>> From theCUBE studios in Palo Alto in Boston bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> The term Supercloud is somewhat new, but the concepts behind it have been bubbling for years, early last decade when NIST put forth a definition of cloud computing it said services had to be accessible over a public network essentially cutting the on-prem crowd out of the cloud conversation. Now a guy named Chuck Hollis, who was a field CTO at EMC at the time and a prolific blogger objected to that criterion and laid out his vision for what he termed a private cloud. Now, in that post, he showed a workload running both on premises and in a public cloud sharing the underlying resources in an automated and seamless manner. What later became known more broadly as hybrid cloud that vision as we now know, really never materialized, and we were left with multi-cloud sets of largely incompatible and disconnected cloud services running in separate silos. The point is what Hollis laid out, IE the ability to abstract underlying infrastructure complexity and run workloads across multiple heterogeneous estates with an identical experience is what super cloud is all about. Hello and welcome to this week's Wikibon cube insights powered by ETR and this breaking analysis. We share what we hope to learn from super cloud 22 next week, next Tuesday at 9:00 AM Pacific. The community is gathering for Supercloud 22 an inclusive pilot symposium hosted by theCUBE and made possible by VMware and other founding partners. It's a one day single track event with more than 25 speakers digging into the architectural, the technical, structural and business aspects of Supercloud. This is a hybrid event with a live program in the morning running out of our Palo Alto studio and pre-recorded content in the afternoon featuring industry leaders, technologists, analysts and investors up and down the technology stack. Now, as I said up front the seeds of super cloud were sewn early last decade. After the very first reinvent we published our Amazon gorilla post, that scene in the upper right corner here. And we talked about how to differentiate from Amazon and form ecosystems around industries and data and how the cloud would change IT permanently. And then up in the upper left we put up a post on the old Wikibon Wiki. Yeah, it used to be a Wiki. Check out my hair by the way way no gray, that's how long ago this was. And we talked about in that post how to compete in the Amazon economy. And we showed a graph of how IT economics were changing. And cloud services had marginal economics that looked more like software than hardware at scale. And this would reset, we said opportunities for both technology sellers and buyers for the next 20 years. And this came into sharper focus in the ensuing years culminating in a milestone post by Greylock's Jerry Chen called Castles in the Cloud. It was an inspiration and catalyst for us using the term Supercloud in John Furrier's post prior to reinvent 2021. So we started to flesh out this idea of Supercloud where companies of all types build services on top of hyperscale infrastructure and across multiple clouds, going beyond multicloud 1.0, if you will, which was really a symptom, as we said, many times of multi-vendor at least that's what we argued. And despite its fuzzy definition, it resonated with people because they knew something was brewing, Keith Townsend the CTO advisor, even though he frankly, wasn't a big fan of the buzzy nature of the term Supercloud posted this awesome Blackboard on Twitter take a listen to how he framed it. Please play the clip. >> Is VMware the right company to make the super cloud work, term that Wikibon came up with to describe the taking of discreet services. So it says RDS from AWS, cloud compute engines from GCP and authentication from Azure to build SaaS applications or enterprise applications that connect back to your data center, is VMware's cross cloud vision 'cause it is just a vision today, the right approach. Or should you be looking towards companies like HashiCorp to provide this overall capability that we all agree, or maybe you don't that we need in an enterprise comment below your thoughts. >> So I really like that Keith has deep practitioner knowledge and lays out a couple of options. I especially like the examples he uses of cloud services. He recognizes the need for cross cloud services and he notes this capability is aspirational today. Remember this was eight or nine months ago and he brings HashiCorp into the conversation as they're one of the speakers at Supercloud 22 and he asks the community, what they think, the thing is we're trying to really test out this concept and people like Keith are instrumental as collaborators. Now I'm sure you're not surprised to hear that mot everyone is on board with the Supercloud meme, in particular Charles Fitzgerald has been a wonderful collaborator just by his hilarious criticisms of the concept. After a couple of super cloud posts, Charles put up his second rendition of "Supercloudifragilisticexpialidoucious". I mean, it's just beautiful, but to boot, he put up this picture of Baghdad Bob asking us to just stop, Bob's real name is Mohamed Said al-Sahaf. He was the minister of propaganda for Sadam Husein during the 2003 invasion of Iraq. And he made these outrageous claims of, you know US troops running in fear and putting down their arms and so forth. So anyway, Charles laid out several frankly very helpful critiques of Supercloud which has led us to really advance the definition and catalyze the community's thinking on the topic. Now, one of his issues and there are many is we said a prerequisite of super cloud was a super PaaS layer. Gartner's Lydia Leong chimed in saying there were many examples of successful PaaS vendors built on top of a hyperscaler some having the option to run in more than one cloud provider. But the key point we're trying to explore is the degree to which that PaaS layer is purpose built for a specific super cloud function. And not only runs in more than one cloud provider, Lydia but runs across multiple clouds simultaneously creating an identical developer experience irrespective of a state. Now, maybe that's what Lydia meant. It's hard to say from just a tweet and she's a sharp lady, so, and knows more about that market, that PaaS market, than I do. But to the former point at Supercloud 22, we have several examples. We're going to test. One is Oracle and Microsoft's recent announcement to run database services on OCI and Azure, making them appear as one rather than use an off the shelf platform. Oracle claims to have developed a capability for developers specifically built to ensure high performance low latency, and a common experience for developers across clouds. Another example we're going to test is Snowflake. I'll be interviewing Benoit Dageville co-founder of Snowflake to understand the degree to which Snowflake's recent announcement of an application development platform is perfect built, purpose built for the Snowflake data cloud. Is it just a plain old pass, big whoop as Lydia claims or is it something new and innovative, by the way we invited Charles Fitz to participate in Supercloud 22 and he decline saying in addition to a few other somewhat insulting things there's definitely interesting new stuff brewing that isn't traditional cloud or SaaS but branding at all super cloud doesn't help either. Well, indeed, we agree with part of that and we'll see if it helps advanced thinking and helps customers really plan for the future. And that's why Supercloud 22 has going to feature some of the best analysts in the business in The Great Supercloud Debate. In addition to Keith Townsend and Maribel Lopez of Lopez research and Sanjeev Mohan from former Gartner analyst and principal at SanjMo participated in this session. Now we don't want to mislead you. We don't want to imply that these analysts are hopping on the super cloud bandwagon but they're more than willing to go through the thought experiment and mental exercise. And, we had a great conversation that you don't want to miss. Maribel Lopez had what I thought was a really excellent way to think about this. She used TCP/IP as an historical example, listen to what she said. >> And Sanjeev Mohan has some excellent thoughts on the feasibility of an open versus de facto standard getting us to the vision of Supercloud, what's possible and what's likely now, again, I don't want to imply that these analysts are out banging the Supercloud drum. They're not necessarily doing that, but they do I think it's fair to say believe that something new is bubbling and whether it's called Supercloud or multicloud 2.0 or cross cloud services or whatever name you choose it's not multicloud of the 2010s and we chose Supercloud. So our goal here is to advance the discussion on what's next in cloud and Supercloud is meant to be a term to describe that future of cloud and specifically the cloud opportunities that can be built on top of hyperscale, compute, storage, networking machine learning, and other services at scale. And that is why we posted this piece on Answering the top 10 questions about Supercloud. Many of which were floated by Charles Fitzgerald and others in the community. Why does the industry need another term what's really new and different? And what is hype? What specific problems does Supercloud solve? What are the salient characteristics of Supercloud? What's different beyond multicloud? What is a super pass? Is it necessary to have a Supercloud? How will applications evolve on superclouds? What workloads will run? All these questions will be addressed in detail as a way to advance the discussion and help practitioners and business people understand what's real today. And what's possible with cloud in the near future. And one other question we'll address is who will build super clouds? And what new entrance we can expect. This is an ETR graphic that we showed in a previous episode of breaking analysis, and it lays out some of the companies we think are building super clouds or in a position to do so, by the way the Y axis shows net score or spending velocity and the X axis depicts presence in the ETR survey of more than 1200 respondents. But the key callouts to this slide in addition to some of the smaller firms that aren't yet showing up in the ETR data like Chaossearch and Starburst and Aviatrix and Clumio but the really interesting additions are industry players Walmart with Azure, Capital one and Goldman Sachs with AWS, Oracle, with Cerner. These we think are early examples, bubbling up of industry clouds that will eventually become super clouds. So we'll explore these and other trends to get the community's input on how this will all play out. These are the things we hope you'll take away from Supercloud 22. And we have an amazing lineup of experts to answer your question. Technologists like Kit Colbert, Adrian Cockcroft, Mariana Tessel, Chris Hoff, Will DeForest, Ali Ghodsi, Benoit Dageville, Muddu Sudhakar and many other tech athletes, investors like Jerry Chen and In Sik Rhee the analyst we featured earlier, Paula Hansen talking about go to market in a multi-cloud world Gee Rittenhouse talking about cloud security, David McJannet, Bhaskar Gorti of Platform9 and many, many more. And of course you, so please go to theCUBE.net and register for Supercloud 22, really lightweight reg. We're not doing this for lead gen. We're doing it for collaboration. If you sign in you can get the chat and ask questions in real time. So don't miss this inaugural event Supercloud 22 on August 9th at 9:00 AM Pacific. We'll see you there. Okay. That's it for today. Thanks for watching. Thank you to Alex Myerson who's on production and manages the podcast. Kristen Martin and Cheryl Knight. They help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some really wonderful editing. Thank you to all. Remember these episodes are all available as podcasts wherever you listen, just search breaking analysis podcast. I publish each week on wikibon.com and Siliconangle.com. And you can email me at David.Vellantesiliconangle.com or DM me at Dvellante, comment on my LinkedIn post. Please do check out ETR.AI for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching. And we'll see you next week in Palo Alto at Supercloud 22 or next time on breaking analysis. (calm music)

>>Mm. Joining me is Aaron Suzuki, founder and CEO of Prowess. >>Aaron. Welcome. Thank you. Thanks so much for having me. >>Absolutely. Thanks for joining us. So let's dive right in. Tell us about prowess. >>Progress has been around for quite a while. We've been serving the technology industry from the very beginning, almost 20 years. We've always been able to bridge the gap between the story of the product and what it actually does. And a lot of times, there's a pretty fundamental disconnect between what engineering says and what marketing wants to claim. And so this is sort of how we got down this road of getting into testing and validation of products such as we do today quite quite extensively. And >>that's really what we're focusing on right now is this idea of your independence as a lab. And in this particular case, uh, it's a series of tests that you've done for, uh, you know, using Dell hardware combined with Broadcom cards. So talk a little more about that. About that the concept of independence and what that means. >>Yeah. You know, it's important to us that we stay vendor, agnostic, platform agnostic. Um, and there are a lot of things happening concurrently in the industry. A lot of people want to get a lot of work done really fast, and most customers are not sort of vendor exclusive. In fact, we're not sure we know of any. We always try to keep this objective point of view. That is to say that we don't allow our customers to buy results when we're doing quantitative testing. We really are out there trying to come up with a story or a narrative, and that really seemed to be The missing link in all of this is that there are the quantitative houses that do traditional benchmark testing on one side and then system integrators and kind of on the other extreme agencies. That would really do the narrative and the system integrator side build out a solution, but they wouldn't be able to tell you how it would perform. And so reconciling those two things really became challenging. So having a source that would be able to give you that insight that goes beyond just transactions, um, you know, per whatever unit of time and finding some of these metrics in between that we're more relevant to people's jobs. Where was really the inspiration for creating this unique practise that we call prowess? Labs? >>So, Erin, when I think about performance testing, uh, it's very easy to think of it from the perspective that it's a bunch of hardware slapped together in Iraq. You get some engineers, scientists to run some tests. Why prowess? What? What do you specifically bring to the table? That's meaningful? >>Performance testing is usually done in one of two ways, predominantly one way. It's a very academic approach, which says this specific benchmark test run this specific way gives us X. Another approach is more narrative in nature and more demonstrative. And there's this huge gap in between, and that's really what prowess labs exist to fulfil. >>So, Erin, give us an idea of the scale of prowess. How many of these projects have you worked on? How many how many customers have you worked with over >>over time? Um, we do this work with most of the leading global hardware and software manufacturers and a select number of emerging providers as well. So for us, you know, year to year dozens of projects of varying scope and scale. Various projects also running kind of programmatic form where we're kind of iterating constantly throughout the year. Um, so it's It's really a lot of fun for our team members to to do this. And some of them have been doing it for 10 or 12 years in continuity. >>Erin, Thanks for joining us to talk about practise today. >>My pleasure. Thanks for having me. Mhm. Yeah.

>>Mm. >>Welcome back to the Cube. Special presentation. Simplifying Hybrid Cloud Brought to You by Cisco We're here with Vegas Rattana, who's the director of product management for you? CSS Cisco and James Leach, who was director of business development at Cisco. Gents, welcome back to the Cube. Good to see you again. >>Hey, thanks for having us. >>Okay, Jim, let's start. We know that when it comes to navigating a transition to hybrid cloud, it's a complicated situation for a lot of customers and as organisations that they hit the pavement for their hybrid cloud journeys, one of the most common challenges that they face. What are they telling you? How is Cisco specifically UCS helping them deal with these problems? >>Well, you know, first, I think that's a That's a great question. And, you know, the customer centric view is is the way that we've taken. Um, it's kind of the approach we've taken from Day one, right? So I think that if you look at the challenges that we're solving for their customers are facing, you could break them into just a few kind of broader buckets. The first would definitely be applications, right? That's the That's where the rubber meets your proverbial road. Um, with the customer. And I would say that you know, what we're seeing is the challenges customers are facing within applications come from the way that applications have evolved. So what we're seeing now is more data centric applications. For example, um, those require that we are able to move, um, and process large datasets really in real time. Um, and the other aspect of application, I think, to give our customers kind of some pose some challenges would be around the fact that they're changing so quickly. So the application that exists today or the day that they make a purchase of infrastructure to be able to support that application. That application is most likely changing so much more rapidly than the infrastructure can't keep up with today. So, um, that creates some some challenges around. How do I build the infrastructure? How do I write? Size it without over provisioning, for example. But also there's a need for some flexibility around life cycle and planting those purchase cycles based on the life cycle of the different hardware elements and within the infrastructure, which I think is the second bucket of challenges. We see customers who are being forced to move away from the like a modular or blade approach, which offers a lot of operational and consolidation benefits. And they have to move to something like, um, Iraq server model for some applications because of these needs that these data centric applications have. And that creates a lot of opportunity for silo going. The infrastructure and those silos, in turn, create multiple operating models within the A data centre environment that, you know, again drive a lot of complexity. So that complexity is definitely the the enemy here. Um, and then finally, I think life cycles. We're seeing this democratisation of of processing, if you will, right, so it's no longer just CPU focus. We have GPU. We have F p g A. We have things that are being done in storage and the fabrics that stitch them together that are all changing rapidly and have very different life cycles. So when those life cycles don't align for a lot of our customers, they see a challenge in how they can can manage this these different life cycles and still make a purchase without having to make too big of a compromise in one area or another because of the misalignment of life cycles. So that is a kind of the other bucket. And then finally, I think management is huge, right? So management at its core is really right size for for our customers and give them the most value when it when it meets the mark around scale and scope. Um, back in 2000 and nine, we weren't meeting that mark in the industry and UCS came about and took management outside the chassis, right? We put at the top of the rack, and that works great for the scale and scope we needed at that time. However, as things have changed, we're seeing a very new scale and scope needed, Right? So we're talking about hybrid cloud world that has to manage across data centres across clouds. And, um, you know, having to stitch things together for some of our customers poses a huge challenge. So there are tools for all of those those operational pieces that that touched the application that touched the infrastructure. But they're not the same tool. They tend to be, um, disparate tools that have to be put together. So our customers, you know, don't really enjoy being in the business of building their own tools. So, um, so that creates a huge challenge. And one where I think that they really crave that full hybrid cloud stack that has that application visibility but also can reach down into the infrastructure. >>Right? You know, Jim, I said in my my Open that you guys, Cisco sort of changed the server game with the original UCS. But the X Series is the next generation, the generation of the next decade, which is really important cause you touched on a lot of things. These data intensive workloads, alternative processors to sort of meet those needs. The whole cloud operating model and hybrid cloud has really changed. So how's it going with the X Series? You made a big splash last year. What's the reception been in the field? >>Actually, it's been great. Um, you know, we're finding that customers can absolutely relate to our UCS X series story. Um, I think that the main reason they relate to it as they helped create it, right, it was their feedback and their partnership that they gave us Really, those problem areas, those, uh, those areas that we could solve for the customer that actually add significant value. So, you know, since we brought you see s to market back in 2000 and nine, we had this unique architectural, um uh, paradigm that we created. And I think that created a product which was the fastest in Cisco history. Um, in terms of growth, Um, what we're seeing now is X series is actually on a faster trajectory. So we're seeing a tremendous amount of uptake. We're seeing, uh, both in terms of the number of customers. But also, more importantly, the number of workloads that our customers are using and the types of workloads are growing. Right? So we're growing this modular segment that exists not just, um, you know, bringing customers onto a new product, But we're actually bringing them into the product in the way that we had envisioned, which is one infrastructure that can run any application and do it seamlessly. So we're really excited to be growing this modular segment. Um, I think the other piece, you know that, you know, we judge ourselves is, you know, sort of not just within Cisco, but also within the industry and I think right now is a You know, a great example. Our competitors have taken kind of swings and misses over the past five years at this, um, at a kind of a new next architecture, and we're seeing a tremendous amount of growth even faster than any any of our competitors have seen. When they announced something, um, that was new to this space. So I think that the ground up work that we did is really paying off. Um, and I think that what we're also seeing is it's not really a leapfrog game, Um, as it may have been in the past, Um, X series is out in front today, and we're extending that lead with some of the new features and capabilities we have. So we're delivering on the story that's already been resonating with customers, and we're pretty excited that we're seeing the results as well. So as our competitors hit walls, I think we're you know, we're executing on the plan that we laid out back in June when we launched that series to the world. And, uh, you know, as we as we continue to do that, um, we're seeing, you know, again tremendous uptake from our customers. >>So thank you for that, Jim. So viscous. I was just on Twitter just today, actually talking about the gravitational pull. You've got the public clouds pulling C x o is one way. And you know I'm Prem folks pulling the other way and hybrid cloud So organisations are struggling with a lot of different systems and architectures and and ways to do things. And I said that what they're trying to do is abstract all that complexity away, and they need infrastructure to support that. And I think your stated aim is really to try to help with that with that confusion with the X series. Right? So how so? Can you explain that? >>Sure. And and and that's the right, Uh, the context that you built up right there, Dave, if you walk into Enterprise Data Centre, you see platform of computer systems spread all across because every application has its unique needs. And hence you find Dr Note Driving system memory system, computing system, coordinate system and a variety of farm factors. When you do, you, for you and every one of them typically come with a variety of adapters and cables and so forth Just create silence of resources. Fabric is broad. The actress brought the power and cooling implications the rack, you know, the space challenges and above all, the multiple management plane that they come of it, which makes it very difficult for I t to have one common centre policy and enforce it all across across the firmware and software and so forth and then think about the great challenges of the baroness makes it even more complex as these go through the great references of their own. As a result, we observe quite a few of our customers. Uh, you know, really, uh, seeing Anna slowness in that agility and high burden, uh, in the cost of overall ownership, this is where the X rays powered by inter side. We have one simple goal. We want to make sure our customers get out of that complexities. They become more Asyl and drive lower tco and we are delivering it by doing three things. Three aspects of simplification first simplify their whole infrastructure by enabling them to run their entire workload on single infrastructure and infrastructure, which removes the narrowness of fun factor and infrastructure which reduces direct from footprint that is required infrastructure were power and cooling better served in the Lord. Second, we want to simplify it with by delivering a cloud operating model where they can create the policy ones across compute network stories and deployed all across. And third, we want to take away the pain they have by simplifying the process of upgrade and any platform evolution that they are going to go through the next 23 years. So that's where the focus is on just driving down the simplicity lowering down there. >>That's key. Less friction is is always a good thing now, of course, because we heard from the hyper flex guys earlier, they had news. Not to be outdone, you have hard news as well. What innovations are you announcing around X series today? >>Absolutely. So we are following up on the excited, exciting extras announcement that we made in June last year. Day and we are now introducing three innovation on experience with the bowl of three things First, expand the supported World War and extra days. Second, take the performance to new levels. Third dramatically reduced the complex cities in the data centre by driving down the number of adapters and cables. To that end, three new innovations are coming in. First, we are introducing the support for the GPU note using a cable list and very unique X fabric architecture. This is the most elegant design to add the GPS to the compute note in the model of form factor thereby, our customers can now power in AML workload on any workload that needs many more number of GPS. Second, we are bringing in GPS right onto the computer note and thereby the our customers can now fire up the accelerated video upload, for example, and turf, which is what you know we are extremely proud about, is we are innovating again by introducing the fifth generation of our very popular unified fabric technology with the increased bandwidth that it brings in, coupled with the local drive capacity and density is that we have on the computer note our customers can now fire up the big data workloads the F C I work. Lord, uh, the FDA has worked with all these workloads that have historically not lived in the model of form. Factor can be run over there and benefit from the architectural benefits that we have. Second, with the announcement of fifth generation fabric, we become the only vendor to now finally enable 100 gig and two and single board banned word and the multiple of those that are coming in there. And we are working very closely with our partners to deliver the benefit of these performance through our Cisco validated design to oversee a franchise. And third, the innovations in, uh, in the in the fifth and public again allow our customers to have fewer physical adapters, made the Internet adapter made with our general doctors or maybe the other stories adapters. They reduced it down and coupled with the reduction in the cable so very, very excited about these three big announcements that we're making in this part of the great >>A lot There. You guys have been busy. So thank you for that. Because so, Jim, you talked a little bit about the momentum that you have. Customers are adopting. What problems are they telling you that X series addresses and and how do they align with where where they want to go in the future? >>Um, that's a great question. I think if you go back to um and think about some of the things that we mentioned before. Um, in terms of the problems that we originally set out to solve, we're seeing a lot of traction. So what the cost mentioned, I think, is really important, right? Those pieces that we just announced really enhanced that story and really move again to kind of to the next level of, of taking advantage of some of these problem solving for our customers. You know, if you look, you know, I think the cost mentioned accelerated VD. That's a great example. Um, these are where customers you know, they need to have this dense compute. They need video acceleration, they need type policy management, right. And they need to be able to deploy these, um, these systems anywhere in the world. Well, that's exactly what we're hitting on here with X series right now, we're hitting the mark in every every single way, right? We have the highest compute config density that we can offer across the, you know, the very top end configurations of CPUs. Um, and a lot of room to grow. Um, we have the the premier cloud based management. You know, hybrid cloud suite. Um uh, in the industry. Right. So check there. We have the flexible GPU accelerators that that the cost just talked about that we're announcing both on the system and also adding additional ones to the through the use of the X fabric, which is really, really critical to this launch as well. And, uh, you know, I think finally, the fifth generation of fabric interconnect and virtual interface card, um, and an intelligent fabric module go hand in hand in creating this 100 gig and end bandwidth story that we can move a lot of data again. You know, having all this performance is only as good as what we can get in and out of it, right? So giving customers the ability to manage it anywhere be able to get the bandwidth that they need to be able to get the accelerators that are flexible to that fit exactly their needs. This is huge, right? This solves a lot of the problems we can take off right away with the infrastructure. As I mentioned, X fabric is really critical here because it opens a lot of doors here. We're talking about GPS today, but in the future, there are other elements that we can disaggregate like the GPS that solve these lifecycle mismanagement issues. They solve issues around the form factor limitations. It solves all these issues for like it does for GPU. We can do that with storage or memory in the future, So that's going to be huge, right? This is disaggregate Asian that actually delivers right. It's not just a gimmicky bar trick here that we're doing. This is something that that customers can really get value out of Day one. And then finally, I think the future readiness here. You know, we avoid saying future proof because we're kind of embracing the future here. We know that not only are the GPS going to evolve, the CPUs are going to evolve the drives, the storage modules are going to evolve. All of these things are changing very rapidly. The fabric that stitches them together. It's critical, and we know that we're just on the edge of some of the developments that are coming with C XL with with some of the the PC express changes that are coming in the in the very near future. So we're ready to go X and the X fabric is exactly the vehicle that's going to be able to deliver those technologies to our customers. Our customers are out there saying that you know, they want to buy into something like X Series that has all the operational benefits, but at the same time, they have to have the comfort in knowing that they're protected against being locked out of some technology that's coming in the future. We want our customers to take these disruptive technologies and not be disrupted, but use them to disrupt, um, their competition as well. So, um, you know, we're really excited about the pieces today, and I think it goes a long way towards continuing to tell the customer benefit story that X Series brings And, um, again, stay tuned because it's going to keep getting better as we go. >>A lot of headroom, uh, for scale and the management piece is key. There just have time for one more question because talk to give us some nuggets on the road map. What's next for? For X X series that we can look forward to? >>Absolutely Dave, as as we talked about. And James also hinted this is the future radio architecture, a lot of focus and innovation that we are going through is about enabling our customers to seamlessly and painlessly adopt very disruptive hardware technologies that are coming up no infantry place. And there we are, looking into enabling the customer journey as the transition from PCH in less than 4 to 5 to six without rip and replace as they embraced the Excel without rip and replace as they embrace the newer paradigm of computing through the desegregated memory desegregated P. C, A, r N B and dance drives and so forth. We're also looking forward to extract Brick Next Generation, which will and now that dynamic assignment of GPS anywhere within the chassis and much more. Um, so this this is again all about focusing on the innovation that will make the Enterprise Data Centre operations a lot more simpler and drive down the PCO by keeping them not only covered for today, but also for future. So that's where some of the focus is on there. >>Okay, Thank you guys. We'll leave it there in a moment. I'll have some closing thoughts. >>Mhm

