a token, so the hard cap, by definition is about 112 million, actually we're planning to do the public sale in April, but we may cancel it or postpone it just because the private sale is going really well, but we'll see how that goes. But in terms of once it's live, this will basically be the utility token of the entire eco-system, so anybody, not just within our Nynja App or platform, but even people, I don't know if you know XMPP federation, like back in the day-- >> Yeah you know about real messaging >> If you could think of us as the next version of XMPP federation, but using cryptocurrency in order to avoid bad actors by making it very expensive to do bad things, and very cheap to do good things and globally. >> So it's like Twitter you can create a bot instantly, but if there's coins involved, you'd have to spend to get it. >> That's right and also people could spin up nodes that are basically their own Twitters and decide if those Twitters of their own, their Nynja boxes of their own, are either just internally, or you could specify specifically context or group of context-- >> We agree, that's a great way to get bad actors out because it costs them money. And it's de-centralized, there's no single spot. >> That's right, if email came out today, when cryptocurrency existed, there would be no spam. Because it would be expensive as hell to send more than a few a second, but it would still be free and for everybody generally, and you wouldn't even have spam. So we think we can do that for messaging globally. >> Great. Marshall, thanks so much for coming on theCUBE, really appreciate it, check out Nynja. Marshall Taplits is the Chief Strategy Officer and co-founder of Nynja.biz, check them out online. Check out the website, it's in Asia, bringing that culture of mobile and fast moving, real time apps, to the rest of the developers. This is theCUBE coverage in Puerto Rico for BlockChain Unbound exclusive two days of coverage. We'll be right back with more, after this short break, thanks for watching.

>> Announcer: Live from Washington, D.C. It's theCUBE covering DotNext Conference. Brought to you by Nutanix. >> Welcome back to the district everybody, I'm Dave Allante with Stu Miniman, and this is theCUBE, the leader in live tech coverage. We go out to the events, and we Extract the Signal from the Noise. We're here, this is day two of the Nutanix.NEXTConf, #NEXTConf, Chris Hsu is here, sorry Calvin Hsu is here, VP of Product Marketing at Citrix. Welcome to theCUBE. >> Thank you very much, nice to be here. >> So, you're up on stage earlier today right? A lot of good action here at the show. Talk about Citrix, and what you guys are doing here. >> Yeah, so I think Citrix, Nutanix, we've had a partnership going back for quite awhile. I think what really brought us together were customers that were actually trying to solve this issue, of how do I implement VDI, and how do I do this better right, there has to be a better way. And it's funny, we were just talking about chatting a little bit before about how many different infrastructure pieces and how many different components there are to learn in order to do VDI, and that was one of the things that always kind of stood as a barrier to adoption in some of the early days, going back, I don't know several years now, and they would say, well, you got to have, be an expert in networking, you got to be an expert in storage, you got to know all the server side infrastructure, the virtualization that goes with it, and then you got to also know the desktops, and the app parts of it, and how to manage all that. And in my experience it was all that technical knowledge, but it was also, it was also the people right? So, you also had to bring those people to the table, have one VDI project, go in and talk to a customer, and we're going to do a pilot for 200 people to start, and there'd be 20 people in the room. Because everybody had different areas of responsibility. And so as Nutanix is involved, and the whole idea of hyper-conversion, and HDI that's come around, that's really been some of the basis of where VDI is kind of getting that second booster of, in it's life cycle here, where they're realizing that it could just be a few people that are responsible for that HDI infrastructure, can deploy the VDI, and now they have a more simple reliable way of implementing that solution so (mumbles). >> I mean, that's kind of where, even when I go back to the converged infrastructure world that's, VDI was the one like foothold use case with Vblock's in the early days, and the HPE stuff, or HP then, and you know I have to say, I have to ask both of you guys, because you know this business really well, and you're obviously a VDI expert but, when you talk to customers, they get really excited about VDI, they're like, "Hey, this is a great use case, "we're going to, we're doing VDI, VDI, VDI, "it was a big project effort." When you talk to the analysts they're like, "Uhhh, VDI is so boring." What is it about VDI that there's this bifurcated opinion base right? Analysts uhhhh, okay, but customers eat it up. What's going on, what...? Unpack that for us. >> Well, I mean analysts don't necessarily feel the day-to-day pain of managing a desktop right? That's what it is right, so for them it's a-- >> Well said. >> It's the truth. Well, actually I know, I know some analysts that actually did that