>>mhm. >>With me now are Manish Agarwal, senior director of product management for Hyper Flex at Cisco at Flash for all number four. Love that on Twitter And Deron Williams, the director of business development and sales for Cisco Mister Hyper flex at Mr Hyper Flex on Twitter. Thanks, guys. Hey, we're going to talk about some news and and hyper flex and what role it plays in accelerating the hybrid cloud journey. Gentlemen, welcome to the Cube. Good to see you. >>Thanks, David. >>Thanks. Hi, >>Daryn. Let's start with you. So for hybrid cloud you gotta have on Prem Connection. Right? So you've got to have basically a private cloud. What are your thoughts on that? >>Yeah, we agree. You can't, but you can't have a hybrid cloud without that private element. And you've got to have a strong foundation in terms of how you set up the whole benefit of the cloud model you're building in terms of what you want to try and get back from the cloud, you need a strong foundation. I'm conversions provides that we see more and more customers requiring a private cloud, and they're building with hyper convergence in particular hyper flex no to make all that work. They need a good, strong Cloud operations model to be able to connect both the private and the public. And that's where we look at insight. We've got solution around that. To be able to connect that around a Saas offering that looks around simplified operations, gives them optimisation and also automation to bring both private and public together in that hybrid world. >>Darren, let's stay with you for a minute when you talk to your customers. What are they thinking these days, when it comes to implementing hyper converged infrastructure in both the the enterprise and and at the edge? What are they trying to achieve? >>So there's many things they're trying to achieve? Probably the most brutal honesty is they're trying to save money. That's probably the quickest answer, but I think they're trying to look at in terms of simplicity. How can they remove layers of components they've had before in their infrastructure? We see obviously collapsing of storage into hyper conversions and storage networking, and we've got customers that have saved 80% worth of savings by doing that, a collapse into hyper conversion infrastructure away from their three tier infrastructure. Also about scalability. They don't know the end game, so they're looking about how they can size for what they know now and how they can grow that with hyper conversions. Very easy is one of the major factors and benefits of hyper conversions. They also obviously need performance and consistent performance. They don't want to compromise performance around their virtual machines when they want to run multiple workloads. They need that consistency all the way through. And then probably one of the biggest ones is that around. The simplicity model is the management layer ease of management to make it easier for their operations that we've got customers that have told us they've saved 50% of costs in their operations model, deploying out flex also around the time savings. They make massive time savings which they can reinvest in their infrastructure and their operations teams in being able to innovate and go forward. And then I think that we one of the biggest pieces we've seen as people move away from three tier architecture is the deployment elements, and the ease of deployment gets easy with hyper converged, especially with edge edges of major key use case for us and what I want. What our customers want to do is get the benefit of the data centre at the edge without a big investment. They don't compromise in performance, and they want that simplicity in both management employment. And we've seen analysts recommendations around what their readers are telling them in terms of how management deployments key for it, operations teams and how much they're actually saving by deploying edge and taking the burden away when they deployed hyper conversions. As I said, the savings elements to keep it and again, not always, but obviously those are his studies around about public Cloud being quite expensive at times over time for the wrong workloads. So by bringing them back, people can make savings. We again have customers that have made 50% savings over three years compared to their public cloud usage. So I'd say that's the key things that customers looking for >>Great. Thank you for that, Darrin minutes. We have some hard news. You've been working a lot on evolving the hyper flex line. What's the big news that you've just announced? >>Yeah, Thanks. Leave. So there are several things that we are announcing today. the first one is a new offer, um, called hyper Flex Express. This is, you know, Cisco Inter site lead and Cisco and decide managed it Hyper flex configurations that we feel are the fastest part to hybrid cloud. The second is we're expanding our server portfolio by adding support for HX on AM Iraq, U. C s and Iraq. And the third is a new capability that we're introducing that we're calling local contemporaries witness. And let me take a minute to explain what this is. This is a very nifty capability to optimise for forage environments. So, you know, this leverages the Ciscos ubiquitous presence. Uh, the networking, um, you know, products that we have in the environments worldwide. So the smallest hyper flex configuration that we have is, uh it do not configuration, which is primarily used in edge environment. Think of a, you know, a back home in a department store or a oil rig. Or it might even be a smaller data centre, uh, somewhere, uh, on the globe. For these two not configurations. There is always a need for a third entity that, you know, industry term for that is either a witness or an arbitrator. Uh, we had that for hyper flex as well. The problem that customers faces where you host this witness it cannot be on the cluster because it's the job of the witnesses to when the when the infrastructure is going down, it basically breaks, um, sort of upgrade rates. Which note gets to survive, so it needs to be outside of the cluster. But finding infrastructure, uh, to actually host this is a problem, especially in the edge environments where these are resource constrained environment. So what we've done is we've taken that witness. We've converted it into a container reform factor and then qualified a very large a slew of Cisco networking products that we have right from S. R. S R. Texas catalyst, industrial routers, even even a raspberry pi that can host host this witness, eliminating the need for you to find yet another piece of infrastructure or doing any, um, you know, care and feeding of that infrastructure. You can host it on something that already exists in the environment. So those are the three things that we're announcing today. >>So I want to ask you about hyper Flex Express. You know, obviously the whole demand and supply chain is out of whack. Everybody's global supply chain issues are in the news. Everybody's dealing with it. Can you expand on that? A little bit more Can can hyper flex express help customers respond to some of these issues. >>Yeah, indeed. The, uh, you know, the primary motivation for hyper Flex Express was indeed, uh, an idea that, you know, one of the folks around my team had, which was to build a set of hyper flex configurations that are, you know, would have a shorter lead time. But as we were brainstorming, we were actually able to tag on multiple other things and make sure that, you know, there is in it for something in it for customers, for sales as well as our partners. So, for example, you know, for customers, we've been able to dramatically simplify the configuration and the instal for hyper flex express. These are still hypertext configurations, and you would, at the end of it, get a hyper flex cluster. But the part to that cluster is much much simplifying. Second is that we've added in flexibility where you can now deploy these, uh, these are data centre configurations But you can deploy these with or without fabric interconnects, meaning you can deploy it with your existing top of rack. Um, we've also, you know, already attract attractive price point for these. And of course, you know these will have better lead times because we made sure that, you know, we are using components that are that we have clear line of sight from a supply perspective for partner and sales. This is represents a high velocity sales motion, a faster turnaround time, Uh, and a frictionless sales motion for our distributors. Uh, this is actually a settled, risky, friendly configurations, which they would find very easy to stalk and with a quick turnaround time, this would be very attractive for the deceased as well. >>It's interesting many. So I'm looking at some fresh survey data. More than 70% of the customers that were surveyed this GTR survey again mentioned at the top. More than 70% said they had difficulty procuring, uh, server hardware and networking was also a huge problem. So so that's encouraging. What about Manisha AMG that's new for hyper flex? What's that going to give customers that they couldn't get before? >>Yeah, so you know, in the short time that we've had UCS am direct support, we've had several record breaking benchmark results that we've published. So it's a it's a It's a powerful platform with a lot of performance in it and hyper flex. Uh, you know, the differentiator that we've had from Day one is that it is. It has the industry leading storage performance. So with this, we're going to get the fastest compute together with the fastest storage and this we are hoping that will basically unlock, you know, a unprecedented level of performance and efficiency, but also unlock several new workloads that were previously locked out from the hyper converged experience. >>Yeah, cool. Uh, so, Darren, can >>you can you give us >>an idea as to how hyper flexes is doing in the field? >>Sure, Absolutely So both me and my initial been involved right from the start and before it was called Hyper Flex, and we've had a great journey, and it's very excited to see where we're taking where we've been with the technology. So we have over 5000 customers worldwide, and we're currently growing faster year over year than the market. The majority of our customers are repeat buyers, which is always a good sign in terms of coming back when they approved the technology and are comfortable with technology. They repeat by for expanding capacity, putting more workloads on. They're using different use cases on there. And from an energy perspective, more numbers of science so really good. Endorsement the technology. We get used across all verticals or segments, um, to house mission critical applications as well as the traditional virtual server infrastructures. Uh, and we are the lifeblood of our customers around those mission critical customers think one example, and I apologise for the worldwide audience. But this resonates with the American audiences the Super Bowl. So the sofa like stadium that housed the Super Bowl actually has Cisco hyper Flex running all the management services through from the entire stadium for digital signage. Four K video distribution, and it's complete completely cashless. So if that were to break during Super Bowl, that would have been a big, uh, news article, but it was run perfectly. We in the design of the solution, we're able to collapse down nearly 200 servers into a few notes across a few racks and have 100 120 virtual machines running the whole stadium without missing a heartbeat. And that is mission critical for you to run Super Bowl and not be on the front of the press afterwards for the wrong reasons. That's a win for us. So we really are really happy with High Flex where it's going, what it's doing. And some of the use cases were getting involved in very, very excited. >>Come on, Darren. It's Super Bowl NFL. That's a That's international now. And, you know, the NFL >>NFL. It's >>invading London. Of course I see the picture of the real football over your shoulder, But last question for many is give us a little roadmap. What's the future hold for hyper flex? >>Yeah, so you know, as Darren said, both Darren and I have been involved the type of flicks since the beginning, Uh, but I think the best is yet to come. There are three main pillars for for hyper Flex. One is in. The site is central to our strategy. It provides a lot of customer benefit from a single pane of glass management. But we're going to take this beyond the Lifecycle management, which is for hyper flex, which is integrated in winter side today and element management. We're going to take it beyond that and start delivering customer value on the dimensions of a job. Because Interstate really provides us an ideal platform to gather starts from all the clusters across the globe. Do AML and do some predictive analysis with that and return it back as, uh, you know, customer valued, um, actionable insights. So that is one. The second is you'll see us expand the hyper flex portfolio. Go beyond you see us to third party server platforms, and newer, you see a server platforms as well. But the highlight there is one that I'm really really excited about and think that there is a lot of potential in terms of the number of customers we can help is a checks on X CDs. Experience is another thing that we're able to, uh you know, uh, announcing a bunch of capabilities on in this particular launch. But a check sonic series. We'll have that by the end of this calendar year, and that should unlock with the flexibility of X series of hosting a multitude of workloads and the simplicity of hyper flex. We're hoping that would bring a lot of benefits to new workloads, that we're locked out previously. And then the last thing is hyper flex leader platform. This is the heart of the offering today, Uh, and you'll see the hyper flex data platform itself. It's a distributed architecture, unique distributed architecture primarily where we get our, you know, record breaking performance from you'll see it get faster, more scalable, more resilient. And we'll optimise it for, you know, containerised workloads, meaning it will get granular containerised container granular management capabilities and optimised for public. So those are some things that were the team is busy working on, and we should see that come to fruition. I'm hoping that we'll be back at this forum and maybe before the end of the year and talking about some of these new capabilities. >>That's great. Thank you very much for that. Okay, guys, we got to leave it there and you know many She was talking about the HX on X Series. That's huge. Customers are gonna love that, and it's a great transition because in a moment I'll be back with Vikas Ratna and Jim Leach and we're gonna dig into X series. Some real serious engineering went into this platform, and we're gonna explore what it all means. You're watching simplifying hybrid cloud on the cube, your leader in enterprise tech coverage.

>>we'll go back to the cubes. Coverage of the age of US Executive Summit at Davis. Reinvent made possible by Accenture My name is Dave Volunteer. We're gonna talk about the arm nation advantage, embraced the future of productivity, improve speed quality and customer experience through artificial intelligence. And we herewith Bhaskar goes, Who's the chief strategy Officer X censure in Rajendra RP Prasad is the senior managing director in Global Automation. The Accenture guys walk into the Cube. Get to seal. >>Thank you. >>Hey, congratulations on the new book. I know it's like giving birth, but it's a mini version. If the well, the automation advantage embraced a future of productivity, improve speed, quality and customer experience to artificial intelligence. What inspired you to write this book? Can you tell us a little bit more about it and how businesses are going to be able to take advantage of the information that's in there? Maybe you could start, >>so I think you know, if we say that what inspired as primarily the two things really style, you know, over inspired have to start this project in first of all is the technology change step change in the technology. Second is the mile maturity of the buyer maturity of the market when it's a little more, you know, when I talk about the technology change, automation is nothing new in the industry. In the starting from the Industrial Revolution, always, industry adopted the automation. But last few years would happen. That there is a significant change in the technology in terms of not of new technologies are coming together like cloud data, artificial intelligence, machine learning and they are gearing match you, and that created a huge opportunity in the industry. So that is number one second if fighting the maturity of the buyer. So buyers are always buying automation, adopting the automation. So when I talked to this different by a different industrial wire, suddenly we realise they're not asking about workings automation, how that will help. But primarily they're talking about how they can scaling. They have all have done the pilot, the prototype, how they can take the full advantage in their enterprise through scheme and talking to few client few of our clients, and he realised that it's best to write this boat and film all our clients to take advantage of this new technologies to skill up their business. If I give a little more than inside that one, exactly we are trying to do in this boat primarily, we dealt with three things. One is the individual automation which deals with the human efficiency. Second is the industrial automation who visited a group efficiency. And third is the intelligent automation. We deal city business, official efficiency while business value. So we believe that this is what will really change their business and help our client help the automation. It users to really make clear an impact in their business. >>Yeah, And so you talked about that? The maturity of the customer. And and I like the way you should describe that spectrum ending with intelligent automation. So the point is you not just paving the cow path, if you will, automating processes that maybe were invented decades ago. You're really trying to rethink the best approach. And that's where you going to get the most business value, our peace In thinking about the maturity, I think the a pre pandemic people were maybe a little reluctant s Bhaskar was saying maybe needed some education. But But how? If things change me, obviously the penned Emmick has had a huge impact. It's accelerated things, but but what's changed in the business environment? In terms of the need to implement automation? R. P >>thank you Well, that is an excellent question. As even through the pandemic, most of the enterprises accelerated what I call as the digital transformation, technology transformation and the war all time that it takes to do. The transformation is compressed in our most land prices. Now do compress transformation. The core of it is innovation and innovation, led technology and technology based solutions. To drive this transformation automation. Artificial intelligence becomes hot of what we do while we are implementing this accelerators. Innovation enablers within the enterprises, most of the enterprises prior to the pandemic we're looking automation and I as a solution for cost efficiency. Saving cost in DePina deriving capacity efficiency does if they do the transformation when we press the fast forward but draw the transformation journey liberating automation. What happens is most of the enterprises which the focus from cost efficiency to speed to market application availability and system resiliency at the core. When I speaking to most of the sea woes Corrine Wall in the tech transformation they have now embrace automation and air as a Conan able to bribe this journeys towards, you know, growth, innovation, lead application, availability and transformation and sustainability of the applications through the are A book addresses all of these aspects, including the most important element of which is compute storeys and the enablement that it can accomplish through cloud transformation, cloud computing services and how I I and Michelle learning take log technologies can in a benefit from transformation to the block. In addition, we also heard person talk about automation in the cloud zero automation taking journey towards the cloud on automation Once you're in the clouds, water the philosophy and principles he should be following to drive the motivation. We also provide holy holistic approach to dry automation by focusing process technology that includes talent and change management and also addressing automation culture for the organisations in the way they work as they go forward. >>You mentioned a couple things computing, storage and when we look at our surveys, guys is it is interesting to see em, especially since the pandemic, four items have popped up where all the spending momentum is cloud province reasons scale and in resource and, you know, be able the report to remotely containers because a lot of people have work loads on Prem that they just can automatically move in the company, want to do development in the cloud and maybe connect to some of those on from work clothes. R P A. Which is underscores automation in, of course, and R. P. You mentioned a computing storage and, of course, the other pieces. Data's We have always data, but so my question is, how has the cloud and eight of us specifically influenced changes in automation? In a >>brilliant question and brilliant point, I say no winner. I talked to my clients. One of the things that I always says, Yeah, I I is nothing but y for the data that is the of the data. So that date of place underlying a very critical part of applying intelligence, artificial intelligence and I in the organization's right as the organisation move along their automation journey. Like you said, promoting process automation to contain a realisation to establishing data, building the data cubes and managing the massive data leveraging cloud and how Yebda please can help in a significant way to help the data stratification Dana Enablement data analysis and not data clustering classification All aspects of the what we need to do within the between the data space that helps for the Lord scale automation effort, the cloud and and ablest place a significant role to help accelerate and enable the data part. Once you do that, building mission learning models on the top of it liberating containers clusters develops techniques to drive, you know the principles on the top of it is very makes it easier to drive that on foster enablement advancement through cloud technologists. Alternatively, using automation itself to come enable the cloud transformation data transformation data migration aspects to manage the complexity, speed and scale is very important. The book stresses the very importance of fuelling the motion of the entire organisation to agility, embracing new development methods like automation in the cloud develops Davis a cop's and the importance of oral cloud adoptions that bills the foundational elements of, you know, making sure you're automation and air capabilities are established in a way that it is scalable and sustainable within the organisations as they move forward, >>Right? Thank you for that r p vast crime want to come back to this notion of maturity and and just quite automation. So Andy Jossy made the phrase undifferentiated, heavy lifting popular. But that was largely last decade. Apply to it. And now we're talking about deeper business integration. And so you know, automation certainly is solves the problem of Okay, I can take Monday and cast like provisioning storage in compute and automate that great. But what is some of the business problems, that deeper business integration that we're solving through things? And I want to use the phrase they used earlier intelligent automation? What is that? Can you give an example? >>Let's a very good question as we said, that the automation is a journey, you know, if we talk to any blind, so everybody wants to use data and artificial intelligence to transform their business, so that is very simple. But the point is that you cannot reach their anti unless you follow the steps. So in our book, we have explained that the process that means you know, we defined in a five steps. We said that everybody has to follow the foundation, which is primarily tools driven optimise, which is process drivel. An official see improvement, which is primarily are driven. Then comes predictive capability, the organisation, which is data driven, and then intelligence, which is primarily artificial intelligence driven. Now, when I talked about the use of artificial intelligence and this new intelligent in the business, what the what I mean is basically improved decision making in every level in the organisation and give the example. We have given multiple example in this, both in a very simple example, if I take suppose, a financial secretary organisation, they're selling wealth management product to the client, so they have a number of management product, and they have number of their number of clients a different profile. But now what is happening? This artificial intelligence is helping their agents to target the night product for the night customers. So then, at the success rate is very high. So that is a change that is a change in the way they do business. Now some of the platform companies like Amazon on Netflix. He will see that this this killed is a very native skill for them. They used the artificial intelligence try to use everywhere, but there a lot of other companies who are trying to adopt this killed today. Their fundamental problem is they do not have the right data. They do not have the capability. They do not have all the processes so that they can inject the decision making artificial intelligence capability in every decision making to empower their workforce. And that is what we have written in this book. To provide the guidance to this in this book. How they can use the better business decision improved the create, the more business value using artificial intelligence and intelligent automation. >>Interesting. Bhaskar are gonna stay with you, you know, in their book in the middle of last decade, Erik Brynjolfsson and Andy McAfee wrote the second Machine Age, and they made a point in the book that machines have always replaced humans in instead of various tasks. But for the first time ever, we're seeing machines replacing human in cognitive task that scares a lot of people so hardy you inspire employees to embrace the change that automation can bring. What what are you seeing is the best ways to do that? >>This is a very good question. The intelligent automation implementation is not, Iet Project is primarily change management. It's primarily change in the culture, the people in the organisation into embrace this change and how they will get empowered with the machine. It is not about the replacing people by machine, which has happened historically into the earlier stages of automation, which I explained. But in this intelligent automation, it is basically empowering people to do the better. Dwelled the example. That is the thing we have written in the book about about a newspaper, 100 years old newspaper in Italy. And you know, this industry has gone through multiple automation and changes black and white printing, printing to digital. Everything happened. And now what is happening? They're using artificial intelligence, so they're writers are using those technologies to write faster. So when they are writing immediately, they're getting supported with the later they're supporting with the related article they are supporting with this script, even they're supported to the heading of this article. So the question is that it is not replacing the news, you know, the content writer, but is basically empowering them so that they can produce the better quality of product they can, better writing in a faster time. So is very different approach and that is why is, um, needs a change management and it's a cultural change. >>Garden R P What's it for me? Why should we read the automation advantage? Maybe you can talk about some of the key takeaways and, you know, maybe the best places to start on an automation journey. >>Very will cut the fastest MP, Newer automation journey and Claude Adoption Journey is to start simple and start right if you know what's have free one of the process, Guru says, If you don't know where you are on a map, a map won't help you, so to start right, a company needs to know where they are on a map today, identify the right focus areas, create a clear roadmap and then move forward with the structured approach for successful our option. The other important element is if you automate an inefficient process, we are going to make your inefficiency run more efficiently. So it is very important to baseline, and then I established the baseline and know very or on the journey map. This is one of the key teams we discuss in the Automation Advantis book, with principles and tips and real world examples on how to approach each of these stages. We also stress the importance of building the right architecture is for intelligent automation, cloud enablement, security at the core of automation and the platform centric approach. Leading enterprises can fade out adopters and Iraq, whether they are in the early stages of the automation, journey or surrender advanced stage the formation journey. They can look at the automation advantage book and build and take the best practises and and what is provided as a practical tips within the book to drive there. Automation journey. This also includes importance of having right partners in the cloud space, like a loveliest who can accelerate automation, journey and making sure accompanies cloud migration. Strategy includes automation, automation, lead, yea and data as part of their journey. Management. >>That's great. Good advice there. Bring us home. Maybe you can wrap it up with the final final world. >>So, lefty, keep it very simple. This book will help you to create difference in your business with the power of automation and artificial intelligence. >>That's a simple message and will governor what industry you're in? There is a disruptions scenario for your industry and that disruption scenarios going to involve automation, so you better get ahead of editor game. They're The book is available, of course, at amazon dot com. You can get more information. X censure dot com slash automation advantage. Gosh, thanks so much for coming in the Cube. Really appreciate your time. >>Thank you. Thank >>you. >>Eh? Thank you for watching this episode of the eight of US Executive Summit of reinvent made possible by Accenture. Keep it right there for more discussions that educating spy inspire You're watching the queue.

>> Hi, I'm Natalie Ehrlich and welcome to the AWS startup showcase presented by The Cube. We have an amazing lineup of great guests who will share their insights on the latest innovations and solutions and leveraging cloud scale data in AI, security and life sciences. And now we're joined by the co-founders and co-CEOs of The Cube, Dave Vellante and John Furrier. Thank you gentlemen for joining me. >> Hey Natalie. >> Hey Natalie. >> How are you doing. Hey John. >> Well, I'd love to get your insights here, let's kick it off and what are you looking forward to. >> Dave, I think one of the things that we've been doing on the cube for 11 years is looking at the signal in the marketplace. I wanted to focus on this because AI is cutting across all industries. So we're seeing that with cybersecurity and life sciences, it's the first time we've had a life sciences track in the showcase, which is amazing because it shows that growth of the cloud scale. So I'm super excited by that. And I think that's going to showcase some new business models and of course the keynotes Ali Ghodsi, who's the CEO Data bricks pushing a billion dollars in revenue, clear validation that startups can go from zero to a billion dollars in revenues. So that should be really interesting. And of course the top venture capitalists coming in to talk about what the enterprise dynamics are all about. And what about you, Dave? >> You know, I thought it was an interesting mix and choice of startups. When you think about, you know, AI security and healthcare, and I've been thinking about that. Healthcare is the perfect industry, it is ripe for disruption. If you think about healthcare, you know, we all complain how expensive it is not transparent. There's a lot of discussion about, you know, can everybody have equal access that certainly with COVID the staff is burned out. There's a real divergence and diversity of the quality of healthcare and you know, it all results in patients not being happy, and I mean, if you had to do an NPS score on the patients and healthcare will be pretty low, John, you know. So when I think about, you know, AI and security in the context of healthcare in cloud, I ask questions like when are machines going to be able to better meet or make better diagnoses than doctors? And that's starting. I mean, it's really in assistance putting into play today. But I think when you think about cheaper and more accurate image analysis, when you think about the overall patient experience and trust and personalized medicine, self-service, you know, remote medicine that we've seen during the COVID pandemic, disease tracking, language translation, I mean, there are so many things where the cloud and data, and then it can help. And then at the end of it, it's all about, okay, how do I authenticate? How do I deal with privacy and personal information and tamper resistance? And that's where the security play comes in. So it's a very interesting mix of startups. I think that I'm really looking forward to hearing from... >> You know Natalie one of the things we talked about, some of these companies, Dave, we've talked a lot of these companies and to me the business model innovations that are coming out of two factors, the pandemic is kind of coming to an end so that accelerated and really showed who had the right stuff in my opinion. So you were either on the wrong side or right side of history when it comes to the pandemic and as we look back, as we come out of it with clear growth in certain companies and certain companies that adopted let's say cloud. And the other one is cloud scale. So the focus of these startup showcases is really to focus on how startups can align with the enterprise buyers and create the new kind of refactoring business models to go from, you know, a re-pivot or refactoring to more value. And the other thing that's interesting is that the business model isn't just for the good guys. If you look at say ransomware, for instance, the business model of hackers is gone completely amazing too. They're kicking it but in terms of revenue, they have their own they're well-funded machines on how to extort cash from companies. So there's a lot of security issues around the business model as well. So to me, the business model innovation with cloud-scale tech, with the pandemic forcing function, you've seen a lot of new kinds of decision-making in enterprises. You seeing how enterprise buyers are changing their decision criteria, and frankly their existing suppliers. So if you're an old guard supplier, you're going to be potentially out because if you didn't deliver during the pandemic, this is the issue that everyone's talking about. And it's kind of not publicized in the press very much, but this is actually happening. >> Well thank you both very much for joining me to kick off our AWS startup showcase. Now we're going to go to our very special guest Ali Ghodsi and John Furrier will seat with him for a fireside chat and Dave and I will see you on the other side. >> Okay, Ali great to see you. Thanks for coming on our AWS startup showcase, our second edition, second batch, season two, whatever we want to call it it's our second version of this new series where we feature, you know, the hottest startups coming out of the AWS ecosystem. And you're one of them, I've been there, but you're not a startup anymore, you're here pushing serious success on the revenue side and company. Congratulations and great to see you. >> Likewise. Thank you so much, good to see you again. >> You know I remember the first time we chatted on The Cube, you weren't really doing much software revenue, you were really talking about the new revolution in data. And you were all in on cloud. And I will say that from day one, you were always adamant that it was cloud cloud scale before anyone was really talking about it. And at that time it was on premises with Hadoop and those kinds of things. You saw that early. I remember that conversation, boy, that bet paid out great. So congratulations. >> Thank you so much. >> So I've got to ask you to jump right in. Enterprises are making decisions differently now and you are an example of that company that has gone from literally zero software sales to pushing a billion dollars as it's being reported. Certainly the success of Data bricks has been written about, but what's not written about is the success of how you guys align with the changing criteria for the enterprise customer. Take us through that and these companies here are aligning the same thing and enterprises want to change. They want to be in the right side of history. What's the success formula? >> Yeah. I mean, basically what we always did was look a few years out, the how can we help these enterprises, future proof, what they're trying to achieve, right? They have, you know, 30 years of legacy software and, you know baggage, and they have compliance and regulations, how do we help them move to the future? So we try to identify those kinds of secular trends that we think are going to maybe you see them a little bit right now, cloud was one of them, but it gets more and more and more. So we identified those and there were sort of three or four of those that we kind of latched onto. And then every year the passes, we're a little bit more right. Cause it's a secular trend in the market. And then eventually, it becomes a force that you can't kind of fight anymore. >> Yeah. And I just want to put a plug for your clubhouse talks with Andreessen Horowitz. You're always on clubhouse talking about, you know, I won't say the killer instinct, but being a CEO in a time where there's so much change going on, you're constantly under pressure. It's a lonely job at the top, I know that, but you've made some good calls. What was some of the key moments that you can point to, where you were like, okay, the wave is coming in now, we'd better get on it. What were some of those key decisions? Cause a lot of these startups want to be in your position, and a lot of buyers want to take advantage of the technology that's coming. They got to figure it out. What was some of those key inflection points for you? >> So if you're just listening to what everybody's saying, you're going to miss those trends. So then you're just going with the stream. So, Juan you mentioned that cloud. Cloud was a thing at the time, we thought it's going to be the thing that takes over everything. Today it's actually multi-cloud. So multi-cloud is a thing, it's more and more people are thinking, wow, I'm paying a lot's to the cloud vendors, do I want to buy more from them or do I want to have some optionality? So that's one. Two, open. They're worried about lock-in, you know, lock-in has happened for many, many decades. So they want open architectures, open source, open standards. So that's the second one that we bet on. The third one, which you know, initially wasn't sort of super obvious was AI and machine learning. Now it's super obvious, everybody's talking about it. But when we started, it was kind of called artificial intelligence referred to robotics, and machine learning wasn't a term that people really knew about. Today, it's sort of, everybody's doing machine learning and AI. So betting on those future trends, those secular trends as we call them super critical. >> And one of the things that I want to get your thoughts on is this idea of re-platforming versus refactoring. You see a lot being talked about in some of these, what does that even mean? It's people trying to figure that out. Re-platforming I get the cloud scale. But as you look at the cloud benefits, what do you say to customers out there and enterprises that are trying to use the benefits of the cloud? Say data for instance, in the middle of how could they be thinking about refactoring? And how can they make a better selection on suppliers? I mean, how do you know it used to be RFP, you deliver these speeds and feeds and you get selected. Now I think there's a little bit different science and methodology behind it. What's your thoughts on this refactoring as a buyer? What do I got to do? >> Well, I mean let's start with you said RFP and so on. Times have changed. Back in the day, you had to kind of sign up for something and then much later you're going to get it. So then you have to go through this arduous process. In the cloud, would pay us to go model elasticity and so on. You can kind of try your way to it. You can try before you buy. And you can use more and more. You can gradually, you don't need to go in all in and you know, say we commit to 50,000,000 and six months later to find out that wow, this stuff has got shelf where it doesn't work. So that's one thing that has changed it's beneficial. But the second thing is, don't just mimic what you had on prem in the cloud. So that's what this refactoring is about. If you had, you know, Hadoop data lake, now you're just going to have an S3 data lake. If you had an on-prem data warehouse now you just going to have a cloud data warehouse. You're just repeating what you did on prem in the cloud, architected for the future. And you know, for us, the most important thing that we say is that this lake house paradigm is a cloud native way of organizing your data. That's different from how you would do things on premises. So think through what's the right way of doing it in the cloud. Don't just try to copy paste what you had on premises in the cloud. >> It's interesting one of the things that we're observing and I'd love to get your reaction to this. Dave a lot** and I have been reporting on it is, two personas in the enterprise are changing their organization. One is I call IT ops or there's an SRE role developing. And the data teams are being dismantled and being kind of sprinkled through into other teams is this notion of data, pipelining being part of workflows, not just the department. Are you seeing organizational shifts in how people are organizing their resources, their human resources to take advantage of say that the data problems that are need to being solved with machine learning and whatnot and cloud-scale? >> Yeah, absolutely. So you're right. SRE became a thing, lots of DevOps people. It was because when the cloud vendors launched their infrastructure as a service to stitch all these things together and get it all working you needed a lot of devOps people. But now things are maturing. So, you know, with vendors like Data bricks and other multi-cloud vendors, you can actually get much higher level services where you don't need to necessarily have lots of lots of DevOps people that are themselves trying to stitch together lots of services to make this work. So that's one trend. But secondly, you're seeing more data teams being sort of completely ubiquitous in these organizations. Before it used to be you have one data team and then we'll have data and AI and we'll be done. ' It's a one and done. But that's not how it works. That's not how Google, Facebook, Twitter did it, they had data throughout the organization. Every BU was empowered. It's sales, it's marketing, it's finance, it's engineering. So how do you embed all those data teams and make them actually run fast? And you know, there's this concept of a data mesh which is super important where you can actually decentralize and enable all these teams to focus on their domains and run super fast. And that's really enabled by this Lake house paradigm in the cloud that we're talking about. Where you're open, you're basing it on open standards. You have flexibility in the data types and how they're going to store their data. So you kind of provide a lot of that flexibility, but at the same time, you have sort of centralized governance for it. So absolutely things are changing in the market. >> Well, you're just the professor, the masterclass right here is amazing. Thanks for sharing that insight. You're always got to go out of date and that's why we have you on here. You're amazing, great resource for the community. Ransomware is a huge problem, it's now the government's focus. We're being attacked and we don't know where it's coming from. This business models around cyber that's expanding rapidly. There's real revenue behind it. There's a data problem. It's not just a security problem. So one of the themes in all of these startup showcases is data is ubiquitous in the value propositions. One of them is ransomware. What's your thoughts on ransomware? Is it a data problem? Does cloud help? Some are saying that cloud's got better security with ransomware, then say on premise. What's your vision of how you see this ransomware problem being addressed besides the government taking over? >> Yeah, that's a great question. Let me start by saying, you know, we're a data company, right? And if you say you're a data company, you might as well just said, we're a privacy company, right? It's like some people say, well, what do you think about privacy? Do you guys even do privacy? We're a data company. So yeah, we're a privacy company as well. Like you can't talk about data without talking about privacy. With every customer, with every enterprise. So that's obviously top of mind for us. I do think that in the cloud, security is much better because, you know, vendors like us, we're investing so much resources into security and making sure that we harden the infrastructure and, you know, by actually having all of this infrastructure, we can monitor it, detect if something is, you know, an attack is happening, and we can immediately sort of stop it. So that's different from when it's on prem, you have kind of like the separated duties where the software vendor, which would have been us, doesn't really see what's happening in the data center. So, you know, there's an IT team that didn't develop the software is responsible for the security. So I think things are much better now. I think we're much better set up, but of course, things like cryptocurrencies and so on are making it easier for people to sort of hide. There decentralized networks. So, you know, the attackers are getting more and more sophisticated as well. So that's definitely something that's super important. It's super top of mind. We're all investing heavily into security and privacy because, you know, that's going to be super critical going forward. >> Yeah, we got to move that red line, and figure that out and get more intelligence. Decentralized trends not going away it's going to be more of that, less of the centralized. But centralized does come into play with data. It's a mix, it's not mutually exclusive. And I'll get your thoughts on this. Architectural question with, you know, 5G and the edge coming. Amazon's got that outpost stringent, the wavelength, you're seeing mobile world Congress coming up in this month. The focus on processing data at the edge is a huge issue. And enterprises are now going to be commercial part of that. So architecture decisions are being made in enterprises right now. And this is a big issue. So you mentioned multi-cloud, so tools versus platforms. Now I'm an enterprise buyer and there's no more RFPs. I got all this new choices for startups and growing companies to choose from that are cloud native. I got all kinds of new challenges and opportunities. How do I build my architecture so I don't foreclose a future opportunity. >> Yeah, as I said, look, you're actually right. Cloud is becoming even more and more something that everybody's adopting, but at the same time, there is this thing that the edge is also more and more important. And the connectivity between those two and making sure that you can really do that efficiently. My ask from enterprises, and I think this is top of mind for all the enterprise architects is, choose open because that way you can avoid locking yourself in. So that's one thing that's really, really important. In the past, you know, all these vendors that locked you in, and then you try to move off of them, they were highly innovative back in the day. In the 80's and the 90's, there were the best companies. You gave them all your data and it was fantastic. But then because you were locked in, they didn't need to innovate anymore. And you know, they focused on margins instead. And then over time, the innovation stopped and now you were kind of locked in. So I think openness is really important. I think preserving optionality with multi-cloud because we see the different clouds have different strengths and weaknesses and it changes over time. All right. Early on AWS was the only game that either showed up with much better security, active directory, and so on. Now Google with AI capabilities, which one's going to win, which one's going to be better. Actually, probably all three are going to be around. So having that optionality that you can pick between the three and then artificial intelligence. I think that's going to be the key to the future. You know, you asked about security earlier. That's how people detect zero day attacks, right? You ask about the edge, same thing there, that's where the predictions are going to happen. So make sure that you invest in AI and artificial intelligence very early on because it's not something you can just bolt on later on and have a little data team somewhere that then now you have AI and it's one and done. >> All right. Great insight. I've got to ask you, the folks may or may not know, but you're a professor at Berkeley as well, done a lot of great work. That's where you kind of came out of when Data bricks was formed. And the Berkeley basically was it invented distributed computing back in the 80's. I remember I was breaking in when Unix was proprietary, when software wasn't open you actually had the deal that under the table to get code. Now it's all open. Isn't the internet now with distributed computing and how interconnects are happening. I mean, the internet didn't break during the pandemic, which proves the benefit of the internet. And that's a positive. But as you start seeing edge, it's essentially distributed computing. So I got to ask you from a computer science standpoint. What do you see as the key learnings or connect the dots for how this distributed model will work? I see hybrids clearly, hybrid cloud is clearly the operating model but if you take it to the next level of distributed computing, what are some of the key things that you look for in the next five years as this starts to be completely interoperable, obviously software is going to drive a lot of it. What's your vision on that? >> Yeah, I mean, you know, so Berkeley, you're right for the gigs, you know, there was a now project 20, 30 years ago that basically is how we do things. There was a project on how you search in the very early on with Inktomi that became how Google and everybody else to search today. So workday was super, super early, sometimes way too early. And that was actually the mistake. Was that they were so early that people said that that stuff doesn't work. And then 20 years later you were invented. So I think 2009, Berkeley published just above the clouds saying the cloud is the future. At that time, most industry leaders said, that's just, you know, that doesn't work. Today, recently they published a research paper called, Sky Computing. So sky computing is what you get above the clouds, right? So we have the cloud as the future, the next level after that is the sky. That's one on top of them. That's what multi-cloud is. So that's a lot of the research at Berkeley, you know, into distributed systems labs is about this. And we're excited about that. Then we're one of the sky computing vendors out there. So I think you're going to see much more innovation happening at the sky level than at the compute level where you needed all those DevOps and SRE people to like, you know, build everything manually themselves. I can just see the memes now coming Ali, sky net, star track. You've got space too, by the way, space is another frontier that is seeing a lot of action going on because now the surface area of data with satellites is huge. So again, I know you guys are doing a lot of business with folks in that vertical where you starting to see real time data acquisition coming from these satellites. What's your take on the whole space as the, not the final frontier, but certainly as a new congested and contested space for, for data? >> Well, I mean, as a data vendor, we see a lot of, you know, alternative data sources coming in and people aren't using machine learning< AI to eat out signal out of the, you know, massive amounts of imagery that's coming out of these satellites. So that's actually a pretty common in FinTech, which is a vertical for us. And also sort of in the public sector, lots of, lots of, lots of satellites, imagery data that's coming. And these are massive volumes. I mean, it's like huge data sets and it's a super, super exciting what they can do. Like, you know, extracting signal from the satellite imagery is, and you know, being able to handle that amount of data, it's a challenge for all the companies that we work with. So we're excited about that too. I mean, definitely that's a trend that's going to continue. >> All right. I'm super excited for you. And thanks for coming on The Cube here for our keynote. I got to ask you a final question. As you think about the future, I see your company has achieved great success in a very short time, and again, you guys done the work, I've been following your company as you know. We've been been breaking that Data bricks story for a long time. I've been excited by it, but now what's changed. You got to start thinking about the next 20 miles stair when you look at, you know, the sky computing, you're thinking about these new architectures. As the CEO, your job is to one, not run out of money which you don't have to worry about that anymore, so hiring. And then, you got to figure out that next 20 miles stair as a company. What's that going on in your mind? Take us through your mindset of what's next. And what do you see out in that landscape? >> Yeah, so what I mentioned around Sky company optionality around multi-cloud, you're going to see a lot of capabilities around that. Like how do you get multi-cloud disaster recovery? How do you leverage the best of all the clouds while at the same time not having to just pick one? So there's a lot of innovation there that, you know, we haven't announced yet, but you're going to see a lot of it over the next many years. Things that you can do when you have the optionality across the different parts. And the second thing that's really exciting for us is bringing AI to the masses. Democratizing data and AI. So how can you actually apply machine learning to machine learning? How can you automate machine learning? Today machine learning is still quite complicated and it's pretty advanced. It's not going to be that way 10 years from now. It's going to be very simple. Everybody's going to have it at their fingertips. So how do we apply machine learning to machine learning? It's called auto ML, automatic, you know, machine learning. So that's an area, and that's not something that can be done with, right? But the goal is to eventually be able to automate a way the whole machine learning engineer and the machine learning data scientist altogether. >> You know it's really fun and talking with you is that, you know, for years we've been talking about this inside the ropes, inside the industry, around the future. Now people starting to get some visibility, the pandemics forced that. You seeing the bad projects being exposed. It's like the tide pulled out and you see all the scabs and bad projects that were justified old guard technologies. If you get it right you're on a good wave. And this is clearly what we're seeing. And you guys example of that. So as enterprises realize this, that they're going to have to look double down on the right projects and probably trash the bad projects, new criteria, how should people be thinking about buying? Because again, we talked about the RFP before. I want to kind of circle back because this is something that people are trying to figure out. You seeing, you know, organic, you come in freemium models as cloud scale becomes the advantage in the lock-in frankly seems to be the value proposition. The more value you provide, the more lock-in you get. Which sounds like that's the way it should be versus proprietary, you know, protocols. The protocol is value. How should enterprises organize their teams? Is it end to end workflows? Is it, and how should they evaluate the criteria for these technologies that they want to buy? >> Yeah, that's a great question. So I, you know, it's very simple, try to future proof your decision-making. Make sure that whatever you're doing is not blocking your in. So whatever decision you're making, what if the world changes in five years, make sure that if you making a mistake now, that's not going to bite you in about five years later. So how do you do that? Well, open source is great. If you're leveraging open-source, you can try it out already. You don't even need to talk to any vendor. Your teams can already download it and try it out and get some value out of it. If you're in the cloud, this pay as you go models, you don't have to do a big RFP and commit big. You can try it, pay the vendor, pay as you go, $10, $15. It doesn't need to be a million dollar contract and slowly grow as you're providing value. And then make sure that you're not just locking yourself in to one cloud or, you know, one particular vendor. As much as possible preserve your optionality because then that's not a one-way door. If it turns out later you want to do something else, you can, you know, pick other things as well. You're not locked in. So that's what I would say. Keep that top of mind that you're not locking yourself into a particular decision that you made today, that you might regret in five years. >> I really appreciate you coming on and sharing your with our community and The Cube. And as always great to see you. I really enjoy your clubhouse talks, and I really appreciate how you give back to the community. And I want to thank you for coming on and taking the time with us today. >> Thanks John, always appreciate talking to you. >> Okay Ali Ghodsi, CEO of Data bricks, a success story that proves the validation of cloud scale, open and create value, values the new lock-in. So Natalie, back to you for continuing coverage. >> That was a terrific interview John, but I'd love to get Dave's insights first. What were your takeaways, Dave? >> Well, if we have more time I'll tell you how Data bricks got to where they are today, but I'll say this, the most important thing to me that Allie said was he conveyed a very clear understanding of what data companies are outright and are getting ready. Talked about four things. There's not one data team, there's many data teams. And he talked about data is decentralized, and data has to have context and that context lives in the business. He said, look, think about it. The way that the data companies would get it right, they get data in teams and sales and marketing and finance and engineering. They all have their own data and data teams. And he referred to that as a data mesh. That's a term that is your mock, the Gany coined and the warehouse of the data lake it's merely a node in that global message. It meshes discoverable, he talked about federated governance, and Data bricks, they're breaking the model of shoving everything into a single repository and trying to make that the so-called single version of the truth. Rather what they're doing, which is right on is putting data in the hands of the business owners. And that's how true data companies do. And the last thing you talked about with sky computing, which I loved, it's that future layer, we talked about multi-cloud a lot that abstracts the underlying complexity of the technical details of the cloud and creates additional value on top. I always say that the cloud players like Amazon have given the gift to the world of 100 billion dollars a year they spend in CapEx. Thank you. Now we're going to innovate on top of it. Yeah. And I think the refactoring... >> Hope by John. >> That was great insight and I totally agree. The refactoring piece too was key, he brought that home. But to me, I think Data bricks that Ali shared there and why he's been open and sharing a lot of his insights and the community. But what he's not saying, cause he's humble and polite is they cracked the code on the enterprise, Dave. And to Dave's points exactly reason why they did it, they saw an opportunity to make it easier, at that time had dupe was the rage, and they just made it easier. They was smart, they made good bets, they had a good formula and they cracked the code with the enterprise. They brought it in and they brought value. And see that's the key to the cloud as Dave pointed out. You get replatform with the cloud, then you refactor. And I think he pointed out the multi-cloud and that really kind of teases out the whole future and landscape, which is essentially distributed computing. And I think, you know, companies are starting to figure that out with hybrid and this on premises and now super edge I call it, with 5G coming. So it's just pretty incredible. >> Yeah. Data bricks, IPO is coming and people should know. I mean, what everybody, they created spark as you know John and everybody thought they were going to do is mimic red hat and sell subscriptions and support. They didn't, they developed a managed service and they embedded AI tools to simplify data science. So to your point, enterprises could buy instead of build, we know this. Enterprises will spend money to make things simpler. They don't have the resources, and so this was what they got right was really embedding that, making a building a managed service, not mimicking the kind of the red hat model, but actually creating a new value layer there. And that's big part of their success. >> If I could just add one thing Natalie to that Dave saying is really right on. And as an enterprise buyer, if we go the other side of the equation, it used to be that you had to be a known company, get PR, you fill out RFPs, you had to meet all the speeds. It's like going to the airport and get a swab test, and get a COVID test and all kinds of mechanisms to like block you and filter you. Most of the biggest success stories that have created the most value for enterprises have been the companies that nobody's understood. And Andy Jazz's famous quote of, you know, being misunderstood is actually a good thing. Data bricks was very misunderstood at the beginning and no one kind of knew who they were but they did it right. And so the enterprise buyers out there, don't be afraid to test the startups because you know the