job, and so they're the ones that are still excited about it right? But in general, like once you get past the idea of that consulting a client on the complexities, and how do you choose a vendor and, and then it comes down to a few basic things, it's which one's going to deliver the best employee experience with the solution, which one's going to be the best operationally to manage and then sort of their job is done. But then, from a IT Admin perspective it's like they're still, every day they're managing new application update, the new desktop image, and it doesn't end right? And that's dozens and dozens of hours out of every week, every month, that you spend. >> Alright let's hear from the analyst. >> Dave, it was called VDI fatigue. Every year was the year of VDI you know. I think we've gotten beyond that, because I tell you, from my viewpoint, it was wait. It was this mess of a stack, and we're going to fix that. Oh wait, now storage is the mess, now flash is going to solve that, oh wait, mobile adoption is you know, the barrier, yet the opportunity, how do we modernize our applications, the changing workforce, mobile workforce. There were always the next, the next, the next, the next, the next thing and, it reminds me of our conversations with (mumbles) you know, it was like we're never finished, and a lot of it was, it was this big category of you know, you talk about the user experience, is I think, what Citrix is focused on, and how do we make that simpler and you know, so many analysts... The other thing from an analyst is, most analysts focus on a piece of it, and this is very different. I know some analysts focus on like, user experience, and let's look at the application, that's probably closer to where VDI is then, right, if you ask the storage guys they're like ah, VDI. If you ask the desktop people they're like wait, my place is fine so, it's that, it was a really complicated problem, but it's very different today, than it was, and I have to think with Nutanix it is, must've changed in the last five years. >> Absolutely, and well, I think the other thing is that's funny is if you take it back to like 2008 right? Analysts called the VDI game really early, so it's like you're saying every year was the VDI. Before anybody was deploying it in any sort of size, they were already saying it's a, X gazillion billion dollar market and that, and it, I think it's taken awhile for the customers... The customers are still just trying to dealing with some very basic desktop management issues today, and they're probably lagging behind the industry and analysts by three to five years I'd say, right? But what I hear now is, Windows 10 is coming around the horizon, how am I going to manage Windows 10 updates? I've got an Office 365 deployment project on my hands, how am I going to get this all out, how am I going to get the functionality that every one of my end users needs? And it comes around and it's like VDI is a great answer for that, it's a great way to solve that issue. >> Calvin, one of the things that we hear from new (mumbles) customers I mean, they love that kind of one-click simplicity, one-click update, and I hear about you know, Windows 10 is like the roll-out of the next thing, and where things break. How are Citrix and Nutanix working together to solve some of these challenges? >> Yeah, I think that approach of one-click, the automation you know, both the blue-printing types of technology is what we're pulling together. All that sort of automation is really important for, for this type of environment. You know I think the, we're both willing to pull together solutions that really then, drive that simplicity for, for both the infrastructure and the management, ongoing of that solution. It's like for example, we're working together on, work on the district's workspace appliance right? And that's, for us it's not a product name that's really a program, it's a way of defining HCI infrastructure like Nutanix and they're jumping on board with this. To be able to point that thing at the Citrix Cloud, and then download all the resources that it needs in order to run a Citrix workload on it. So it's a very automated way of getting stood up, so that not only is it deployment of the infrastructure, automated and simple, but placing that workload on it, and getting it set to manage, and then even running it and operating it is more like running and operating a Cloud service than it is even operating a local infrastructure for it. >> One of the things that David Floyer from Wikibon, has done a lot of analysis saying, if we can get to basically a single-managed entity is where he calls it, so I can have the entire thing comes out, not just the infrastructure, but all the way through the stack. Not only does that really help your deployment, but the overall kind of time-to-value, customer experience is just tremendously improved, tell us how you're helping to kind of reach that vision. >> Yeah, well I think it's time-to-value, but it's also making VDI accessible to more customers right, and more segments of the market. The types of things that VDI solves, security, manageability, those aren't just enterprise problems right? Even midsize companies, they have security concerns, and for them it's actually probably even more dramatic, like they have a breach there, and it's catastrophic for the company, not just, you know we're