next Data bricks is out there. And I think that's where I see the psychology changing from the old IT buyers, Dave. It's like, okay, let's let's test this company. And there's plenty of ways to do that. He illuminated those premium, small pilots, you don't need to go on these big things. So I think that is going to be a shift in how companies going to evaluate startups. >> Yeah. Think about it this way. Why should the large banks and insurance companies and big manufacturers and pharma companies, governments, why should they burn resources managing containers and figuring out data science tools if they can just tap into solutions like Data bricks which is an AI platform in the cloud and let the experts manage all that stuff. Think about how much money in time that saves enterprises. >> Yeah, I mean, we've got 15 companies here we're showcasing this batch and this season if you call it. That episode we are going to call it? They're awesome. Right? And the next 15 will be the same. And these companies could be the next billion dollar revenue generator because the cloud enables that day. I think that's the exciting part. >> Well thank you both so much for these insights. Really appreciate it. AWS startup showcase highlights the innovation that helps startups succeed. And no one knows that better than our very next guest, Jeff Barr. Welcome to the show and I will send this interview now to Dave and John and see you just in the bit. >> Okay, hey Jeff, great to see you. Thanks for coming on again. >> Great to be back. >> So this is a regular community segment with Jeff Barr who's a legend in the industry. Everyone knows your name. Everyone knows that. Congratulations on your recent blog posts we have reading. Tons of news, I want to get your update because 5G has been all over the news, mobile world congress is right around the corner. I know Bill Vass was a keynote out there, virtual keynote. There's a lot of Amazon discussion around the edge with wavelength. Specifically, this is the outpost piece. And I know there is news I want to get to, but the top of mind is there's massive Amazon expansion and the cloud is going to the edge, it's here. What's up with wavelength. Take us through the, I call it the power edge, the super edge. >> Well, I'm really excited about this mostly because it gives a lot more choice and flexibility and options to our customers. This idea that with wavelength we announced quite some time ago, at least quite some time ago if we think in cloud years. We announced that we would be working with 5G providers all over the world to basically put AWS in the telecom providers data centers or telecom centers, so that as their customers build apps, that those apps would take advantage of the low latency, the high bandwidth, the reliability of 5G, be able to get to some compute and storage services that are incredibly close geographically and latency wise to the compute and storage that is just going to give customers this new power and say, well, what are the cool things we can build? >> Do you see any correlation between wavelength and some of the early Amazon services? Because to me, my gut feels like there's so much headroom there. I mean, I was just riffing on the notion of low latency packets. I mean, just think about the applications, gaming and VR, and metaverse kind of cool stuff like that where having the edge be that how much power there. It just feels like a new, it feels like a new AWS. I mean, what's your take? You've seen the evolutions and the growth of a lot of the key services. Like EC2 and SA3. >> So welcome to my life. And so to me, the way I always think about this is it's like when I go to a home improvement store and I wander through the aisles and I often wonder through with no particular thing that I actually need, but I just go there and say, wow, they've got this and they've got this, they've got this other interesting thing. And I just let my creativity run wild. And instead of trying to solve a problem, I'm saying, well, if I had these different parts, well, what could I actually build with them? And I really think that this breadth of different services and locations and options and communication technologies. I suspect a lot of our customers and customers to be and are in this the same mode where they're saying, I've got all this awesomeness at my fingertips, what might I be able to do with it? >> He reminds me when Fry's was around in Palo Alto, that store is no longer here but it used to be back in the day when it was good. It was you go in and just kind of spend hours and then next thing you know, you built a compute. Like what, I didn't come in here, whether it gets some cables. Now I got a motherboard. >> I clearly remember Fry's and before that there was the weird stuff warehouse was another really cool place to hang out if you remember that. >> Yeah I do. >> I wonder if I could jump in and you guys talking about the edge and Jeff I wanted to ask you about something that is, I think people are starting to really understand and appreciate what you did with the entrepreneur acquisition, what you do with nitro and graviton, and really driving costs down, driving performance up. I mean, there's like a compute Renaissance. And I wonder if you could talk about the importance of that at the edge, because it's got to be low power, it has to be low cost. You got to be doing processing at the edge. What's your take on how that's evolving? >> Certainly so you're totally right that we started working with and then ultimately acquired Annapurna labs in Israel a couple of years ago. I've worked directly with those folks and it's really awesome to see what they've been able to do. Just really saying, let's look at all of these different aspects of building the cloud that were once effectively kind of somewhat software intensive and say, where does it make sense to actually design build fabricate, deploy custom Silicon? So from putting up the system to doing all kinds of additional kinds of security checks, to running local IO devices, running the NBME as fast as possible to support the EBS. Each of those things has been a contributing factor to not just the power of the hardware itself, but what I'm seeing and have seen for the last probably two or three years at this point is the pace of innovation on instance types just continues to get faster and faster. And it's not just cranking out new instance types because we can, it's because our awesomely diverse base of customers keeps coming to us and saying, well, we're happy with what we have so far, but here's this really interesting new use case. And we needed a different ratio of memory to CPU, or we need more cores based on the amount of memory, or we needed a lot of IO bandwidth. And having that nitro as the base lets us really, I don't want to say plug and play, cause I haven't actually built this myself, but it seems like they can actually put the different elements together, very very quickly and then come up with new instance types that just our customers say, yeah, that's exactly what I asked for and be able to just do this entire range of from like micro and nano sized all the way up to incredibly large with incredible just to me like, when we talk about terabytes of memory that are just like actually just RAM memory. It's like, that's just an inconceivably large number by the standards of where I started out in my career. So it's all putting this power in customer hands. >> You used the term plug and play, but it does give you that nitro gives you that optionality. And then other thing that to me is really exciting is the way in which ISVs are writing to whatever's underneath. So you're making that, you know, transparent to the users so I can choose as a customer, the best price performance for my workload and that that's just going to grow that ISV portfolio. >> I think it's really important to be accurate and detailed and as thorough as possible as we launch each one of these new instance types with like what kind of processor is in there and what clock speed does it run at? What kind of, you know, how much memory do we have? What are the, just the ins and outs, and is it Intel or arm or AMD based? It's such an interesting to me contrast. I can still remember back in the very very early days of back, you know, going back almost 15 years at this point and effectively everybody said, well, not everybody. A few people looked and said, yeah, we kind of get the value here. Some people said, this just sounds like a bunch of generic hardware, just kind of generic hardware in Iraq. And even back then it was something that we were very careful with to design and optimize for use cases. But this idea that is generic is so, so, so incredibly inaccurate that I think people are now getting this. And it's okay. It's fine too, not just for the cloud, but for very specific kinds of workloads and use cases. >> And you guys have announced obviously the performance improvements on a lamb** does getting faster, you got the per billing, second billings on windows and SQL server on ECE too**. So I mean, obviously everyone kind of gets that, that's been your DNA, keep making it faster, cheaper, better, easier to use. But the other area I want to get your thoughts on because this is also more on the footprint side, is that the regions and local regions. So you've got more region news, take us through the update on the expansion on the footprint of AWS because you know, a startup can come in and these 15 companies that are here, they're global with AWS, right? So this is a major benefit for customers around the world. And you know, Ali from Data bricks mentioned privacy. Everyone's a privacy company now. So the huge issue, take us through the news on the region. >> Sure, so the two most recent regions that we announced are in the UAE and in Israel. And we generally like to pre-announce these anywhere from six months to two years at a time because we do know that the customers want to start making longer term plans to where they can start thinking about where they can do their computing, where they can store their data. I think at this point we now have seven regions under construction. And, again it's all about customer trice. Sometimes it's because they have very specific reasons where for based on local laws, based on national laws, that they must compute and restore within a particular geographic area. Other times I say, well, a lot of our customers are in this part of the world. Why don't we pick a region that is as close to that part of the world as possible. And one really important thing that I always like to remind our customers of in my audience is, anything that you choose to put in a region, stays in that region unless you very explicitly take an action that says I'd like to replicate it somewhere else. So if someone says, I want to store data in the US, or I want to store it in Frankfurt, or I want to store it in Sao Paulo, or I want to store it in Tokyo or Osaka. They get to make that very specific choice. We give them a lot of tools to help copy and replicate and do cross region operations of various sorts. But at the heart, the customer gets to choose those locations. And that in the early days I think there was this weird sense that you would, you'd put things in the cloud that would just mysteriously just kind of propagate all over the world. That's never been true, and we're very very clear on that. And I just always like to reinforce that point. >> That's great stuff, Jeff. Great to have you on again as a regular update here, just for the folks watching and don't know Jeff he'd been blogging and sharing. He'd been the one man media band for Amazon it's early days. Now he's got departments, he's got peoples on doing videos. It's an immediate franchise in and of itself, but without your rough days we wouldn't have gotten all the great news we subscribe to. We watch all the blog posts. It's essentially the flow coming out of AWS which is just a tsunami of a new announcements. Always great to read, must read. Jeff, thanks for coming on, really appreciate it. That's great. >> Thank you John, great to catch up as always. >> Jeff Barr with AWS again, and follow his stuff. He's got a great audience and community. They talk back, they collaborate and they're highly engaged. So check out Jeff's blog and his social presence. All right, Natalie, back to you for more coverage. >> Terrific. Well, did you guys know that Jeff took a three week AWS road trip across 15 cities in America to meet with cloud computing enthusiasts? 5,500 miles he drove, really incredible I didn't realize that. Let's unpack that interview though. What stood out to you John? >> I think Jeff, Barr's an example of what I call direct to audience a business model. He's been doing it from the beginning and I've been following his career. I remember back in the day when Amazon was started, he was always building stuff. He's a builder, he's classic. And he's been there from the beginning. At the beginning he was just the blog and it became a huge audience. It's now morphed into, he was power blogging so hard. He has now support and he still does it now. It's basically the conduit for information coming out of Amazon. I think Jeff has single-handedly made Amazon so successful at the community developer level, and that's the startup action happened and that got them going. And I think he deserves a lot of the success for AWS. >> And Dave, how about you? What is your reaction? >> Well I think you know, and everybody knows about the cloud and back stop X** and agility, and you know, eliminating the undifferentiated, heavy lifting and all that stuff. And one of the things that's often overlooked which is why I'm excited to be part of this program is the innovation. And the innovation comes from startups, and startups start in the cloud. And so I think that that's part of the flywheel effect. You just don't see a lot of startups these days saying, okay, I'm going to do something that's outside of the cloud. There are some, but for the most part, you know, if you saw in software, you're starting in the cloud, it's so capital efficient. I think that's one thing, I've throughout my career. I've been obsessed with every part of the stack from whether it's, you know, close to the business process with the applications. And right now I'm really obsessed with the plumbing, which is why I was excited to talk about, you know, the Annapurna acquisition. Amazon bought and a part of the $350 million, it's reported, you know, maybe a little bit more, but that isn't an amazing acquisition. And the reason why that's so important is because Amazon is continuing to drive costs down, drive performance up. And in my opinion, leaving a lot of the traditional players in their dust, especially when it comes to the power and cooling. You have often overlooked things. And the other piece of the interview was that Amazon is actually getting ISVs to write to these new platforms so that you don't have to worry about there's the software run on this chip or that chip, or x86 or arm or whatever it is. It runs. And so I can choose the best price performance. And that's where people don't, they misunderstand, you always say it John, just said that people are misunderstood. I think they misunderstand, they confused, you know, the price of the cloud with the cost of the cloud. They ignore all the labor costs that are associated with that. And so, you know, there's a lot of discussion now about the cloud tax. I just think the pace is accelerating. The gap is not closing, it's widening. >> If you look at the one question I asked them about wavelength and I had a follow up there when I said, you know, we riff on it and you see, he lit up like he beam was beaming because he said something interesting. It's not that there's a problem to solve at this opportunity. And he conveyed it to like I said, walking through Fry's. But like, you go into a store and he's a builder. So he sees opportunity. And this comes back down to the Martine Casada paradox posts he wrote about do you optimize for CapEx or future revenue? And I think the tell sign is at the wavelength edge piece is going to be so creative and that's going to open up massive opportunities. I think that's the place to watch. That's the place I'm watching. And I think startups going to come out of the woodwork because that's where the action will be. And that's just Amazon at the edge, I mean, that's just cloud at the edge. I think that is going to be very effective. And his that's a little TeleSign, he kind of revealed a little bit there, a lot there with that comment. >> Well that's a to be continued conversation. >> Indeed, I would love to introduce our next guest. We actually have Soma on the line. He's the managing director at Madrona venture group. Thank you Soma very much for coming for our keynote program. >> Thank you Natalie and I'm great to be here and will have the opportunity to spend some time with you all. >> Well, you have a long to nerd history in the enterprise. How would you define the modern enterprise also known as cloud scale? >> Yeah, so I would say I have, first of all, like, you know, we've all heard this now for the last, you know, say 10 years or so. Like, software is eating the world. Okay. Put it another way, we think about like, hey, every enterprise is a software company first and foremost. Okay. And companies that truly internalize that, that truly think about that, and truly act that way are going to start up, continue running well and things that don't internalize that, and don't do that are going to be left behind sooner than later. Right. And the last few years you start off thing and not take it to the next level and talk about like, not every enterprise is not going through a digital transformation. Okay. So when you sort of think about the world from that lens. Okay. Modern enterprise has to think about like, and I am first and foremost, a technology company. I may be in the business of making a car art, you know, manufacturing paper, or like you know, manufacturing some healthcare products or what have you got out there. But technology and software is what is going to give me a unique, differentiated advantage that's going to let me do what I need to do for my customers in the best possible way [Indistinct]. So that sort of level of focus, level of execution, has to be there in a modern enterprise. The other thing is like not every modern enterprise needs to think about regular. I'm competing for talent, not anymore with my peers in my industry. I'm competing for technology talent and software talent with the top five technology companies in the world. Whether it is Amazon or Facebook or Microsoft or Google, or what have you cannot think, right? So you really have to have that mindset, and then everything flows from that. >> So I got to ask you on the enterprise side again, you've seen many ways of innovation. You've got, you know, been in the industry for many, many years. The old way was enterprises want the best proven product and the startups want that lucrative contract. Right? Yeah. And get that beach in. And it used to be, and we addressed this in our earlier keynote with Ali and how it's changing, the buyers are changing because the cloud has enabled this new kind of execution. I call it agile, call it what you want. Developers are driving modern applications, so enterprises are still, there's no, the playbooks evolving. Right? So we see that with the pandemic, people had needs, urgent needs, and they tried new stuff and it worked. The parachute opened as they say. So how do you look at this as you look at stars, you're investing in and you're coaching them. What's the playbook? What's the secret sauce of how to crack the enterprise code today. And if you're an enterprise buyer, what do I need to do? I want to be more agile. Is there a clear path? Is there's a TSA to let stuff go through faster? I mean, what is the modern playbook for buying and being a supplier? >> That's a fantastic question, John, because I think that sort of playbook is changing, even as we speak here currently. A couple of key things to understand first of all is like, you know, decision-making inside an enterprise is getting more and more de-centralized. Particularly decisions around what technology to use and what solutions to use to be able to do what people need to do. That decision making is no longer sort of, you know, all done like the CEO's office or the CTO's office kind of thing. Developers are more and more like you rightly said, like sort of the central of the workflow and the decision making process. So it'll be who both the enterprises, as well as the startups to really understand that. So what does it mean now from a startup perspective, from a startup perspective, it means like, right. In addition to thinking about like hey, not do I go create an enterprise sales post, do I sell to the enterprise like what I might have done in the past? Is that the best way of moving forward, or should I be thinking about a product led growth go to market initiative? You know, build a product that is easy to use, that made self serve really works, you know, get the developers to start using to see the value to fall in love with the product and then you think about like hey, how do I go translate that into a contract with enterprise. Right? And more and more what I call particularly, you know, startups and technology companies that are focused on the developer audience are thinking about like, you know, how do I have a bottom up go to market motion? And sometime I may sort of, you know, overlap that with the top down enterprise sales motion that we know that has been going on for many, many years or decades kind of thing. But really this product led growth bottom up a go to market motion is something that we are seeing on the rise. I would say they're going to have more than half the startup that we come across today, have that in some way shape or form. And so the enterprise also needs to understand this, the CIO or the CTO needs to know that like hey, I'm not decision-making is getting de-centralized. I need to empower my engineers and my engineering managers and my engineering leaders to be able to make the right decision and trust them. I'm going to give them some guard rails so that I don't find myself in a soup, you know, sometime down the road. But once I give them the guard rails, I'm going to enable people to make the decisions. People who are closer to the problem, to make the right decision. >> Well Soma, what are some of the ways that startups can accelerate their enterprise penetration? >> I think that's another good question. First of all, you need to think about like, Hey, what are enterprises wanting to rec? Okay. If you start off take like two steps back and think about what the enterprise is really think about it going. I'm a software company, but I'm really manufacturing paper. What do I do? Right? The core thing that most enterprises care about is like, hey, how do I better engage with my customers? How do I better serve my customers? And how do I do it in the most optimal way? At the end of the day that's what like most enterprises really care about. So startups need to understand, what are the problems that the enterprise is trying to solve? What kind of tools and platform technologies and infrastructure support, and, you know, everything else that they need to be able to do what they need to do and what only they can do in the most optimal way. Right? So to the extent you are providing either a tool or platform or some technology that is going to enable your enterprise to make progress on what they want to do, you're going to get more traction within the enterprise. In other words, stop thinking about technology, and start thinking about the customer problem that they want to solve. And the more you anchor your company, and more you anchor your conversation with the customer around that, the more the enterprise is going to get excited about wanting to work with you. >> So I got to ask you on the enterprise and developer equation because CSOs and CXOs, depending who you talk to have that same answer. Oh yeah. In the 90's and 2000's, we kind of didn't, we throttled down, we were using the legacy developer tools and cloud came and then we had to rebuild and we didn't really know what to do. So you seeing a shift, and this is kind of been going on for at least the past five to eight years, a lot more developers being hired yet. I mean, at FinTech is clearly a vertical, they always had developers and everyone had developers, but there's a fast ramp up of developers now and the role of open source has changed. Just looking at the participation. They're not just consuming open source, open source is part of the business model for mainstream enterprises. How is this, first of all, do you agree? And if so, how has this changed the course of an enterprise human resource selection? How they're organized? What's your vision on that? >> Yeah. So as I mentioned earlier, John, in my mind the first thing is, and this sort of, you know, like you said financial services has always been sort of hiring people [Indistinct]. And this is like five-year old story. So bear with me I'll tell you the firewall story and then come to I was trying to, the cloud CIO or the Goldman Sachs. Okay. And this is five years ago when people were still like, hey, is this cloud thing real and now is cloud going to take over the world? You know, am I really ready to put my data in the cloud? So there are a lot of questions and conversations can affect. The CIO of Goldman Sachs told me two things that I remember to this day. One is, hey, we've got a internal edict. That we made a decision that in the next five years, everything in Goldman Sachs is going to be on the public law. And I literally jumped out of the chair and I said like now are you going to get there? And then he laughed and said like now it really doesn't matter whether we get there or not. We want to set the tone, set the direction for the organization that hey, public cloud is here. Public cloud is there. And we need to like, you know, move as fast as we realistically can and think about all the financial regulations and security and privacy. And all these things that we care about deeply. But given all of that, the world is going towards public load and we better be on the leading edge as opposed to the lagging edge. And the second thing he said, like we're talking about like hey, how are you hiring, you know, engineers at Goldman Sachs Canada? And he said like in hey, I sort of, my team goes out to the top 20 schools in the US. And the people we really compete with are, and he was saying this, Hey, we don't compete with JP Morgan or Morgan Stanley, or pick any of your favorite financial institutions. We really think about like, hey, we want to get the best talent into Goldman Sachs out of these schools. And we really compete head to head with Google. We compete head to head with Microsoft. We compete head to head with Facebook. And we know that the caliber of people that we want to get is no different than what these companies want. If you want to continue being a successful, leading it, you know, financial services player. That sort of tells you what's going on. You also talked a little bit about like hey, open source is here to stay. What does that really mean kind of thing. In my mind like now, you can tell me that I can have from given my pedigree at Microsoft, I can tell you that we were the first embraces of open source in this world. So I'll say that right off the bat. But having said that we did in our turn around and said like, hey, this open source is real, this open source is going to be great. How can we embrace and how can we participate? And you fast forward to today, like in a Microsoft is probably as good as open source as probably any other large company I would say. Right? Including like the work that the company has done in terms of acquiring GitHub and letting it stay true to its original promise of open source and community can I think, right? I think Microsoft has come a long way kind of thing. But the thing that like in all these enterprises need to think about is you want your developers to have access to the latest and greatest tools. To the latest and greatest that the software can provide. And you really don't want your engineers to be reinventing the wheel all the time. So there is something available in the open source world. Go ahead, please set up, think about whether that makes sense for you to use it. And likewise, if you think that is something you can contribute to the open source work, go ahead and do that. So it's really a two way somebody Arctic relationship that enterprises need to have, and they need to enable their developers to want to have that symbiotic relationship. >> Soma, fantastic insights. Thank you so much for joining our keynote program. >> Thank you Natalie and thank you John. It was always fun to chat with you guys. Thank you. >> Thank you. >> John we would love to get your quick insight on that. >> Well I think first of all, he's a prolific investor the great from Madrona venture partners, which is well known in the tech circles. They're in Seattle, which is in the hub of I call cloud city. You've got Amazon and Microsoft there. He'd been at Microsoft and he knows the developer ecosystem. And reason why I like his perspective is that he understands the value of having developers as a core competency in Microsoft. That's their DNA. You look at Microsoft, their number one thing from day one besides software was developers. That was their army, the thousand centurions that one won everything for them. That has shifted. And he brought up open source, and .net and how they've embraced Linux, but something that tele before he became CEO, we interviewed him in the cube at an Xcel partners event at Stanford. He was open before he was CEO. He was talking about opening up. They opened up a lot of their open source infrastructure projects to the open compute foundation early. So they had already had that going and at that price, since that time, the stock price of Microsoft has skyrocketed because as Ali said, open always wins. And I think that is what you see here, and as an investor now he's picking in startups and investing in them. He's got to read the tea leaves. He's got to be in the right side of history. So he brings a great perspective because he sees the old way and he understands the new way. That is the key for success we've seen in the enterprise and with the startups. The people who get the future, and can create the value are going to win. >> Yeah, really excellent point. And just really quickly. What do you think were some of our greatest hits on this hour of programming? >> Well first of all I'm really impressed that Ali took the time to come join us because I know he's super busy. I think they're at a $28 billion valuation now they're pushing a billion dollars in revenue, gap revenue. And again, just a few short years ago, they had zero software revenue. So of these 15 companies we're showcasing today, you know, there's a next Data bricks in there. They're all going to be successful. They already are successful. And they're all on this rocket ship trajectory. Ali is smart, he's also got the advantage of being part of that Berkeley community which they're early on a lot of things now. Being early means you're wrong a lot, but you're also right, and you're right big. So Berkeley and Stanford obviously big areas here in the bay area as research. He is smart, He's got a great team and he's really open. So having him share his best practices, I thought that was a great highlight. Of course, Jeff Barr highlighting some of the insights that he brings and honestly having a perspective of a VC. And we're going to have Peter Wagner from wing VC who's a classic enterprise investors, super smart. So he'll add some insight. Of course, one of the community session, whenever our influencers coming on, it's our beat coming on at the end, as well as Katie Drucker. Another Madrona person is going to talk about growth hacking, growth strategies, but yeah, sights Raleigh coming on. >> Terrific, well thank you so much for those insights and thank you to everyone who is watching the first hour of our live coverage of the AWS startup showcase for myself, Natalie Ehrlich, John, for your and Dave Vellante we want to thank you very much for watching and do stay tuned for more amazing content, as well as a special live segment that John Furrier is going to be hosting. It takes place at 12:30 PM Pacific time, and it's called cracking the code, lessons learned on how enterprise buyers evaluate new startups. Don't go anywhere.

>>from around the globe. It's the Cube presenting Cuban cloud brought >>to you by silicon angle. Okay, we're back. Here. Live Cuban Cloud. And this is Dave. Want with my co host, John Ferrier Were all remote. We're getting into the analyst power half hour. Really pleased to have Maribel Lopez here. She's the principal and founder of Lopez Research and Zias Caraballo, who is the principal and founder of ZK research. Guys, great to see you. Let's get into it. How you doing? >>Great. How you been? Good, >>thanks. Really good. John's hanging in there quarantining and, uh, all healthy, So I hope you guys are too. Hey, Mary, But let's start with you. You know, here we are on 2021 you know, just exited one of the strangest years, if not the strangest year of our lives. But looking back in the past decade of cloud and we're looking forward. How do you see that? Where do we come from? Where we at and where we going >>When we obviously started with the whole let's build a public cloud and everything was about public cloud. Uh, then we went thio the notion of private cloud than we had hybrid cloud and multi cloud. So we've done a lot of different clouds right now. And I think where we are today is that there's a healthy recognition on the cloud computing providers that you need to give it to the customers the way they want it, not the way you've decided to build it. So how do you meet them where they are so that they can have a cloud like experience wherever they want their data to be? >>Yes and yes, you've, you know, observed, This is well, in the early days of cloud, you heard a lot of rhetoric. It was private cloud And and then now we're, you know, hearing a lot of multi cloud and so forth. But initially, a lot of the traditional vendors kind of pooh poohed it. They called us analysts. We said we were all cloud crazy, but they seem to have got their religion. >>Well, everything. Everyone's got a definition of cloud, but I actually think we are right in the midst of another transformation of clouds Miracle talked about. We went from, you know, private clouds, which is really hosting the public cloud to multi cloud hybrid cloud. And if you look at the last post that put on Silicon Angle, which was talking about five acquisition of Volterra, I actually think we're in the midst of the transition to what's called distributed Club, where if you look at modernized cloud apps today, they're actually made up of services from different clouds on also distributed edge locations. And that's gonna have a pretty profound impact on the way we build out, because those distributed edges be a telco edge, cellular vagina. Th whatever the services that lived there are much more ephemeral in nature, right? So the way we secure the way we connect changes quite a bit. But I think that the great thing about Cloud is we've seen several several evolutionary changes. So what the definition is and we're going through that now, which is which is pretty cool to think about, right? It's not a static thing. Um, it's, uh, you know, it's a it's an ongoing transition. But I think, uh, you know, we're moving into this distributed Cloudera, which to me is a lot more complex than what we're dealing with in the Palace. >>I'm actually pretty excited about that because I think that this move toe edge and the distribution that you've talked about, it's like we now have processing everywhere. We've got it on devices, we've got it in, cars were moving, the data centers closer and closer to where the action's happening. And I think that's gonna be a huge trend for 2021. Is that distributed that you were talking about a lot of edge discussion? You >>know what? The >>reason we're doing This, too, is we want. It's not just we're moving the data closer to the user, right? And some. If you think you brought up the autonomous vehicle right in the car being an edge, you think of the data that generates right? There's some things such as the decision to stop or not right that should be done in car. I don't wanna transport that data all the way back to Google him back to decide whether I want to stop. You could also use the same data determine whether drivers driving safely for insurance purposes, right? So the same data give me located at the edge or in a centralized cloud for different purposes, and I think that's what you know, kind of cool about this is we're being able to use our data and much different ways. Now. >>You know, it's interesting is it's so complex. It's mind blowing because this is distributed computing. Everyone kind of agrees this is where it is. But if you think about the complexity and I want to get your guys reaction to this because you know some of the like side fringe trend discussions are data sovereignty, misinformation as a vulnerability. Okay, you get the chips now you got gravitas on with Amazon in front. Apple's got their own chips. Intel is gonna do a whole new direction. So you've got tons of computer. And then you mentioned the ephemeral nature. How do you manage those? What's the observe ability look like? They're what's the trust equation? So all these things kind of play into it. It sounds almost mind blowing, just even thinking about it. But how do you guys, this analyst tryto understand where someone's either blowing bullshit or kind of like has the real deal? Because all those things come into play? I mean, you could have a misinformation campaign targeting the car. Let's say Hey, you know that that data is needs to be. This is this is misinformation who's a >>in a lot of ways, this creates almost unprecedented opportunity now for for starts and for companies to transform right. The fundamental tenet of my research has always been share shifts happen when markets transition and we're in the middle of the big one. If the computer resource is we're using, John and the application resource will be using or ephemeral nature than all the things that surrounded the way we secured the way we connect. Those also have to be equal, equally agile, right, So you can't have, you know, you think of a micro services based application being secured with traditional firewalls, right? Just the amount of, or even virtual the way that the length of time it takes to spend those things up is way too long. So in many ways, this distributed cloud change changes everything in I T. And that that includes all of the services in the the infrastructure that we used to secure and connect. And that's a that is a profound change, and you mentioned the observe ability. You're right. That's another thing that the traditional observe ability tools are based on static maps and things and, you know, traditional up, down and we don't. Things go up and down so quickly now that that that those don't make any sense. So I think we are going to see quite a rise in different types of management tools and the way they look at things to be much more. I suppose you know Angela also So we can measure things that currently aren't measurable. >>So you're talking about the entire stack. Really? Changing is really what you're inferring anyway from your commentary. And that would include the programming model as well, wouldn't it? >>Absolutely. Yeah. You know, the thing that is really interesting about where we have been versus where we're going is we spent a lot of time talking about virtual izing hardware and moving that around. And what does that look like? And that, and creating that is more of a software paradigm. And the thing we're talking about now is what is cloud is an operating model look like? What is the manageability of that? What is the security of that? What? You know, we've talked a lot about containers and moving into a different you know, Dev suck ups and all those different trends that we've been talking about, like now we're doing them. So we've only got into the first crank of that. And I think every technology vendor we talked to now has to address how are they going to do a highly distributed management and security landscape? Like, what are they gonna layer on top of that? Because it's not just about Oh, I've taken Iraq of something server storage, compute and virtualized it. I now have to create a new operating model around it. In a way, we're almost redoing what the OS I stack looks like and what the software and solutions are for that. >>So >>it was really Hold on, hold on, hold on their lengthened. Because that side stack that came up earlier today, Mayor. But we're talking about Yeah, we were riffing on the OSC model, but back in the day and we were comparing the S n a definite the, you know, the proprietary protocol stacks that they were out there and someone >>said Amazon's S N a. Is that recall? E think that's what you said? >>No, no. Someone in the chest. That's a comment like Amazon's proprietary meaning, their scale. And I said, Oh, that means there s n a But if you think about it, that's kind of almost that can hang. Hang together. If the kubernetes is like a new connective tissue, is that the TCP pipe moment? Because I think Os I kind of was standardizing at the lower end of the stack Ethernet token ring. You know, the data link layer physical layer and that when you got to the TCP layer and really magic happened right to me, that's when Cisco's happened and everything started happening then and then. It kind of stopped because the application is kinda maintain their peace there. A little history there, but like that's kind of happening now. If you think about it and then you put me a factor in the edge, it just kind of really explodes it. So who's gonna write that software? E >>think you know, Dave, your your dad doesn't change what you build ups. It's already changed in the consumer world, you look atyou, no uber and Waze and things like that. Those absolute already highly decomposed applications that make a P I calls and DNS calls from dozens of different resource is already right. We just haven't really brought that into the enterprise space. There's a number, you know, what kind of you know knew were born in the cloud companies that have that have done that. But they're they're very few and far between today. And John, your point about the connectivity. We do need to think about connectivity at the network layer. Still, obviously, But now we're creating that standardization that standardized connectivity all the way a player seven. So you look at a lot of the, you know, one of the big things that was a PDP. I calls right, you know, from different cloud services. And so we do need to standardize in every layer and then stitch that together. So that does make It does make things a lot more complicated. Now I'm not saying Don't do it because you can do a whole lot more with absolute than you could ever do before. It's just that we kind of cranked up the level of complexity here, and flowered isn't just a single thing anymore, right? That's that. That's what we're talking about here It's a collection of edges and private clouds and public clouds. They all have to be stitched together at every layer in orderto work. >>So I was I was talking a few CEOs earlier in the day. We had we had them on, I was asking them. Okay, So how do you How do you approach this complexity? Do you build that abstraction layer? Do you rely on someone like Microsoft to build that abstraction layer? Doesn't appear that Amazon's gonna do it, you know? Where does that come from? Or is it or is it dozens of abstraction layers? And one of the CEO said, Look, it's on us. We have to figure out, you know, we get this a p I economy, but But you guys were talking about a mawr complicated environment, uh, moving so so fast. Eso if if my enterprise looks like my my iPhone APs. Yes, maybe it's simpler on an individual at basis, but its app creep and my application portfolio grows. Maybe they talk to each other a little bit better. But that level of complexity is something that that that users are gonna have to deal >>with what you thought. So I think quite what Zs was trying to get it and correct me if I'm wrong. Zia's right. We've got to the part where we've broken down what was a traditional application, right? And now we've gotten into a P. I calls, and we have to think about different things. Like we have to think about how we secure those a p I s right. That becomes a new criteria that we're looking at. How do we manage them? How do they have a life cycle? So what was the life cycle of, say, an application is now the life cycle of components and so that's a That's a pretty complex thing. So it's not so much that you're getting app creep, but you're definitely rethinking how you want to design your applications and services and some of those you're gonna do yourself and a lot of them are going to say it's too complicated. I'm just going to go to some kind of SAS cloud offering for that and let it go. But I think that many of the larger companies I speak to are looking for a larger company to help them build some kind of framework to migrate from what they've used with them to what they need tohave going forward. >>Yeah, I think. Where the complexities. John, You asked who who creates the normalization layer? You know, obviously, if you look to the cloud providers A W s does a great job of stitching together all things AWS and Microsoft does a great job of stitching together all things Microsoft right in saying with Google. >>But >>then they don't. But if if I want to do some Microsoft to Amazon or Google Toe Microsoft, you know, connectivity, they don't help so much of that. And that's where the third party vendors that you know aviatrix on the network side will tear of the security side of companies like that. Even Cisco's been doing a lot of work with those companies, and so what we what we don't really have And we probably won't for a while if somebody is gonna stitch everything together at every >>you >>know, at every layer. So Andi and I do think we do get after it. Maribel, I think if you look at the world of consumer APS, we moved to a lot more kind of purpose built almost throwaway apps. They serve a purpose or to use them for a while. Then you stop using them. And in the enterprise space, we really haven't kind of converted to them modeling on the mobile side. But I think that's coming. Well, >>I think with micro APS, right, that that was kind of the issue with micro APS. It's like, Oh, I'm not gonna build a full scale out that's gonna take too long. I'm just gonna create this little workflow, and we're gonna have, like, 200 work flows on someone's phone. And I think we did that. And not everybody did it, though, to your point. So I do think that some people that are a little late to the game might end up in in that app creep. But, hey, listen, this is a fabulous opportunity that just, you know, throw a lot of stuff out and do it differently. What What? I think what I hear people struggling with ah lot is be to get it to work. It typically is something that is more vertically integrated. So are you buying all into a Microsoft all you're buying all into an Amazon and people are starting to get a little fear about doing the full scale buy into any specific platform yet. In absence of that, they can't get anything to work. >>Yeah, So I think again what? What I'm hearing from from practitioners, I'm gonna put a micro serve. And I think I think, uh, Mirabelle, this is what you're implying. I'm gonna put a micro services layer. Oh, my, my. If I can't get rid of them, If I can't get rid of my oracle, you know, workloads. I'm gonna connect them to my modernize them with a layer, and I'm gonna impart build that. I'm gonna, you know, partner to get that done. But that seems to be a a critical path forward. If I don't take that step, gonna be stuck in the path in the past and not be able to move forward. >>Yeah, absolutely. I mean, you do have to bridge to the past. You you aren't gonna throw everything out right away. That's just you can't. You can't drive the bus and take the wheels off that the same time. Maybe one wheel, but not all four of them at the same time. So I think that this this concept of what are the technologies and services that you use to make sure you can keep operational, but that you're not just putting on Lee new workloads into the cloud or new workloads as decomposed APS that you're really starting to think about. What do I want to keep in whatever I want to get rid of many of the companies you speak Thio. They have thousands of applications. So are they going to do this for thousands of applications? Are they gonna take this as an opportunity to streamline? Yeah, >>well, a lot of legacy never goes away, right? And I was how companies make this transition is gonna be interesting because there's no there's no really the fact away I was I was talking to this one company. This is New York Bank, and they've broken their I t division down into modern I t and legacy I t. And so modern. Everything is cloud first. And so imagine me, the CEO of Legacy i e 02 miracles. But what they're doing, if they're driving the old bus >>and >>then they're building a new bus and parallel and eventually, you know, slowly they take seats out of the old bus and they take, you know, the seat and and they eventually start stripping away things. That old bus, >>But >>that old bus is going to keep running for a long time. And so stitching the those different worlds together is where a lot of especially big organizations that really can't commit to everything in the cloud are gonna struggle. But it is a It is a whole new world. And like I said, I think it creates so much opportunity for people. You know, e >>whole bus thing reminds me that movie speed when they drive around 55 miles an hour, just put it out to the airport and just blew up E >>got But you know, we all we all say that things were going to go away. But to Zia's point, you know, nothing goes away. We're still in 2021 talking about mainframes just as an aside, right? So I think we're going to continue tohave some legacy in the network. But the But the issue is ah, lot will change around that, and they're gonna be some people. They're gonna make a lot of money selling little startups that Just do one specific piece of that. You know, we just automation of X. Oh, >>yeah, that's a great vertical thing. This is the This is the distributed network argument, right? If you have a note in the network and you could put a containerized environment around it with some micro services um, connective tissue glue layer, if you will software abstract away some integration points, it's a note on the network. So if in mainframe or whatever, it's just I mean makes the argument right, it's not core. You're not building a platform around the mainframe, but if it's punching out, I bank jobs from IBM kicks or something, you know, whatever, Right? So >>And if those were those workloads probably aren't gonna move anywhere, right, they're not. Is there a point in putting those in the cloud? You could say Just leave them where they are. Put a connection to the past Bridge. >>Remember that bank when you talk about bank guy we interviewed in the off the record after the Cube interviews like, Yeah, I'm still running the mainframe, so I never get rid of. I love it. Run our kicks job. I would never think about moving that thing. >>There was a large, large non US bank who said I buy. I buy the next IBM mainframe sight unseen. Andi, he's got no choice. They just write the check. >>But milliseconds is like millions of dollars of millisecond for him on his back, >>so those aren't going anywhere. But then, but then, but they're not growing right. It's just static. >>No, no, that markets not growing its's, in fact. But you could make a lot of money and monetizing the legacy, right? So there are vendors that will do that. But I do think if you look at the well, we've already seen a pretty big transition here. If you look at the growth in a company like twilio, right, that it obviates the need for a company to rack and stack your own phone system to be able to do, um, you know, calling from mobile lapse or even messaging. Now you just do a P. I calls. Um, you know, it allows in a lot of ways that this new world we live in democratizes development, and so any you know, two people in the garage can start up a company and have a service up and running another time at all, and that creates competitiveness. You know much more competitiveness than we've ever had before, which is good for the entire industry. And, you know, because that keeps the bigger companies on their toes and they're always looking over their shoulder. You know what, the banks you're looking at? The venues and companies like that Brian figure out a way to monetize. So I think what we're, you know well, that old stuff never going away. The new stuff is where the competitive screen competitiveness screen. >>It's interesting. Um IDs Avery. Earlier today, I was talking about no code in loco development, how it's different from the old four g l days where we didn't actually expand the base of developers. Now we are to your point is really is democratizing and, >>well, everybody's a developer. It could be a developer, right? A lot of these tools were written in a way that line of business people create their own APs to point and click interface is, and so the barrier. It reminds me of when, when I started my career, I was a I. I used to code and HTML build websites and then went to five years. People using drag and drop interface is right, so that that kind of job went away because it became so easy to dio. >>Yeah, >>sorry. A >>data e was going to say, I think we're getting to the part. We're just starting to talk about data, right? So, you know, when you think of twilio, that's like a service. It's connecting you to specific data. When you think of Snowflake, you know, there's been all these kinds of companies that have crept up into the landscape to feel like a very specific void. And so now the Now the question is, if it's really all about the data, they're going to be new companies that get built that are just focusing on different aspects of how that data secured, how that data is transferred, how that data. You know what happens to that data, because and and does that shift the balance of power about it being out of like, Oh, I've created these data centers with large recommend stack ums that are virtualized thio. A whole other set of you know this is a big software play. It's all about software. >>Well, we just heard from Jim Octagon e You guys talking earlier about just distributed system. She basically laid down that look. Our data architectures air flawed there monolithic. And data by its very nature is distributed so that she's putting forth the whole new paradigm around distributed decentralized data models, >>which Howie shoe is just talking about. Who's gonna build the visual studio for data, right? So programmatic. Kind of thinking around data >>I didn't >>gathering. We didn't touch on because >>I do think there's >>an opportunity for that for, you know, data governance and data ownership and data transport. But it's also the analytics of it. Most companies don't have the in house, um, you know, data scientists to build on a I algorithms. Right. So you're gonna start seeing, you know, cos pop up to do very specific types of data. I don't know if you saw this morning, um, you know, uniforms bought this company that does, you know, video emotion detection so they could tell on the video whether somebody's paying attention, Not right. And so that's something that it would be eso hard for a company to build that in house. But I think what you're going to see is a rise in these, you know, these types of companies that help with specific types of analytics. And then you drop you pull those in his resource is into your application. And so it's not only the storage and the governance of the data, but also the analytics and the analytics. Frankly, there were a lot of the, uh, differentiation for companies is gonna come from. I know Maribel has written a lot on a I, as have I, and I think that's one of the more exciting areas to look at this year. >>I actually want to rip off your point because I think it's really important because where we left off in 2020 was yes, there was hybrid cloud, but we just started to see the era of the vertical eyes cloud the cloud for something you know, the cloud for finance, the cloud for health care, the telco and edge cloud, right? So when you start doing that, it becomes much more about what is the specialized stream that we're looking at. So what's a specialized analytic stream? What's a specialized security stack stream? Right? So until now, like everything was just trying to get to what I would call horizontal parody where you took the things you had before you replicated them in a new world with, like, some different software, but it was still kind of the same. And now we're saying, OK, let's try Thio. Let's try to move out of everything, just being a generic sort of cloud set of services and being more total cloud services. >>That is the evolution of everything technology, the first movement. Everything doing technology is we try and make the old thing the new thing look like the old thing, right? First PCs was a mainframe emulator. We took our virtual servers and we made them look like physical service, then eventually figure out, Oh, there's a whole bunch of other stuff that I could do then I couldn't do before. And that's the part we're trying to hop into now. Right? Is like, Oh, now that I've gone cloud native, what can I do that I couldn't do before? Right? So we're just we're sort of hitting that inflection point. That's when you're really going to see the growth takeoff. But for whatever reason, and i t. All we ever do is we're trying to replicate the old until we figure out the old didn't really work, and we should do something new. >>Well, let me throw something old and controversial. Controversial old but old old trope out there. Consumerism ation of I t. I mean, if you think about what year was first year you heard that term, was it 15 years ago? 20 years ago. When did that first >>podcast? Yeah, so that was a long time ago >>way. So if you think about it like, it kind of is happening. And what does it mean, right? Come. What does What does that actually mean in today's world Doesn't exist. >>Well, you heard you heard. Like Fred Luddy, whose founder of service now saying that was his dream to bring consumer like experiences to the enterprise will. Well, it didn't really happen. I mean, service not pretty. Pretty complicated compared toa what? We know what we do here, but so it's It's evolving. >>Yeah, I think there's also the enterprise ation of consumer technology that John the companies, you know, you look a zoom. They came to market with a highly consumer facing product, realized it didn't have the security tools, you know, to really be corporate great. And then they had to go invest a bunch of money in that. So, you know, I think that waken swing the pendulum all the way over to the consumer side, but that that kind of failed us, right? So now we're trying to bring it back to center a little bit where we blend the two together. >>Cloud kind of brings that I never looked at that way. That's interesting and surprising of consumer. Yeah, that's >>alright, guys. Hey, we gotta wrap Zs, Maribel. Always a pleasure having you guys on great great insights from the half hour flies by. Thanks so much. We appreciate it. >>Thank >>you guys. >>Alright, keep it right there. Mortgage rate content coming from the Cuban Cloud Day Volonte with John Ferrier and a whole lineup still to come Keep right there.

>>on the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Hello and welcome back to the Cubes Live coverage Cube live here in Palo Alto, California, with the Virtual Cube this year because we can't be there in person. I'm your host, John Fairy year. We're kicking off Day two of the three weeks of reinvent a lot of great leadership sessions to review, obviously still buzzing from the Andy Jassy three. Our keynote, which had so many storylines, is really hard to impact. We're gonna dig that into into into that today with Jerry Chan, who has been a Cube alumni since the beginning of our AWS coverage. Going back to 2013, Jerry was wandering the hallways as a um, in between. You were in between vm ware and V C. And then we saw you there. You've been on the Cube every year at reinvent with us. So special commentary from you. Thanks for coming on. >>Hey, John, Thanks for having me and a belated happy birthday as well. If everyone out there John's birthday was yesterday. So and hardest. Howard's working man in technology he spent his entire birthday doing live coverage of Amazon re events. Happy birthday, buddy. >>Well, I love my work. I love doing this. And reinvent is the biggest event of the year because it really is. It's become a bellwether and eso super excited to have you on. We've had great conversations by looking back at our conversations over the Thanksgiving weekend. Jerry, the stuff we were talking about it was very proposed that Jassy is leaning in with this whole messaging around change and horizontal scalability. He didn't really say that, but he was saying you could disrupt in these industries and still use machine learning. This was some of the early conversations we were having on the Cube. Now fast forward, more mainstream than ever before. So big, big part of the theme there. >>Yeah, it z you Amazon reinvent Amazon evolution to your point, right, because it's both reinventing what countries are using with the cloud. But also what Amazon's done is is they're evolving year after year with their services. So they start a simple infrastructure, you know, s three and e c. Two. And now they're building basically a lot of what Andy said you actually deconstructed crm? Ah, lot of stuff they're doing around the call centers, almost going after Salesforce with kind of a deconstructed CRM services, which is super interesting. But the day you know, Amazon announces all those technologies, not to mention the AI stuff, the seminar stuff you have slack and inquired by Salesforce for $27.7 billion. So ah, lot of stuff going on in the cloud world these days, and it's funny part of it, >>you know, it really is interesting. You look up the slack acquisition by, um, by Salesforce. It's interesting, you know, That kind of takes slack out of the play here. I mean, they were doing really well again. Message board service turns into, um, or collaboration software. They hit the mainstream. They have great revenue. Is that going to really change the landscape of the industry for Salesforce? They've got to acquire it. It opens the door up from, or innovation. And it's funny you mention the contact Center because I was pressing Jassy on my exclusive one on one with him. Like they said, Andy, my my daughter and my sons, they don't use the phone. They're not gonna call. What's this? Is it a call center deal? And he goes, No, it's the It's about the contact. So think about that notion of the contact. It's not about the call center. It's the point of contact. Okay, Linked in is with Microsoft. You got slack and Salesforce Contact driven collaboration. Interesting kind of play for Microsoft to use voice and their data. What's your take on that? >>I think it's, um you know, I have this framework. As you know, I talked my friend systems of engagement over systems intelligence and systems record. Right? And so you could argue voice email slack because we're all different systems of engagement, and they sit on top of system of record like CRM customer support ticketing HR. Something like that. Now what sells first did by buying slack is they now own a system engagement, right? Not on Lee is slack. A system engagement for CRM, but also system engagement for E. R. P Service. Now is how you interact with a bunch of applications. And so if you think about sales for strategy in the space, compete against Marcus Soft or serves now or other large AARP's now they own slack of system engagement, that super powerful way to actually compete against rival SAS companies. Because if you own the layer engagement layer, you can now just intermediate what's in the background. Likewise, the context center its own voice. Email, chat messaging, right? You can just inter mediate this stuff in the back, and so they're trying to own the system engagement. And then, likewise, Facebook just bought that company customer a week ago for a billion dollars, which also Omni Channel support because it is chat messaging voice. It's again the system engagement between End User, which could be a customer or could be employees. >>You know, this really gonna make Cit's enterprise has been so much fun over the past 10 years, I gotta say, in the past five, you know, it's been even more fun, has become or the new fun area, you know, And the impact to enterprise has been interesting because and we're talking about just engaging system of record. This is now the new challenge for the enterprise. So I wanna get your thoughts, Jerry, because how you see the Sea, X O's and CSOs and the architects out there trying to reinvent the enterprise. Jassy saying Look and find the truth. Be on the right side of history here. Certainly he's got himself service interest there, but there is a true band eight with Cove it and with digital acceleration for the enterprise to change. Um, given all these new opportunities Thio, revolutionize or disrupt or radically improve, what's the C. C X's do? What's your take on? How do you see that? >>It's increasingly messy for the CXS, and I don't I don't envy them, right? Because back in the day they kind of controlled all the I t spend and kind of they had a standard of what technologies they use in the company. And then along came Amazon in cloud all of sudden, like your developers and Dio Hey, let me swipe my credit card and I'm gonna access to a bunch of a P I s around computing stories. Likewise. Now they could swipe the credit card and you strike for billing, right? There's a whole bunch of services now, so it becomes incumbent upon CSOs. They need Thio new set of management tools, right? So not only just like, um, security tools they need, they need also observe ability, tools, understanding what services are being used by the customers, when and how. And I would say the following John like CSOs is both a challenge for them. But I think if I was a C X, so I'll be pretty excited because now I have a bunch of other weapons and other bunch of services I could offer. My end users, my developers, my employees, my customers and, you know it's exciting for them is not only could they do different things, but they also changed how their business being done. And so I think both interact with their end users. Be a chat like slack or be a phone like a contact center or instagram for your for your for your kids. It's actually a new challenge if I were sick. So it's it's time to build again, you know, I think Cove it has said it is time to build again. You can build >>to kind of take that phrase from the movie Shawshank Redemption. Get busy building or