delayed by a few hours. And so you know, having that simplicity, and then making that whole thing easier to deploy, and faster, it's not just easier to deploy, but on day two, it's easier to manage ongoing. Those things are getting into tension again. >> So for years I remember in the Citrix, Synergy, a bunch of VMware, VM world's, talked to customers, and it was always a two-horse race between those two companies, and Citrix was like Secretariat, and VMware was like Devil His Due. You've probably never heard of Devil His Due. Pretty good horse but not Secretariat, and you guys, Citrix was the dominant player in that marketplace. What's the competitive situation today? It seems like VMware has made some acquisitions, has maybe caught up, maybe has some advantages, what, how do you see them as a competitor? >> I, so I think where Citrix is, I think that what really happens in the competitors space now is that it becomes less about VDI, versus VDI, and like what features are in each one. Although I could talk for hours, I think there's still a bunch of differentiation in there. You know earlier talking about user experience, I think the way we're looking at this market, and what's happening to it right now, is less about sort of user experience in the sense of a classic protocol versus protocol sense, in a technical sense, and more about, and I'll use the term more and more often about employee experience, alright, so it's not just what is the performance of my virtual desktop when I'm on x-y-z device, over a certain network. It is what happens that first time I give an employee a resource, or a virtual desktop, or a mobile application, or access to a SAS application, or an internally-hosted Web application through a virtual browser, and they go in and they, they want to get work done right? So the experience of that employee is now, not just one of these technologies, it is what we refer to as workspace technology. It's everything I need from the applications, to the files that I want to use, to the workflows that I want to kick off, and I think that will be their new area of differentiation, and again, that's where we want to move very far for. >> Calvin, what should we be expecting to see from Citrix and Nutanix going for a long partnership, and how does it improve even more for customers? >> I think you know, the stuff that Nutanix has announced here, with the whole Hybrid Cloud strategy, I think that very much is in alignment with our philosophy on Hybrid Cloud approaches for customers. So I would expect to see a lot more in that collaboration area. There's lots more that we can do on the NetScaler side of the business for networking, and enabling the reliability of a lot of these network connections as people become, you know I love that concept of the core, the distributing the Edge Cloud right, and all of that's going to need interconnectivity, and security and reliability. And you know, more of the same on making VDI simpler for, for all customers of all sizes. I think we're just at the cusp of you know we've got this automation plan going in, we're creating the workspace appliance in its simplicity there. I think there's a lot more we can do, again, from day two perspective operationally, as I keep going and I'm growing this thing, and I'm managing my images, and I'm managing applications, and growing the infrastructure, increasing performance, taking on different types of workloads, there's lots more we can do in that area. >> What is the all Citrix Stack Workplace Appliance? >> Right, so that is really the Nutanix has announced support for XenServer, and for us, you know XenServer, we've really done a transformation of that technology over the last couple years, where we've taken what was a general platform virtualization solution, and we've really specifically targeted at our workloads. At XenApp, XenDesktop, NetScaler, and making it the best virtualization platform for our, for our solutions. Why do we do that? We do that because there's going to be certain things that we need out of that layer from an innovation standpoint whether it's supporting graphics, which we were the first to do, across all the major ship vendors, virtual GPUs, coming up with new security paradigms like being able to do deep Hypervisor Introspection, and identify day one malware attacks before they, even infect any of the machines. You know, those sorts of innovations become really important that we can drive, and having control over XenServer we're able to do that. So through the partnership with Nutanix, and getting their support on that as well, then all the joint Nutanix and Citrix customers could take advantage of that innovation. So now they also have the obviously at their disposal, everything that Nutanix is putting into HV, everything we're putting into XenServer, and being able to manage it that way. So, in the workspace appliance, sort of reference guide for building this, one of the things we focus on is the XenServer component of it, and being able to have that innovation coming from Citrix as part of that solution. >> Great. Calvin, thanks very much for coming to theCUBE, appreciate your time, and your insights. >> Thank you, yeah it's good to be here. >> Good to see you. Alright, keep it right there buddy, Stu and I will be back with our next guest. We're live from DotNext, #NEXTConf, this is theCUBE. (techno music)