get busy dying. Kinda rephrase it there. And that's kind of the theme I'm seeing here because covert kind of forced people saying, Look, this things like work at home. Who would have thought 100% people would be working at home? Who would have thought that now the workloads gonna change differently? So it's an opportunity to deconstruct or distant intermediate these services. And I think, you know, in all the trends that I've seen over my career, it's been those inflection points where breaking the monolith or breaking the proprietary piece of it has always been an opportunity for for entrepreneur. So you know, and and for companies, whether you're CEO or startup by decomposing and you can come in and create value E I think to me, snowflake going public on the back of Amazon. Basically, this is interesting. I mean, so you don't have to be. You could kill one feature and nail it and go big. >>I think we talked to the past like it's Amazon or Google or Microsoft Gonna win. Everything is winner take all winner take most, and you could argue that it's hard to find oxygen as a start up in a broad platform play. But we think Snowflake and other companies have done and comes like mongo DB, for example, elastic have shown that if you can pick a service or a problem space and either developed like I p. That's super deep or own developer audience. You can actually fight the big guys. The Big Three cloud vendors be Amazon, Google or or market soft in different markets. And I think if you're a startup founder, you should not be afraid of competing with the big cloud vendors because there there are success patterns and how you can win and you know and create a lot of value. So I have found Investor. I'm super excited by that because, you know, I don't think you're gonna find a company takedown Amazon completely because they're just the scale and the network effects is too large. But you can create a lot of value and build Valuable comes like snowflake in and around the Amazon. Google Microsoft Ecosystem. >>Yeah, I want to get your thoughts. You have one portfolio we've covered rock rock set, which does a lot of sequel. Um, one of your investments. Interesting part of the Kino yesterday was Andy Jassy kind of going after Microsoft saying Windows sequel server um, they're targeting that with this new, uh, tool, but, you know, sucks in the database of it is called the Babel Fish for Aurora for post Chris sequel. Um, well, how was your take on that? I mean, obviously Microsoft big. Their enterprise sales tactics are looking like more like Oracle, which he was kind of hinting at and commenting on. But sequel is Lingua Franca for data >>correct. I think we went to, like, kind of a no sequel phase, which was kind of a trendy thing for a while and that no sequel still around, not only sequel like mongo DB Document TV. Kind of that interface still holds true, but your point. The world speaks sequel. All your applications be sequel, right? So if you want backwards, compatibility to your applications speaks equal. If you want your tire installed base of employees that no sequel, we gotta speak sequel. So, Rock said, when the first public conversations about what they're building was on on the key with you and Me and vent hat, the founder. And what Rock said is doing their building real time. Snowflake Thio, Lack of better term. It's a real time sequel database in the cloud that's super elastic, just like Snowflake is. But unlike snowflake, which is a data warehouse mostly for dashboards and analytics. Rock set is like millisecond queries for real time applications, and so think of them is the evolution of where cloud databases air going is not only elastic like snowflake in the cloud like Snowflake. We're talking 10 15 millisecond queries versus one or two second queries, and I think what any Jassy did and Amazon with bowel officials say, Hey, Sequels, Legal frank of the cloud. There's a large installed base of sequel server developers out there and applications, and we're gonna use Babel fish to kind of move those applications from on premise the cloud or from old workload to the new workloads. And, I think, the name of the game. For for cloud vendors across the board, big and small startups thio Google markets, often Amazon is how do you reduce friction like, How do you reduce friction to try a new service to get your data in the cloud to move your data from one place to the next? And so you know, Amazon is trying to reduce friction by using Babel fish, and I think it is a great move by them. >>Yeah, by the way. Not only is it for Aurora Post Chris equal, they're also open sourcing it. So that's gonna be something that is gonna be interesting to play out. Because once they open source it essentially, that's an escape valve for locking. I mean, if you're a Microsoft customer, I mean, it ultimately is. Could be that Gateway drug. It's like it is ultimately like, Hey, if you don't like the licensing, come here. Now there's gonna be some questions on the translations. Um, Vince, um, scuttlebutt about that. But we'll see it's open source. We'll see what goes on. Um great stuff on on rocks that great. Great. Start up next. Next, uh, talk track I wanna get with you is You know, over the years, you know, we've talked about your history. We're gonna vm Where, uh, now being a venture capitalist. Successful, wanted Greylock. You've seen the waves, and I would call it the two ways pre cloud Early days of cloud. And now, with co vid, we're kind of in the, you know, not just born in the cloud Total cloud scale cloud operations. This is kind of what jazz he was going after. E think I tweeted Cloud is eating the world and on premise and the edges. What it's hungry for. It kind of goof on mark injuries since quote a software eating the world. This is where it's going. So it's a whole another chapter coming. You saw the pre cloud you saw Cloud. Now we've got basically global I t everything else >>It's cloud only I would say, You know, we saw pre cloud right the VM ware days and before that he called like, you know, data centers. I would say Amazon lawns of what, 6 4007, the Web services. So the past 14 15 years have been what I've been calling cloud transition, right? And so you had cos technologies that were either doing on migration from on premise and cloud or hybrid on premise off premise. And now you're seeing a generation of technologies and companies. Their cloud only John to your point. And so you could argue that this 15 year transitions were like, you know, Thio use a bad metaphor like amphibians. You're half in the water, half on land, you know, And like, you know, you're not You're not purely cloud. You're not purely on premise, but you can do both ways, and that's great. That's great, because that's a that's a dominant architecture today. But come just like rock set and snowflake, your cloud only right? They're born in the cloud, they're built on the cloud And now we're seeing a generation Startups and technology companies that are cloud only. And so, you know, unlike you have this transitionary evolution of like amphibians, land and sea. Now we have ah, no mammals, whatever that are Onley in the cloud Onley on land. And because of that, you can take advantage of a whole different set of constraints that are their cloud. Only that could build different services that you can't have going backwards. And so I think for 2021 forward, we're going to see a bunch of companies or cloud only, and they're gonna look very, very different than the previous set of companies the past 15 years. And as an investor, as you covering as analysts, is gonna be super interesting to see the difference. And if anything, the cloud only companies will accelerate the move of I t spending the move of mawr developers to the cloud because the cloud only technologies are gonna be so much more compelling than than the amphibians, if you will. >>Yeah, insisting to see your point. And you saw the news announcement had a ton of news, a ton of stage making right calls, kind of the democratization layer. We'll look at some of the insights that Amazon's getting just as the monster that they are in terms of size. The scope of what? Their observation spaces. They're seeing all these workloads. They have the Dev Ops guru. They launched that Dev Ops Guru thing I found interesting. They got data acquisition, right? So when you think about these new the new data paradigm with cloud on Lee, it opens up new things. Um, new patterns. Um, S o. I think I think to me. I think that's to me. I see where this notion of agility moves to a whole nother level, where it's it's not just moving fast, it's new capabilities. So how do you How do you see that happening? Because this is where I think the new generation is gonna come in and be like servers. Lambs. I like you guys actually provisioned E c. Two instances before I was servers on data centers. Now you got ec2. What? Lambda. So you're starting to see smaller compute? Um, new learnings, All these historical data insights feeding into the development process and to the application. >>I think it's interesting. So I think if you really want to take the next evolution, how do you make the cloud programmable for everybody? Right. And I think you mentioned stage maker machine learning data scientists, the sage maker user. The data scientists, for example, does not on provisioned containers and, you know, kodama files and understand communities, right? Like just like the developed today. Don't wanna rack servers like Oh, my God, Jerry, you had Iraq servers and data center and install VM ware. The generation beyond us doesn't want to think about the underlying infrastructure. You wanna think about it? How do you just program my app and program? The cloud writ large. And so I think where you can see going forward is two things. One people who call themselves developers. That definition has expanded the past 10, 15 years. It's on Lee growing, so everyone is gonna be developed right now from your white collar knowledge worker to your hard core infrastructure developer. But the populist developers expanding especially around machine learning and kind of the sage maker audience, for sure. And then what's gonna happen is, ah, law. This audience doesn't want to care about the stuff you just mentioned, John in terms of the online plumbing. So what Amazon Google on Azure will do is make that stuff easy, right? Or a starved could make it easy. And I think that the move towards land and services that moved specifically that don't think about the underlying plumbing. We're gonna make it easy for you. Just program your app and then either a startup, well, abstract away, all the all the underlying, um, infrastructure bits or the big three cloud vendors to say, you know, all this stuff would do in a serverless fashion. So I think serverless as, ah paradigm and have, quite frankly, a battlefront for the Big Three clouds and for startups is probably one in the front lines of the next generation. Whoever owns this kind of program will cloud model programming the Internet program. The cloud will be maybe the next platform the next 10 or 15 years. I still have two up for grabs. >>Yeah, I think that is so insightful. I think that's worth calling out. I think that's gonna be a multi year, um, effort. I mean, look at just how containers now, with ks anywhere and you've got the container Service of control plane built in, you got, you know, real time analytics coming in from rock set. And Amazon. You have pinned Pandora Panorama appliance that does machine learning and computer vision with sensors. I mean, this is just a whole new level of purpose built stuff software powered software operated. So you have this notion of Dev ops going to hand in the glove software and operations? Kind of. How do you operate this stuff? So I think the whole new next question was Okay, this is all great. But Amazon's always had this problem. It's just so hard. Like there's so much good stuff. Like, who do you hired operate it? It is not yet programmable. This has been a big problem for them. Your thoughts on that, >>um e think that the data illusion around Dev ops etcetera is the solution. So also that you're gonna have information from Amazon from startups. They're gonna automate a bunch of the operations. And so, you know, I'm involved to come to Kronos Fear that we talked about the past team kind of uber the Bilson called m three. That's basically next generation data dog. Next generation of visibility platform. They're gonna collect all the data from the applications. And once they have their your data, they're gonna know how to operate and automate scaling up, scaling down and the basic remediation for you. So you're going to see a bunch of tools, take the information from running your application infrastructure and automate exactly how to scale and manager your app. And so AI and machine learning where large John is gonna be, say, make a lot of plumbing go away or maybe not completely, but lets you scale better. So you, as a single system admin are used. A single SRE site reliability engineer can scale and manage a bigger application, and it's all gonna be around automation and and to your point, you said earlier, if you have the data, that's a powerful situations. Once have the data can build models on it and can start building solutions on the data. And so I think What happens is when Bill this program of cloud for for your, you know, broad development population automating all this stuff becomes important. So that's why I say service or this, You know, automation of infrastructure is the next battleground for the cloud because whoever does that for you is gonna be your virtualized back and virtualized data center virtualized SRE. And if whoever owns that, it's gonna be a very, very strategic position. >>Yeah, it's great stuff. This is back to the theme of this notion of virtualization is now gone beyond server virtualization. It's, you know, media virtualization with the Cube. My big joke here with the Q virtual. But it's to your point. It's everything can now be replicated in software and scale the cloud scale. So it's super big opportunity for entrepreneurs and companies. Thio, pivot and differentiate. Uh, the question I have for you next is on that thread Huge edge discussion going on, right. So, you know, I think I said it two years ago or three years ago. The data center is just a edges just a big fat edge. Jassy kind of said that in his keynote Hey, looks at that is just a Nedum point with his from his standpoint. But you have data center. You have re alleges you've got five G with wavelength. This local zone concept, which is, you know, Amazon in these metro areas reminds me the old wireless point of presence kind of vibe. And then you've got just purpose built devices like cameras and factory. So huge industrial innovation, robotics, meet software. I mean, whole huge edge development exploding, Which what's your view of this? And how do you look at that from? Is an investor in industry, >>I think edges both the opportunity for start ups and companies as well as a threat to Amazon, right to the reason why they have outposts and all the stuff the edges if you think about, you know, decentralizing your application and moving into the eggs from my wearable to my home to my car to my my city block edges access Super interesting. And so a couple things. One companies like Cloudflare Fastly company I'm involved with called Kato Networks that does. SAS is secure access service edge write their names and the edges In the category definition sassy is about How do you like get compute to the edge securely for your developers, for your customers, for your workers, for end users and what you know comes like Cloudflare and Kate have done is they built out a network of pops across the world, their their own infrastructure So they're not dependent upon. You know, the big cloud providers, the telco providers, you know, they're partnering with Big Cloud, their parting with the telcos. But they have their own kind of system, our own kind of platform to get to the edge. And so companies like Kato Networks in Cloud Player that have, ah, presence on the edge and their own infrastructure more or less, I think, are gonna be in a strategic position. And so Kate was seen benefits in the past year of Of of Cove it and locked down because more remote access more developers, Um, I think edge is gonna be a super great area development going forward. I think if you're Amazon, you're pushing to the edge aggressively without post. I think you're a developer startup. You know, creating your own infrastructure and riding this edge wave could be a great way to build a moat against a big cloud guy. So I'm super excited. You think edge in this whole idea of your own infrastructure. Like what Kato has done, it is gonna be super useful going forward. And you're going to see more and more companies. Um, spend the money to try to copy kind of, ah, Cloudflare Kato presence around the world. Because once you own your own kind of, um, infrastructure instead of pops and you're less depend upon them a cloud provider, you're you're in a good position because there's the Amazon outage last week and I think like twilio and a bunch of services went down for for a few hours. If you own your own set of pops, your independent that it is actually really, really secure >>if you and if they go down to the it's on you. But that was the kinesis outage that they had, uh, they before Thanksgiving. Um, yeah, that that's a problem. So on this on. So I guess the question for you on that is that Is it better to partner with Amazon or try to get a position on the edge? Have them either by you or computer, create value or coexist? How do you see that that strategy move. Do you coexist? Do you play with them? >>E think you have to co exist? I think that the partner coexist, right? I think like all things you compete with Amazon. Amazon is so broad that will be part of Amazon and you're gonna compete with and that's that's fair game, you know, like so Snowflake competes against red shift, but they also part of Amazon's. They're running Amazon. So I think if you're a startup trying to find the edge, you have to coexist in Amazon because they're so big. Big cloud, right, The Big three cloud Amazon, Google, Azure. They're not going anywhere. So if you're a startup founder, you definitely coexist. Leverage the good things of cloud. But then you gotta invest in your own edge. Both both figure early what? Your edge and literally the edge. Right. And I think you know you complement your edge presence be it the home, the car, the city block, the zip code with, you know, using Amazon strategically because Amazon is gonna help you get two different countries, different regions. You know you can't build a company without touching Amazon in some form of fashion these days. But if you're a star found or doing strategically, how use Amazon and picking how you differentiate is gonna be key. And if the differentiation might be small, John. But it could be super valuable, right? So maybe only 10 or 15%. But that could be ah Holton of value that you're building on top of it. >>Yeah, and there's a little bit of growth hack to with Amazon if you you know how it works. If you compete directly against the core building blocks like a C two has three, you're gonna get killed, right? They're gonna kill you if the the white space is interest. In the old days in Microsoft, you had a white space. They give it to you or they would roll you over and level you out. Amazon. If you're a customer and you're in a white space and do better than them, they're cool with that. They're like, basically like, Hey, if you could innovate on behalf of the customer, they let you do that as long as you have a big bill. Yeah. Snowflakes paying a lot of money to Amazon. Sure, but they also are doing a good job. So again, Amazon has been very clear on that. If you do a better job than us for, the customer will do it. But if they want Amazon Red Shift, they want Amazon Onley. They can choose that eso kind of the playbook. >>I think it is absolutely right, John is it sets from any jassy and that the Amazon culture of the customer comes first, right? And so whatever is best for the customer that's like their their mission statement. So whatever they do, they do for the customer. And if you build value for the customer and you're on top of Amazon, they'll be happy. You might compete with some Amazon services, which, no, the GM of that business may not be happy, but overall. Net Net. Amazon's getting a share of those dollars that you're that you're charging the customer getting a share of the value you're creating. They're happy, right? Because you know what? The line rising tide floats all the boats. So the Mork cloud usage is gonna only benefit the Big Three cloud providers Amazon, particularly because they're the biggest of the three. But more and more dollars go the cloud. If you're helping move more. Absolute cloud helping build more solutions in the cloud. Amazon is gonna be happy because they know that regardless of what you're doing, you will get a fraction of those dollars. Now, the key for a startup founder and what I'm looking for is how do we get mawr than you know? A sliver of the dollars. How to get a bigger slice of the pie, if you will. So I think edge and surveillance or two areas I'm thinking about because I think there are two areas where you can actually invest, own some I p owned some surface area and capture more of the value, um, to use a startup founder and, you know, are built last t to Amazon. >>Yeah. Great. Great thesis. Jerry has always been great. You've been with the Cube since the beginning on our first reinvented 2013. Um, and so we're now on our eighth year. Great to see your success. Great investment. You make your world class investor to great firm Greylock. Um great to have you on from your perspective. Final take on this year. What's your view of Jackie's keynote? Just in general, What's the vibe. What's the quick, um, soundbite >>from you? First, I'm so impressed and you can do you feel like a three Archy? No more or less by himself. Right then, that is, that is, um, that's a one man show, and I'm All of that is I don't think I could pull that off. Number one. Number two It's, um, the ability to for for Amazon to execute at so many different levels of stack from semiconductors. Right there, there there ai chips to high level services around healthcare solutions and legit solutions. It's amazing. So I would say both. I'm impressed by Amazon's ability. Thio go so broad up and down the stack. But also, I think the theme from From From Andy Jassy is like It's just acceleration. It's, you know now that we will have things unique to the cloud, and that could be just a I chips unique to the cloud or the services that are cloud only you're going to see a tipping point. We saw acceleration in the past 15 years, John. He called like this cloud transition. But you know, I think you know, we're talking about 2021 beyond you'll see a tipping point where now you can only get certain things in the cloud. Right? And that could be the underlying inference. Instances are training instances, the Amazons giving. So all of a sudden you as a founder or developer, says, Look, I guess so much more in the cloud there's there's no reason for me to do this hybrid thing. You know, Khyber is not gonna go away on Prem is not going away. But for sure. We're going to see, uh, increasing celebration off cloud only services. Um, our edge only services or things. They're only on functions that serve like serverless. That'll be defined the next 10 years of compute. And so that for you and I was gonna be a space and watch >>Jerry Chen always pleasure. Great insight. Great to have you on the Cube again. Great to see you. Thanks for coming on. >>Congrats to you guys in the Cube. Seven years growing. It's amazing to see all the content put on. So you think it isn't? Just Last point is you see the growth of the curve growth curves of the cloud. I'd be curious Johnson, The growth curve of the cube content You know, I would say you guys are also going exponential as well. So super impressed with what you guys have dealt. Congratulations. >>Thank you so much. Cute. Virtual. We've been virtualized. Virtualization is coming here, or Cubans were not in person this year because of the pandemic. But we'll be hybrid soon as events come back. I'm John for a year. Host for AWS reinvent coverage with the Cube. Thanks for watching. Stay tuned for more coverage all day. Next three weeks. Stay with us from around the globe. It's the Cube with digital coverage of aws reinvent 2020 sponsored by Intel >>and AWS. Welcome back here to our coverage here on the Cube of AWS.

>>from around the globe. It's the Cube with coverage of Yukon and Cloud. Native Con North America. 2020. Virtual Brought to You by Red Hat, The Cloud, Native Computing Foundation and Ecosystem Partners Welcome back to the Cube. Virtual coverage of KUB Con Cloud native 2020. It's virtual this year. We're not face to face. Were normally in person where we have great interviews. Everyone's kind of jamming in the hallways, having a good time talking tech, identifying the new projects and knew where So we're not. There were remote. I'm John for your host. We've got two great gas, both Cuba alumni's Chris. And is it chief technology officer of the C and C F Chris, Welcome back. Great to see you. Thanks for coming on. Appreciate it. >>Awesome. Glad to be here. >>And, of course, another Cube alumni who is in studio. But we haven't had him at a Show Jr store meant executive director of the Fin Ops Foundation. And that's the purpose of this session. A interesting data point we're going to dig into how cloud has been enabling Mawr communities, more networks of practitioners who are still working together, and it's also a success point Chris on the C N C F vision, which has been playing out beautifully. So we're looking forward to digging. Jr. Thanks for coming on. Great to see you. >>Yeah, great to be here. Thanks, John. >>So, first of all, I want to get the facts out there. I think this is really important story that people should pay attention to the Finn Ops Foundation. That J. R. That you're running is really an interesting success point because it's it's not the c n c f. Okay. It's a practitioner that builds on cloud. Your experience in community you had is doing specific things that they're I won't say narrow but specific toe a certain fintech things. But it's really about the success of Cloud. Can you explain and and layout for take a minute to explain What is the fin Ops foundation and has it relate to see NCF? >>Yeah, definitely. So you know, if you think about this, the shift that we've had to companies deploying primarily in cloud, whether it be containers a ciencia focuses on or traditional infrastructure. The thing that typically people focus on right is the technology and innovation and speed to market in all those areas. But invariably companies hit this. We'd like to call the spend panic moment where they realize they're They're initially spending much more than they expected. But more importantly, they don't really have the processes in place or the people or the tools to do things like fully, you know, understand where their costs are going to look at how to optimize those to operate that in their organizations. And so the foundation pinups foundation eyes really focused on, uh, the people in practitioners who are in organizations doing cloud financial management, which is, you know, being those who drive this accountability of this variable spin model that's existed. So we were partnering very closely with, uh, see NCF. And we're now actually part of the Linux Foundation as of a few months ago, Uh, and you know, just to kind of put into context how that you kind of Iraq together, whereas, you know, CNC s very focused on open source coordinative projects, you know, For example, Spotify just launched their backstage cloud called Management Tool into CFCF Spotify folks, in our end, are working on the best practices around the cloud financial management that standards to go along with that. So we're there to help, you know, define this sort of cultural transformation, which is a shift to now. Engineers happen to think about costs as they never did before. On finance, people happen to partner with technology teams at the speed of cloud, and, you know executives happen to make trade off decisions and really change the way that they operate the business. With this variable page ago, engineers have all the access to spend the money in Cloud Model. >>Hey, blank check for engineers who doesn't like that rain that in its like shift left for security. And now you've got to deal with the financial Finn ops. It's really important. It's super point, Chris. In all seriousness. Putting kidding aside, this is exactly the kind of thing you see with open sores. You're seeing things like shift left, where you wanna have security baked in. You know what Jr is done in a fabulous job with his community now part of Linux Foundation scaling up, there's important things to nail down that is specific to that domain that are related to cloud. What's your thoughts on this? Because you're seeing it play out. >>Yeah, no, I mean, you know, I talked to a lot of our end user members and companies that have been adopting Cloud Native and I have lots of friends that run, you know, cloud infrastructure at companies. And Justus Jr said, You know, eventually there's been a lot of success and cognitive and want to start using a lot of things. Your bills are a little bit more higher than you expect. You actually have trouble figuring out, you know, kind of who's using what because, you know, let's be honest. A lot of the clouds have built amazing services. But let's say the financial management and cost management accounting tools charge back is not really built in well. And so I kind of noticed this this issue where it's like, great everyone's using all these services. Everything is great, But costs are a little bit confusing, hard to manage and, you know, you know, scientifically, you know, I ran into, you know, Jr and his community out there because my community was having a need of like, you know, there's just not good tools, standards, no practices out there. And, you know, the Finau Foundation was working on these kind of great things. So we started definitely found a way to kind of work together and be under the same umbrella foundation, you know, under the under Linux Foundation. In my personal opinion, I see more and more standards and tools to be created in this space. You know, there's, you know, very few specifications or standards and trying to get cost, you know, data out of different clouds and tools out there, I predict, Ah, lot more work is going to be done. Um, in this space, whether it's done and defendants foundation itself, CNC f, I think will probably be, uh, collaboration amongst communities. Can I truly figure this out? So, uh, engineers have any easier understanding of, you know, if I spent up the service or experiment? How much is this actually going to potentially impact the cost of things and and for a while, You know, uh, engineers just don't think about this. When I was at Twitter, we spot up services all time without really care about cost on, and that's happening a lot of small companies now, which don't necessarily have as a big bucket. So I'm excited about the space. I think you're gonna see a huge amount of focus on cloud financial management drops in the near future. >>Chris, thanks for that great insight. I think you've got a great perspective. You know, in some cases, it's a fast and loose environment. Like Twitter. You mentioned you've got kind of a blank check and the rocket ships going. But, Jr, this brings up to kind of points. This kind of like the whole code side of it. The software piece where people are building code, but also this the human error. I mean, we were playing with clubs, so we have a big media cloud and Amazon and we left there. One of the buckets open on the switches and elemental. We're getting charged. Massive amounts for us cash were like, Wait a minute, not even using this thing. We used it once, and it left it open. It was like the water was flowing through the pipes and charging us. So you know, this human error is throwing the wrong switch. I mean, it was simply one configuration error, in some cases, just more about planning and thinking about prototypes. >>Yeah. I mean, so take what your experience there. Waas and multiply by 1000 development teams in a big organization who all have access to cloud. And then, you know, it's it's and this isn't really about a set of new technologies. It's about a new set of processes and a cultural change, as Chris mentioned, you know, engineers now thinking about cost and this being a whole new efficiency metric for them to manage, right? You know, finance teams now see this world where it's like tomorrow. The cost could go three x the next day they could go down. You've got, you know, things spending up by the second. So there's a whole set of cross functional, and that's the majority of the work that are members do is really around. How do we get these cross functional teams working together? How do we get you know, each team up leveled on what they need, understand with cloud? Because not only is it, you know, highly variable, but it's highly decentralized now, and we're seeing, you know, cloud hit. These sort of material spend levels where you know, the big, big cloud spenders out there spending, you know, high nine figures in some cases you know, in cloud and it's this material for their for their businesses. >>And let's just let's be honest. Here is like Clouds, for the most part, don't really have a huge incentive in offering limits and so on. It's just, you know, like, hey, the more usage that the better And hopefully getting a group of practitioners in real figures. Well, holy put pressure to build better tools and services in this area. I think actually it is happening. I think Jared could correct me if wrong. I think AWS recently announced a feature where I think it's finally like quotas, you know, enabled, you know, you have introducing quotas now for and building limits at some level, which, you know, I think it's 2020 Thank you know, >>just to push back a little bit in support of our friends, you ask Google this company, you know, for a long time doing this work, we were worried that the cloud would be like, What are you doing? Are you trying to get our trying to minimize commitments and you know the dirty secret of this type of work? And I were just talking a bunch of practitioners today is that cloud spend never really goes down. When you do this work, you actually end up spending more because you know you're more comfortable with the efficiency that you're getting, and your CEO is like, let's move more workloads over. But let's accelerate. Let's let's do Maurin Cloud goes out more data centers. And so the cloud providers air actually largely incentivized to say, Yeah, we want people to be officially don't understand this And so it's been a great collaboration with those companies. As you said, you know, aws, Google, that you're certainly really focused in this area and ship more features and more data for you. It's >>really about getting smart. I mean, you know, they no, >>you could >>do it. I mean, remember the old browser days you could switch the default search engine through 10 menus. You could certainly find the way if you really wanted to dig in and make policy a simple abstraction layer feature, which is really a no brainer thing. So I think getting smarter is the right message. I want to get into the synergy Chris, between this this trend, because I think this points to, um kind of what actually happened here if you look at it at least from my perspective and correct me if I'm wrong. But you had jr had a community of practitioners who was sharing information. Sounds like open source. They're talking and sharing, you know? Hey, don't throw that switch. Do This is the best practice. Um, that's what open communities do. But now you're getting into software. You have to embed cost management into everything, just like security I mentioned earlier. So this trend, I think if you kind of connect the dots is gonna happen in other areas on this is really the synergy. Um, I getting that right with CNC >>f eso The way I see it is, and I dream of a future where developers, as they develop software, will be able to have some insight almost immediately off how much potential, you know, cost or impact. They'll have, you know, on maybe a new service or spinning up or potentially earlier in the development cycle saying, Hey, maybe you're not doing this in a way that is efficient. Maybe you something else. Just having that feedback loop. Ah lot. You know, closer to Deb time than you know a couple weeks out. Something crazy happens all of a sudden you notice, You know, based on you know, your phase or financial folks reaching out to you saying, Hey, what's going on here? This is a little bit insane. So I think what we'll see is, as you know, practitioners and you know, Jr spinoffs, foundation community, you know, get together share practices. A lot of them, you know, just as we saw on sense. Yeah, kind of build their own tools, models, abstractions. And, you know, they're starting to share these things. And once you start sharing these things, you end up with a you know, a dozen tools. Eventually, you know, sharing, you know, knowledge sharing, code sharing, you know, specifications. Sharing happens Eventually, things kind of, you know, become de facto tools and standards. And I think we'll see that, you know, transition in the thin ops community over the next 12 to 4 months. You know, very soon in my thing. I think that's kind of where I see things going, >>Jr. This really kind of also puts a riel, you know, spotlight and illustrates the whole developer. First cliche. I mean, it's really not a cliche. It's It's happening. Developers first, when you start getting into the calculations of our oi, which is the number one C level question is Hey, what's the are aware of this problem Project or I won't say cover your ass. But I mean, if someone kind of does a project that it breaks the bank or causes a, you know, financial problem, you know, someone gets pulled out to the back would shed. So, you know, here you're you're balancing both ends of the spectrum, you know, risk management on one side, and you've got return on investment on the other. Is that coming out from the conversation where you guys just in the early stages, I could almost imagine that this is a beautiful tailwind for you? These thes trends, >>Yeah. I mean, if you think about the work that we're doing in our practice you're doing, it's not about saving money. It's about making money because you actually want empower those engineers to be the innovation engines in the organization to deliver faster to ship faster. At the same time, they now can have, you know, tangible financial roo impacts on the business. So it's a new up leveling skill for them. But then it's also, I think, to Christmas point of, you know, people seeing this stuff more quickly. You know what the model looks like when it's really great is that engineers get near real time visibility into the impact of their change is on the business, and they can start to have conversations with the business or with their finance partners about Okay, you know, if you want me to move fast, I could move fast, But it's gonna cost this if you want me to optimize the cost. I could do that or I can optimize performance. And there's actually, you know, deeper are like conversation the candidate up. >>Now I know a lot of people who watch the Cube always share with me privately and Chris, you got great vision on this. We talked many times about it. We're learning a lot, and the developers are on the front lines and, you know, a lot of them don't have MBAs and, you know they're not in the business, but they can learn quick. If you can code, you can learn business. So, you know, I want you to take a minute Jr and share some, um, educational knowledge to developers were out there who have to sit in these meetings and have to say, Hey, I got to justify this project. Buy versus build. I need to learn all that in business school when I had to see s degree and got my MBA, so I kind of blended it together. But could you share what the community is doing and saying, How does that engineer sit in the meeting and defend or justify, or you some of the best practices what's coming out of the foundation? >>Yeah, I mean, and we're looking at first what a core principles that the whole organization used to line around. And then for each persona, like engineers, what they need to know. So I mean, first and foremost, it's It's about collaboration, you know, with their partners andan starting to get to that world where you're thinking about your use of cloud from a business value driver, right? Like, what is the impact of this? The critical part of that? Those early decentralization where you know, now you've got everybody basically taking ownership for their cloud usage. So for engineers, it's yes, we get that information in front of us quickly. But now we have a new efficiency metric. And engineers don't like inefficiency, right? They want to write fishing code. They wanna have efficient outcomes. Um, at the same time, those engineers need to now, you know, have ah, we call it, call it a common lexicon. Or for Hitchhiker's Guide to the Galaxy, folks. Ah, Babel fish that needs to be developed between these teams. So a lot of the conversations with engineers right now is in the foundation is okay. What What financial terms do I need to understand? To have meaningful conversations about Op X and Capex? And what I'm going to make a commitment to a cloud provider like a committed use discount, Google or reserved instance or savings Planet AWS. You know, Is it okay for me to make that? What? How does that impact our, you know, cost of capital. And then and then once I make that, how do I ensure that I could work with those teams to get that allocated and accounted? The right area is not just for charge back purposes, but also so that my teams can see my portion of the estate, right? And they were having the flip side of that conversation with all the finance folks of like, You need to understand how the variable cloud, you know, model works. And you need to understand what these things mean and how they impact the business. And then all that's coming together. And to the point of like, how we're working with C and C f you know, into best practices White papers, you know, training Siri's etcetera, sets of KP eyes and capabilities. Onda. All these problems have been around for years, and I wouldn't say they're solved. But the knowledge is out there were pulling it together. The new level that we're trying to talk with the NCF is okay. In the old world of Cloud, you had 1 to 1 use of a resource. You're running a thing on an instance in the new world, you're running in containers and that, you know, cluster may have lots of pods and name spaces, things inside of it that may be doing lots of different workloads, and you can no longer allocate. I've got this easy to instance and this storage to this thing it's now split up and very ephemeral. And it is a whole new layer of virtualization on top of virtual ization that we didn't have to deal with before. >>And you've got multiple cloud. I'll throw that in there, just make another dimension on it. Chris, tie this together cause this is nice energy to scale up what he's built with the community now, part of the Linux Foundation. This fits nicely into your vision, you know, perfectly. >>Yeah, no, 100% like, you know, so little foundation. You know, as you're well, well aware, is just a federation of open source foundations of groups working together to share knowledge. So it definitely fits in kind of the little foundation mission of, you know, building the largest share technology investment for, you know, humankind. So definitely good there with my kind of C and C f c T o hat, you know, on is, you know, I want to make sure that you know, you know my community and and, you know, the community of cloud native has access and, you know, knowledge about modern. You know, cloud financial management practices out there. If you look at some of the new and upcoming projects in ciencia things like, you know, you know, backstage, which came out of Spotify. They're starting to add functionality that, you know, you know, originally backstage kind of started out as this, you know, everyone builds their own service catalog to go catalog, and you know who owns what and, you know and all that goodness and developers used it. And eventually what happened is they started to add cost, you know, metrics to each of these services and so on. So it surfaces things a little bit closer, you know, a depth time. So my whole goal is to, you know, take some of these great, you know, practices and potential tools that were being built by this wonderful spinoffs community and trying to bring it into the project. You know, front inside of CNC F. So having more projects either exposed, you know, useful. You know, Finn, ops related metrics or, you know, be able to, you know, uh, you know, tool themselves to quickly be able to get useful metrics that could be used by thin ox practitioners out there. That's my kind of goal. And, you know, I just love seeing two communities, uh, come together to improve, improve the state of the world. >>It's just a great vision, and it's needed so and again. It's not about saving money. Certainly does that if you play it right, but it's about growth and people. You need better instrumentation. You need better data. You've got cloud scale. Why not do something there, right? >>Absolutely. It's just maturity after the day because, you know, a lot of engineers, you know, they just love this whole like, you know, rental model just uses many Resource is they want, you know, without even thinking about just basic, you know, metrics in terms of, you know, how many idle instances do I have out there and so, like, people just don't think about that. They think about getting the work done, getting the job done. And if they anything we do to kind of make them think a little bit earlier about costs and impact efficiency, charge back, you know, I think the better the world isn't Honestly, you know, I do see this to me. It's It's almost like, you know, with my hippie hat on. It's like Stephen Green or for the more efficient we are. You know, the better the world off cloud is coming. Can you grow? But we need to be more efficient and careful about the resource is that we use in sentencing >>and certainly with the pandemic, people are virtually you wanted mental health, too. I mean, if people gonna be pulling their hair out, worrying about dollars and cents at scale, I mean, people are gonna be freaking out and you're in meetings justifying why you did things. I mean, that's a time waster, right? I mean, you know, talking about wasting time. >>I have a lot of friends who, you know, run infrastructure at companies. And there's a lot of you know, some companies have been, you know, blessed during this, you know, crazy time with usage. But there is a kind of laser focused on understanding costs and so on and you not be. Do not believe how difficult it is sometimes even just to get, you know, reporting out of these systems, especially if you're using, you know, multiple clouds and multiple services across them. It's not. It's non trivial. And, you know, Jared could speak to this, But, you know, a lot of this world runs in like terrible spreadsheets, right and in versus kind of, you know, nice automated tools with potential, a p I. So there's a lot of this stuff. It's just done sadly in spreadsheets. >>Yeah, salute the flag toe. One standard to rally around us. We see this all the time Jr and emerging inflection points. No de facto kind of things develop. Kubernetes took that track. That was great. What's your take on what he just said? I mean, this is a critical path item for people from all around. >>Yeah, and it's It's really like becoming this bigger and bigger data problem is well, because if you look at the way the clouds are building, they're building per seconds and and down to the very fine grain detail, you know, or functions and and service. And that's amazing for being able to have accountability. But also you get people with at the end of the month of 300 gigabyte billing files, with hundreds of millions of rows and columns attached. So, you know, that's where we do see you companies come together. So yeah, it is a spreadsheet problem, but you can now no longer open your bill in a spreadsheet because it's too big. Eso you know, there's the native tools are doing a lot of work, you know, as you mentioned, you know, AWS and Azure Google shipping a lot. There's there's great, you know, management platforms out there. They're doing work in this area, you know, there's there's people trying to build their own open source the things like Chris was talking about as well. But really, at the end of the day like this, this is This is not a technology. Changes is sort of a cultural shift internally, and it's It's a lot like the like, you know, move from data center to cloud or like waterfall to Dev ops. It's It's a shift in how we're managing, you know, the finances of the money in the business and bringing these groups together. So it it takes time and it takes involvement. I'm also amazed I look like the job titles of the people who are plugged into the Phenoms Foundation and they range from like principal engineers to tech procurement. Thio you know, product leaders to C. T. O. S. And these people are now coming together in the classic to get a seat at the table right toe, Have these conversations and talk about not How do we reduce, you know, cost in the old eighties world. But how do we work together to be more quickly to innovate, to take advantage of these cognitive technologies so that we could be more competitive? Especially now >>it's automation. I mean, all these things are at play. It's about software. I mean, software defined operations is clearly the trend we've been covering. You guys been riding the wave cloud Native actually is so important in all these modern APS, and it applies to almost every aspect of stacks, so makes total sense. Great vision. Um, Chris props to you for that, Jr. Congratulations on a great community, Jerry. I'll give you the final word. Put a plug in for the folks watching on the fin ops Foundation where you're at. What are you looking to do? You adding people, What's your objectives? Take a minute to give the plug? >>Yeah, definitely. We were in open source community, which means we thrive on people contributing inputs. You know, we've got now almost 3000 practitioner members, which is up from 1500 just this this summer on You know, we're looking for those who have either an interesting need to plug into are checked advisory council to help define standards as part of this event, The cognitive gone we're launching Ah, white paper on kubernetes. Uh, and how to do confidential management for it, which was a collaborative effort of a few dozen of our practitioners, as well as our vendor members from VM Ware and Google and APP Thio and a bunch of others who have come together to basically defined how to do this. Well, and, you know, we're looking for folks to plug into that, you know, because at the end of the day, this is about everybody sort of up leveling their skills and knowledge and, you know, the knowledge is out there, nobody's head, and we're focused on how toe drive. Ah, you know, a central collection of that be the central community for it. You enable the people doing this work to get better their jobs and, you know, contribute more of their companies. So I invite you to join us. You know, if your practitioner ITT's Frito, get in there and plug into all the bits and there's great slack interaction channels where people are talking about kubernetes or pinups kubernetes or I need to be asked Google or where we want to go. So I hope you consider joining in the community and join the conversation. >>Thanks for doing that, Chris. Good vision. Thanks for being part of the segment. And, as always, C N C F. This is an enablement model. You throw out the soil, but the 1000 flowers bloom. You don't know what's going to come out of it. You know, new standards, new communities, new vendors, new companies, some entrepreneur Mike jump in this thing and say, Hey, I'm gonna build a better tool. >>Love it. >>You never know. Right? So thanks so much for you guys for coming in. Thanks for the insight. Appreciate. >>Thanks so much, John. >>Thank you for having us. >>Okay. I'm John Furry, the host of the Cube covering Coop Con Cloud, Native Con 2020 with virtual This year, we wish we could be there face to face, but it's cute. Virtual. Thanks for watching

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>>From around the globe. It's the queue cover >>Space and cyber security >>Symposium 2020 hosted by Cal poly. >>Hello and welcome to the space and cybersecurity symposium 2020 hosted by Cal poly and the cube I'm chilling for a, your host. We have a great session here. Space, cyber security, the department of defense perspective. We have bond Google hall, director of C four ISR directorate office of the undersecretary of defense for acquisition and sustainment for the DOD and Chris Henson, technical director space and weapons, cybersecurity solutions for the national security agency. Gentlemen, thank you for taking the time for this awesome session. Thank you, John. Thank you. So we're gonna talk about the perspective of the DOD relative to space cybersecurity, a lot, going on congestion, contention, freedom, evolution innovation. So Paul, I'd like to have you start with your opening statement on how you see the space cybersecurity perspective, Don, thanks for the intro. Really appreciate it. First, let me give my thanks to Cal poly for a convening, the space and cybersecurity symposium this year, you know, and despite the pandemic, the organization and the content delivery spreading impressive, I really foot stomping. >>What can possibly be done with a number of these virtual platforms? This has been awesome. Thanks for the opportunity. I also want to recognize my colleague, Chris Nissen from NSA was actually assigned to our staff that LSD, but he brings both policy and technical perspective in this whole area. So I think you'll, you'll find his commentary, uh, and positions on things very refreshing or for today's seminar. Now space cyber security is a pretty interesting terminology for us all. Uh, cyber security means protecting against cyber threats and it's really more than just computers here on earth, right? Uh, space is the newest war fighting domain, and cybersecurity's perhaps even more of a challenge in this domain that and others. Uh, I'm sure it'll turn journal Thompson and major journals Shaw discuss the criticality of this new dorm space force. It's the newest military service in the earlier sessions and they're at the risk of repeating what they already addressed. >>Let me start by talking about what space means to DOD and what we're doing directly from my vantage point as part of the acquisition and sustainment arm of the Pentagon. Uh, what I want to share with you today is how the current space strategy ties into the national defense strategy and supports the department's operational objectives. As the director of CFRI SAR. I have come to understand how the integration of CFRI Sarcic. Billy is a powerful asset to enhance the lethality of the joint war fighter. Secretary Lord, our boss, the sec, the undersecretary for acquisition and sustainment is diligent in her pursuit to adapt and modernize acquisition processes, to influence the strategy and to focus our efforts domain are to make our objectives a reality. I think first and foremost, we are building a more lethal force. This joint force will project low Valley and custom contested environments and across all domains through an operationally integrated and resiliency for ISR infrastructure. >>We are also called debating our alliances, deepening interoperability, which is very important in a future fight and collab, collaboratively planning with those partner with us in the fight most significantly for our work in acquisition and sustainment, we continue to optimize the department for greater performance and affordability through reform of the acquisition process. Now space is our newest war fighting domain. And while it is indeed unique, it shares many common traits with the others land, air and sea all are important to the defense of the U S in conflict. No doubt about this. They will be contested and they must be defended. One domain will not win future conflicts in a joint operation in a future fight in the future conflict. They must all succeed. I see three areas being key to a DOD strategic success in space, one, developing our whole of government approach in close partnership with the private sector and our allies to prioritizing our investments in resiliency, innovation, and adaptive operations, and third responding rapidly and effectively to leverage emerging technologies and seize opportunities to advance your strengths, partnerships and alliances. >>Let me emphasize that space is increasingly congested and tested and demanded as essential delete Valley operational effectiveness and the security of our nation. Now the commercialization of space offers a broad set of investments in satellite technology, potential opportunities to leverage those investments and pathways to develop cost efficient space architecture, where the department and the nation. It's funny, there's a new race, a race for space. If you will, between commercial companies buying for dominance of space. Now the joint staff within DOD is currently building an operational construct to employ and engage as a unified force, coordinated across all domains. We call it the joint, all domain command and control. It is the framework that is under development to allow us to conduct integrated operations in the future. The objective of Jesse too is to provide the war fighter access to the decision making information while providing mission assurance of the information and resilience of the underlying terrestrial air in space networks that support them operationally. >>six to maintain seamless integration, adaptation, and employment of our capability. To sense signal connect, transmit, process control, direct, and deliver lethal capabilities against the enemy. We gain a strategic advantage through the integration of these capabilities across all the domains, by providing balance bowel space, awareness, horse protection, and weapons controlled and deployment capabilities. Now successfully any ratings, the systems and capabilities will provide our war fighters overwhelming superiority on the battlefield environment, challenged by near peer adversaries, as well as non state actors in space. The character of its employment is changing, driven by increasing demands, not just by DOD, but by the commercial sector as well. You know, more and more, uh, we see greater use of small satellite systems to address a myriad of emerging questions, ubiquitous communications, awareness, sensor diversity, and many more. Uh, as I said before, the commercial world is pioneering high rate production of small satellites in our efforts to deploy hundreds, if not thousands of nodes space X, Darlene constellation is one example. >>Another one is Amazon's Kiper, uh, Kuyper just received FCC approval to deploy like over 3000 of these different notes. While a number of these companies continue to grow. Some have struggled. They some pointed as one web, uh, nevertheless, the appetite remains strong and DOD is taking advantage of these advances to support our missions. We are currently exploring how to better integrate the DOD activities involving small satellites under the small satellite coordinating activity, scholarly call it. We want to ensure collaboration and interoperability to maximize efficiency in acquisition and operation. When we started this activity on over a year and a half ago, we documented over 70 plus separate small, small sat programs within DOD. And now we've developed a very vibrant community of interest surrounding a small satellites. Now, part of the work we have identified nine focus areas for further development. These are common areas to all systems and by continuing to expand on these, our plan is they enable a standard of practice that can be applied across all of the domains. >>This includes lawn services, ground processing distribution, and of course, a topic of interest to the symposium space security and Chris we'll, we'll talk more about that being the Houston expert, uh, in this area. Uh, one challenge that we can definitely start working on today is workforce development. Cybersecurity's unique as it straddles STEM and security and policy, the trade craft is different. And unfortunately I've seen estimates recently, so suggesting a workforce gap in the next several years, much like the STEM fields, uh, during the next session, I am a part of a panel with precedent, Armstrong, Cal poly, and Steve Jake's the founder of the national security space association to address workforce development. But for this panel, I'll look forward to having further dialogue surrounding space, opera security with Chris and John. Thank you, John >>Bob, thank you for that whole thing, Steven. Yes. Workforce gaps. We need the new skill space is here. Thank you very much. Chris Henson, technical director of space and weapons, cybersecurity solutions for the national security agency. Your statement, >>Thank you for having me. Uh, I'm one of several technical leaders in space at the national security agency. And I'm currently on a joint duty assignment at the office of under secretary of defense for acquisition and sustainment. I work under mr. GUMA hot in the C four ISR area, but almost 63 years ago on the 4th of October, 1957, Sputnik was the first artificial satellite launched by the Soviet union in space. History was made in each of you can continue to write future space history in your careers. And just like in 1957, the U S isn't alone in space to include our close partnerships and longterm activities with organizations like the Japanese space agency, the European space agency, and, uh, the Canadian space agency, just to name a few. And when we tackle cybersecurity per space, we have to address, address the idea that the communications command and control, uh, and those mission datas will transverse networks owned and operated by a variety of partners, not only.go.mil.com.edu, et cetera. We need to have all the partners address the cyber effects of those systems because the risk excepted by one is shared by all and sharing cyber best practices, lessons learned, uh, data vulnerabilities, threat data, mitigation, mitigation procedures, all our valuable takeaways, uh, in expanding this space community, improving overall conditions for healthy environment. So thank you for having me, and I appreciate the opportunity to speak to you and your audience. And I look forward to the discussion questions. Thank you. >>Thank you, Chris. Thank you, Bob. Okay. I mean open innovation, the internet, you see plenty of examples. The theme here is partners, commercial government. It's going to take a lot of people and tech companies and technologies to make space work. So we asked my first question, Bonnie, we'll start with you is what do you see as the DOD his role in addressing cybersecurity in space? Uh, it's real, uh, it's a new frontier. Um, it's not going away. It's only going to get more innovative, more open, more contested. It seems like a lot to do there. So what's your role in addressing cyber security in space? >>I think our role is to be the leader in developing and only is it the strategy, but the, uh, the implementation plan is to ensure a full of cybersecurity. If you look at the national cyber cyber strategy, I think publishing 2018 calls for like-minded countries, industry academia, and civil society. Once you mentioned John, the support technology development, uh, digital safety policy advocacy, and research you here today, and those listening are fulfilling their strategy. When you, when you develop, enable use cyber hygiene products, as examples of capabilities, you're pushing the goal to fruition. When you know, what's on your network patron network backup, you're in encrypt your network, you're hardening and preventing cyber attacks. And we in government academia in the case of Cal poly civil networks and in commercial companies, we all benefit from doing that cyber security. Uh, and I think Chris will, we'll, we'll definitely back me up on this more than passwords encryption or pharma. It's truly a mindset and a culture of enabling missions to succeed in assured in a resilient fashion. >>Chris, you're taking reaction to, to the cybersecurity challenge involved here, >>That's it, it's starting really at the highest level of governments. We have, uh, you know, the, the recent security policy directive five that just came out just a couple of days ago, recognize all the factors of cybersecurity that need to come into play. And probably the most important outcome of that as mr said, is the leadership role and that leadership, uh, blends out very well into partnership. So partnership with industry partnership with academia partnership, with, uh, other people that are exploring space. And those partnerships lend itself very naturally to sharing cybersecurity issues, topics as we come up with best practices as we come up with mitigation strategies. And as we come up with vulnerabilities and share that information, the, uh, we're not going to go alone in space, just like we're probably not going to go alone in many other industries or areas, uh, that the DOD has to be, uh, involved in many spectrums of deploying to space. >>And that deployment involves as Mr. Guzman said, encryption authentication, knowing what's on the network, knowing the, the fabric of that network. And if nothing else, this, uh, this, uh, internet of things and work from home environment that we've, uh, partaken of these last few months has even explored and expanded that notion even more dramatically as we have people dial in from all over the different, uh, locations, well space will be that natural node that, uh, natural, uh, next network and mesh involvement that we'll have to protect and explore on not just from a terrestrial involvement, but all segments of it. Th the comm segment, the space vehicle and the ground portion, >>No bond. We talked about this in our other segment, um, around with the president of Cal poly, but the operating models of the space force and the DOD and getting space. It's a software defined world, right? So cybersecurity is a real big issue. Cause you have an operating model that's requiring software to power, these low hanging satellites. That's just an extension to the network. It's distributed computing, know what this is. If you understand what technology we do in space, it's no different, it's just a different environment. So it's software defined that just lends itself well to hacking. I mean, if I'm a hacker I'm going, Hey, why not just take out a satellite and crash it down or make the GPS do something different? I mean, it's definitely an attack vector. This is a big deal. It's not just like getting credentials that are cashed on a server. You gotta really protect, >>Right? Because in one hand it space will carry not only, uh, uh, you know, for local national security information. Uh, but the, uh, I feel like at the economic wellbeing, the financial state of allowed a lot of countries and institutions, you know, more and more John lb, they'll be using space assets to, uh, uh, to make, uh, make, make all that happen. Right. So, and if you look at the, you talk, you mentioned the attack vectors in space, you know, it's not just the computers in the ground, but if you look at the whole life cycle for satellite systems in space, you know, that the, the, the tasking that you need to do that the command, the controlling of the vehicle, the data that comes down in the ground, even when you launch the, the birds, the satellites, you know, they only need to be protected because they're all somewhat vulnerable to, uh, to hacking, uh, to cyber attacks. Especially as we grow into commercialization space, it's going to be a lot more people out there playing in this world. It's going to be a lot more companies out there. And, you know, it's hard to track, uh, uh, you know, the, the potential of, of, of foreign influences as an example, and therefore the potential of being vulnerable in terms of the cyber threat. >>Gentlemen, I like you guys said to move on to this leadership role, you mentioned that you want to be a leader. I get it. The DOD is department of defense. That's a new frontier to defend war time zone. You mentioned war time opportunity potentially, but how do you guys assist that's term hat to getting done? Because there's public and private space operations happening, um, there's security challenge. What does being a leader mean? And how does the DOD department of defense assist driving the public and private? Do you lead from a project standpoint, you lead from a funding standpoint? Is it architectural? I mean, you're talking about now a new end to end architecture. It's not just cloud it's on premise. It's in devices, it's offloaded with new AI technology and Nicks and devices. It's IOT, it's all, this is all new, this is all new. What does it mean for the DOD to be a leader and how do you assist others to get involved? And what does that mean? >>Yeah, I think, uh, the one hand, you know, DOD used to lead, uh, in terms of, uh, uh, being the only source of funding for a lot of, uh, highly developmental efforts. Uh, we're seeing a different story in space. Again, I keep going back to the commercialization of space. We're seeing a lot more players, right? So in many ways >>Ally's commercial companies are actually legally leading the R and D uh, of a lot of different technologies. So we want to take, we certainly want to take advantage of that. So from a leadership standpoint, I think we, we, Lucia can come in, you know, by partnering a lot more with, with the commercial companies, uh, in 2022, the DOD released the defense, uh, uh, space strategy as an example that highlights the threats, the challenges and opportunities the United States has faced by, by sending a example of how we, how we, uh, how we counter, uh, the threats that are out there, not just the DOD, but, but the disability and the commercial sector as well. Our current conditions are strong, but we want to use four lines of effort to meet our challenges and capitalize on our desire state space, uh, lines of effort include building a comprehensive military badges space, integrating space into a national joint and combined operations. Like I mentioned before, shaping that strategic environment and cooperating with allies, partners, and industry and other U S governmental agencies, departments, and agencies to advance the cost of space to take full advantage of what space can provide us, uh, in DOD, uh, and the nation. Chris has a domain. Now, what's your take on all that? >>That's because again, it's going to take more people, >>More diverse, potentially more security >>Halls. What's your view on it? >>Well, let's, let's look at how innovation and new technologies can help us in these areas. So, uh, and, and mentioned it a couple of topics that you hit on already. One of the areas that we can improve on is certainly in the, uh, the architecture, uh, where we look at a zero trust architecture, one of the NIST standards that's come about where it talks about the authentication, uh, the need to know a granular approach, this idea of being able to protect, not just data, but the resources and how people can get access to those, whether they're coming in through an identification, authentication Prudential, or, uh, other aspects of, uh, the, the idea of not just anybody should be able to have access to data or anybody should have access once they're on the inside of the network. So that zero trust architecture is, is one approach where we can show some leadership and guidance. >>Another area is in, uh, a topic that you touched on as well was in the software area. So some innovations are coming on very rapidly and strong in this artificial intelligence and machine learning. So if we can take this AI and ML and apply it to our software development areas, they can parse so much information very quickly. And, uh, you know, this vast array of code that's going into system nowadays, and then that frees up our human, uh, explicit talent and developers that can then look at other areas and not focus on minor bawling to Beverly fix a vulnerability. Uh, they, they can really use their unique skills and talents to come up with a better process, a better way, and let the artificial intelligence and machine learning, find those common problems, those, those unknown, hidden lines of code that, uh, get put into a software alarm Prairie, and then pull down over and over again from system to system. So I think between, uh, an architecture leadership role and employee innovation are two areas that we can show, uh, some benefits and process improvement to this whole system. >>That's a great point, Chris, and you think about just the architectural computer architecture, you know, S you know, network attached storage is an advantage software defined there. You could have flash all flash arrays for storage. You could have multiple cores on a device and this new architecture, offloads things, and it's a whole new way to gain efficiencies. I mean, you got Intel, you got Nvidia, you've got armed all the processors all built in. Um, so there's definitely been commercial best practices and benefits to a new kind of architecture that takes advantage of these new things. It's just, just efficiencies. Um, but this brings up the whole supply chain conversation. I want to get your thoughts on this, because there is talk about predatory investments and access and tactics to gain supply chain access to space systems, your thoughts. >>Yeah. It's a serious threat and not just for, uh, the U S uh, space. So supply chain, if you will, is the supply chain. And I says, you know, writ large, I think, uh, I think it's a, it's a, it's a threat that's, that's real, we're we're seeing today. I just saw an example recently, uh, involving, uh, our, I think our launch services were, there was a, uh, a foreign, uh, threat that was those trying to get into a true through with predatory investments. Uh, so, uh, it is something that we need to, uh, be aware of it it's happening, uh, and is continuing to happen. Uh, it's an easy way to gain access, to, uh, do our IP. Uh, and, uh, so it's something that we, uh, are serious about in terms of, uh, awareness and, and countering >>Chris, your thoughts. I mean, we've see, I mean, I'm an open source guy. I was seen it when I grew up in the industry in the eighties, open source became a revolution, but with that, it enabled new tactics for, um, state sponsored attacks on it that became a domain in of itself. Um, that's well-documented and people talk about that all the time in cyber. Now you have open innovation with hardware, software connected systems. This is going to bring supply chain nightmare. How do you track it all? Who's got what software and what device, where the chip come from, who made it, this is the potential is everywhere. How do you see the, these tactics, whether it's a VC firm from another country or this, that, and the other thing startup. >>Yeah. So when we see, when we see coal companies being purchased by foreign investors, and, you know, we can get blocked out of those, whether it's in the food industry, or if it's in a microchip, then that microchip could be used in a cell phone or a satellite or an automobile. So all of our industries that have these companies that are being purchased, or a large born investment influx into those, you know, that could be suspect. And we, we have to be very careful with those, uh, and, and do the tracking of those, especially when those, uh, some of those parts of mechanisms are coming from off shore. And then going again, going back to, uh, the space policy directive five, it calls out for better supply chain, resource management, the tracking, the knowing the pedigree and the, the quantitative of ability of knowing where those software libraries came from, where the parts came from and the tracking and delivery of that from an end to end system. >>And typically when we have a really large vendor, they can, they can do that really well. But when we have a subcontractor to a subcontractor, to a subcontractor, their resources may not be such that they can do that. Try tracking in mitigation for counterfeits or fraudulent materials going into our systems. So it's a very difficult challenge, and we want to ensure as best we can that as we ingest those parts, as we ingest those software libraries and technologies into the system, that, uh, before we employ them, we have to do some robust testing. And I don't want to say that the last line of defense, but that certainly is a mechanism for finding out, do the systems perform as they stated, uh, on a test bench or a flat set, whatever the case may be before we actually deploy it. And then we're relying on the output or the data that comes from that, that system that may have some corrupt or suspect parts in it. >>Great point, this federal grant, >>The problem with space systems is kind of, you know, is once you, once you launch the bird or the sunlight, uh, your access to it is, is diminished significantly, right? Unless you, you go up there and take it down. Uh, so, you know, kind of to Chris's point, we need to be able to test all the different parts of insurer that is performing as, as described there ass, I spent as specified, uh, with, with good knowledge that it's, uh, it's, uh, it's trustworthy. Uh, and, uh, so we that all on the ground before we, we take it up to launch it. >>It's funny. You want agility, you want speed and you want security, and you want reliability and risk management all aggressive, and it's a technical problem. It says it's a business model problem. I'd love to get real quick. Before we jump into some of the more workforce and gap issues on the personnel side, have you guys should just take a minute to explain quickly what's the federal view. If you had to kind of summarize the federal view of the DOD and the roll with it wants to take, so all the people out there on the commercial side or students out there who are, you know, wanting to jump in, what is the current modern federal view of space cybersecurity. >>Chris, why don't you take that on I'll follow up. Okay. Uh, I don't know that I can give you the federal view, but I can certainly give you the department of defense. That cybersecurity is extremely important. And as our vendors and our suppliers, uh, take on a very, very large and important role, one area that we're looking at improving on is a cyber certification maturity model, where we, where we look at the vendors and how they implement an employee cyber hygiene. So that guidance in and of itself shows the emphasis of cyber security that when we want to write a contract or a vendor, uh, for, for a purchase, that's going to go into a space system. We'd like to know from a third party audit capability, can that vendor, uh, protect and defend to some extent the amount that that part or piece or software system is going to have a cyber protection already built into it from that vendor, from the ground floor up before it even gets put into a larger system. >>So that shows a level of the CMMC process that we've thought about and, uh, started to employ, uh, beginning in 2021 and will be further built on in, in the out years. How, how important the DOD takes that. And other parts of the government are looking at this, in fact, other nations are looking at the CMMC model. So I think it shows a concern in very many areas, uh, not just in the department of defense that they're going to adopt an approach like this. Uh, so it shows the, the pluses and the benefits of a cybersecurity model that, uh, all can build on boggy reaction. Yeah, I'll just, uh, I'll just add to that, John, you, you, you, you asked earlier about, you know, how do we, uh, track, uh, commercial entities or, or people in the space and cyber security domains? Uh, I can tell you that, uh, at least my view of it, you know, space and cyber security are new, it's exciting, it's challenging a lot technical challenges there. So I think in >>Terms of attracting the right people, personnel to work those areas, uh, I think it's, it's not only intellectually challenging, uh, but it's important for, for the dependency that NASA States, uh, and it's important for, for, for economic security, uh, writ large for, for us as well. So I think, uh, in terms of a workforce and trying to get people interested in, in those domains, uh, I hope that they see the same thing we do in terms of, of the challenges and the opportunities it presents itself in the future. >>Awesome. I love your talk on intro track there falling. You mentioned, uh, the three key areas of DOD sec success, developing a government whole government approach to partnership with the private sector. I think that's critical and the allies prioritizing the right investments on resilience, innovation, adaptive operations, and responding to rapidly to effectively emerging technology. So you can be fast, all think are all things. I all, all those things are relevant. So given that, I want to get your thoughts on the defense space strategy in 2020, the DOD released dispense defense space, strategy, highlighting threats, and challenges and opportunities. How would you summarize those threats and those challenges and opportunities? What are the, what are those things that you're watching in the defense space area? Right. >>Well, I think, I think I saw, as I said before, of course, as well, you know, uh, or, or seeing that a space will be highly contested, uh, because it's a critical element in our, in our war fighting construct, uh, Dwayne, a future conflict, I think we need to, to win space as well. So when you, when you look at our near peer adversaries, there's a lot of efforts, uh, in trying to, to, to take that advantage away from the United States. So, so the threat is real, uh, and I think it's going to continue to evolve and grow. Uh, and the more we use space, both commercial and government, I think you're going to see a lot more when these threads some AFAs itself, uh, in, in forms of cyber, cyber attacks, or even kinetic attacks in some cases as needed. Uh, so yeah, so with the, the, the threat is need growing, uh, space is congested, as we talked about, it will continually be contested in the future as well. So we need to have, uh, like we do now in, in, in all the other domains, a way to defend it. And that's what we're working on with India, with the, how do we pilot with tech, our assets in space, and how do we make sure that the data information that traverses through space assets are trust 40, um, and, uh, and, and, and free of any, uh, uh, interference >>Chris, exciting time. I'm your, if you're in technology, um, this is crossing many lines here, tech society will war time, defense, new areas, new tech. I mean, it's security, it's intoxicating at many levels, because if you think about it, it's not one thing. It's not one thing anymore. It spans a broader spectrum, these opportunities. >>Yeah. And I, and I think that expansion is, is a natural outgrowth from, as our microprocessors and chips and technology continue to shrink smaller and smaller. You know, we, we think of our, our cell phones and our handheld devices and tablets, and so on that have just continued to, uh, get embedded in our everyday society, our everyday way of life. And that's a natural extension when we start applying those to space systems. When we think of smallsats and cube sets and the technology that's, uh, can be repurposed into, uh, a small vehicle and the cost has come down so dramatically that, you know, we, we can afford to get a rapid experiments, rapid, um, exploitations and, and different approaches in space and learn from those and repeat them very quickly and very rapidly. And that applies itself very well to an agile development process, dev sec ops, and this notion of spins and cycles and refreshing and re uh, addressing priorities very quickly so that when we do put a new technology up, that the technology is very lean and cutting edge, and hasn't been years and years in the making, but it's, uh, relevant and new, and the, uh, the cybersecurity and the vulnerabilities of that have to be addressed because of, and allow that DevSecOps process to take place so that we can look at those vulnerabilities and get that new technology and those new, new experiments and demonstrations in space and get lessons learned from them over and over again. >>Well, that brings us to the next big topic I want to spend the remainder of our time on that is workforce this next generation. If I wasn't so old, I would quit my job and I would join medially. It's so much, it's a fun, it's exciting. And it's important. And this is what I think is a key point is that cybersecurity in and of itself has got a big gap of shortage of workers, nevermind, adding space to it. So this is, uh, the intersection of space and cybersecurity. There is a workforce opportunity for this next generation, a young person to person re-skilling, this is a big deal. Bong, you have thoughts on this. It's not just STEM, it's everything. >>Yeah. It's everything, you know, uh, the opportunities would have in space it's significant and tremendous. And I think, uh, if I were young, again, as you pointed out, John, uh, you know, I'm, I'm, I'm lucky that I'm in this domain in this world and I started years ago. Uh, but it continues to be exciting, uh, lots of, lots of opportunities, you know, and when you, when you look at, uh, some of the commercial space, uh, systems that are being, being put up, uh, if you look at, I mentioned Starlink before, and, and, uh, Amazon's Kuyper constellation. These guys are talking about couple of thousand satellites in space to provide ubiquitous communications for internet globally and that sort of thing. Uh, and they're not the only ones that are out there producing capability. Uh, we're seeing a lot more commercial imagery products being developed by bike, by companies, both within the U S and, and, uh, foreign foreign elements as well. So I think it's an exciting time to be in space. Certainly lots of opportunities, there's technical challenges, uh, galore in terms of, you know, not only the overcoming the physics of space, but being able to operate, uh, flexibly, uh, in, uh, get the most you can out of the capabilities we have, uh, uh, operating up as high as being cool. I mean, everyone looks at launch. >>She gets millions of views on live streams, the on demand, reruns get millions and millions of views. Um, it's, there's a lot of things there. Um, so Chris, what specifically could you share are things that people would work on? Um, jobs skills, what are some, what's the aperture, what's it look like if you zoom out and look at all the opportunities from a scale standpoint, what's out there, >>We'll talk to the aperture, but I want to give a shout out to our space force. And I mean, their, their job is to train and equip, uh, future space and, uh, that, that space talent. And I think that's going to be a huge plus up, uh, to have, uh, uh, a space force that's dedicated to training equipping, uh, the, an acquisition and a deployment model that, uh, will benefit not just the other services, but all of our national defense and our, uh, you know, our, our strategic way of, uh, how, how this company, country, employees space, uh, altogether. So having, having a space for us, I think, as a, is a huge, uh, a huge issue. And then to get to that aperture aspect of, of what you're, what you're asking and, you know, that addresses a larger workforce. Uh, we need so many different talents in, in this area. >>Uh, we can, we can have, we can employ a variety of people, uh, from technical writers to people who write, uh, write in developed software to those who, uh, are bending metal and actually, uh, working in a hardware environment. And, uh, those that do planning and launch operations and all of those spectrums and issues of jobs, or are directly related to a workforce that can contribute to, to space. And then once that data gets to the ground and employed out to a user, whether it's a data or we're looking at, uh, from a sensor recent, uh, recent events on, uh, shipping lanes, those types of things. So space has such a wide and diverse swath that the aperture's really wide open, uh, for a variety of backgrounds. And, and those that, uh, really just want to take an opportunity, take a, take a technical degree or a degree that, uh, can apply itself to a tough problem, uh, because they certainly exist in space. And we can, we can use that mindset of problem solving, whether you come at it from a hacker mindset, an ethical, a white hat approach to testing and vulnerability exploration, or somebody who knows how to actually, um, make, uh, operations, uh, safer, better, uh, through space situation awareness. So there's a, there's a huge swath of opportunity for us >>Bon talk about the, um, the cyber security enabled environment, the use cases that are possible when you have cybersecurity in play with space systems, um, which is in and of itself, a huge range of jobs, codings supply chain. We just talked about a bunch of them. There's still more connected use cases that go beyond that, that, that are enabled by it. If you think about it, and this is what the students at Cal poly and every other college and university community college, you name it, or watching videos on YouTube, anyone with a brain can jump in. If they, if they see the future, it's an all net new space force is driving awareness, but there's a whole slew of these new use cases that I call space enabled by cybersecurity systems. Your thoughts. >>Absolutely. I, you know, I was, uh, had planned on attending the, uh, uh, the cyber challenge that's Cal poly had planned in June, of course, a pandemic, uh, uh, took care of that plan. But, but I was intrigued by, by the approach that the Cal poly was taking with, with, uh, middle school and high school kids of, of, of, of exposing him to a problem set here. You have a, a satellite that came down from space, uh, and, uh, part of the challenge was to do Porensic analysis on the debris, uh, the remaining pieces of the sound like to figure out what happened. Uh, it had a, uh, a cybersecurity connotation. It was hacked. It was attacked by, by cyber threat nation, took it down. And the beauty of having these kids kind of play with, with the remaining parts of the satellite figure out what happened. >>So I was pretty exciting. I was really looking forward to participating in that, but again, the pandemic kind of blew that up, but I, I look forward to future events like that to, to get our young people intrigued and interested in, uh, in this new field of space. Now, you know, Chris was talking earlier about opportunities, the opportunity that you talk about, you know, while I would like to have people come to the government, right. To help us out. It's not, it's not just focused on government, right? There's not lots of opportunities in commercial space. I, if you will, uh, for, for a lot of talent to, uh, uh, to have, uh, to participate in. So the challenge is a man's government and the commercial sector, John, >>I mean, you get the hardcore, you know, I want to work for the DOD. I want to work for NSA. I want to work for the government. You clearly got people who want to have that kind of mission, but for the folks out there, Chris and bong that are like, I'll do I qualify it? It's like the black box of the DOD. It's like a secret thing. You got any clearance, you've got to get all these certifications. And you've got to take all kinds of tests and background checks. And, um, is it like that? And will that continue? Cause some people might say, Hey, can I even get involved? What do I do? So I know there's some private partnerships going on with companies out there in the private sector. So this is now a new, you guys seem to be partnering and going outside the comfort zone of the old kind of tactical things. What are some of those opportunities that people could get involved that they might not know about >>PR for NSA, there's a variety of workforce, uh, initiatives that, uh, uh, for anybody from a high school work study can take advantage of to, uh, those that would like have to have internships. And those that are in a traditional academic environment, there's, uh, several NSA schools across the country that have a academic and cyber acts, uh, sites of excellence that participate in projects that are shepherded and mentored by those at NSA that can get those tough problems that don't have maybe a classified or super sensitive, uh, nature that that can be worked in and in an academia environment. So, so those are two or three examples of how somebody can break into, uh, the, uh, an intelligence organization and the, and the other agencies have those, uh, opportunities as well across the intelligence community and the, the partnership between and collaborative collaboration between private industry and the agencies and the department of defense just continue to grow over and over again. And even myself being able to take care advantage of a joint duty assignment between my home organization and the Pentagon just shows another venue of somebody that's in one organization can partner and leverage with another organization as well. So I'm an example of, of that partnering that's going on today. >>So there's some innovation, bong, non traditional pathways to find talent. What are out there? What are new, what are these new nontraditional ways >>I was going to add to what Chris was, was mentioning John? Yeah. Even within view and under the purview of our chief information officer, back in 2013, the deputy surfed dirty defense signed the, uh, what we call the DOD cyberspace workforce strategy, uh, into effect. And that included a program called the cyber information technology exchange program. It's an exchange program in which a, uh, you know, private sector employee and worked for the DOD in cyber security positions, uh, span across multiple mission critical areas. So this is one opportunity to learn, uh, you know, in inside the DOD what's happening as a private sector person, if you will, uh, going back to what we talked about, you know, kinda, uh, opportunities, uh, within the government for, for somebody who might be interested, uh, you know, you don't have to be super smart, Bork and space. Uh, there's a lot of like, like Chris pointed out, there's a lot of different areas that we need to have people down within people to do, uh, to conduct the mission space. So you don't have to be mathematician mathematician. You don't have to be an engineer to succeed in this business. I think there's plenty of opportunities for, for any types of, of talent, any type of academic disciplines that, that, that, that they're out there. >>And I think, you know, Chris is shout out to the space force is really worth calling out again, because I think to me, that's a big deal. It's a huge deal. It's going to change the face of our nation and society. So super, super important. And that's going to rise the tide. I think it's gonna create, uh, some activation, uh, for a younger generation, certainly, and kind of new opportunities, new problems to solve new threats to take on and, and move it on. So really super conversation space in cybersecurity, the department of defense perspective, Von and Chris, thank you for taking the time. I'd love you guys just to close out. We'll start with you bong. And then Chris summarize for the folks watching, whether it's a student at Cal poly or other university or someone in industry and government, what is the department of defense perspective for space cybersecurity? >>Chris, won't go and take that on. I started, thank you. Uh, cyber security applies to much more than just the launch and download of mission data or human led exploration and the planning, testing, and experiments in the lab prior to launch require that cyber protection, just as much as any other space link, ground segment, trust rail network, or user data, and any of that loss of intellectual property or proprietary data is an extremely valuable and important, and really warrants, cybersecurity safeguards in any economic espionage or data exfiltration or denied access to that data I E ransomware or some other, uh, attack that can cripple any business or government endeavor. Uh, no matter how small or large, if it's left in our economic backbone, uh, clearly depends on space and GPS is more than just a direction finding our banking needs that a T and timing from P and T or whether it says systems that protect our shipping and airline industry of whether they can navigate and go through a particular storm or not, uh, even fighting forest fires picked up by a remote sensor. >>All those space-based assets, uh, require protection from spoofing date, uh, data denial or total asset loss. An example would be if a satellite sensitive optics were intentionally pointed at the sun and damaged, or if a command, uh, to avoid collision with another space vehicle was delayed or disrupted or a ground termination command. As we just saw just a few days ago at T minus three seconds prior to liftoff, if those all don't go as planned, uh, those losses are real and can be catastrophic. So the threat to space is pervasive real and genuine, and your active work across all those platforms is a necessary and appreciated. And your work in this area is critical, uh, going forward going forward. Uh, thank you for this opportunity to speak with you and, uh, talking on this important topic. >>Thank you, Chris Henson, goodbye. >>Closing remarks. Yeah. Likewise, John, uh, again, uh, as, as Chris said, thank you for, for the opportunity to discuss this very important, uh, around space, cyber security, as well as addressing, uh, at the end there, we were talking about workforce development and the need to have, uh, people, uh, in the mix for four features. We discussed with you. We need to start that recruiting early, uh, as we're doing to address, uh, the STEM gap today, we need to apply the same thing for cybersecurity. We, we absolutely need smart, innovative people to protect both Iraq. Anomic wellbeings a nation as well as our national defense. So this is the right conversation to have at this time, John and I, again, thank you and our Cal poly hose for, or, uh, having a symposium and, and having this opportunity to have this dialogue. Thank you, >>Gentlemen. Thank you for your time and great insights. We couldn't be there in person. We're here virtual for the space and cybersecurity symposium, 2020, the Cal poly I'm Jennifer with Silicon angle and the cube, your host. Thank you for watching.

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

>>Hi everyone, This system A fellow from the University of Tokyo before I thought that would like to thank you she and all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today or some of the recent works that have been done either by me or by character of Hong Kong Noise Group indicating the title of my talk is a neuro more fic in silica simulator for the commenters in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then I will show some proof of concept of the game in performance that can be obtained using dissimulation in the second part and the production of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is adapted from a recent natural tronics paper from the Village Back hard People. And this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, Interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba purification machine, or a recently proposed restricted Bozeman machine, FPD eight, by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition influx you beat or the energy efficiency off memory sisters uh P. J. O are still an attractive platform for building large theorizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particle in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system in this respect, the f. D. A s. They are interesting from the perspective, off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see. And so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for suggesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics. Orphan, chaotic because of symmetry, is interconnectivity. The infrastructure. No neck talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's a schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the Cortes in machine, which is a growing toe the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo F represents the monitor optical parts, the district optical parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback cooking cm using oh, more than detection and refugee A then injection off the cooking time and eso this dynamics in both cases of CME in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the Eyes in coping and the H is the extension of the rising and attorney in India and expect so. >>Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of >>this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted convergence to the global minimum of there's even 20 and using this approach. And so this is >>why we propose toe introduce a macro structure the system or where one analog spin or one D o. P. O is replaced by a pair off one and knock spin and one error on cutting. Viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a >>learning process for searching for the ground state of the icing. Every 20 >>within this massacre structure the role of the ER variable eyes to control the amplitude off the analog spins to force the amplitude of the expense toe, become equal to certain target amplitude. A Andi. This is known by moderating the strength off the icing complaints or see the the error variable e I multiply the icing complain here in the dynamics off UH, D o p o on Then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different, I think introduces a >>symmetry in the system, which in turn creates chaotic dynamics, which I'm showing here for solving certain current size off, um, escape problem, Uh, in which the exiled from here in the i r. From here and the value of the icing energy is shown in the bottom plots. And you see this Celtics search that visit various local minima of the as Newtonian and eventually finds the local minima Um, >>it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing hamiltonian so that we're gonna do not get stuck in any of them. On more over the other types of attractors, I can eventually appear, such as the limits of contractors or quality contractors. They can also be destabilized using a moderation of the target amplitude. And so we have proposed in the past two different motivation of the target constitute the first one is a moderation that ensure the 100 >>reproduction rate of the system to become positive on this forbids the creation of any non tree retractors. And but in this work I will talk about another modulation or Uresti moderation, which is given here that works, uh, as well as this first, uh, moderation, but is easy to be implemented on refugee. >>So this couple of the question that represent the current the stimulation of the cortex in machine with some error correction, they can be implemented especially efficiently on an F B G. And here I show the time that it takes to simulate three system and eso in red. You see, at the time that it takes to simulate the X, I term the EI term, the dot product and the rising everything. Yet for a system with 500 spins analog Spain's equivalent to 500 g. O. P. S. So in f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements tobacco cm in which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as, ah one gear repression to replicate the post phaser CIA. Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts, all the dog products, respect to the problem size. And and if we had a new infinite amount of resources and PGA to simulate the dynamics, then the non in optical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a low carrot off end and while the kite off end. Because computing the dot product involves the summing, all the terms in the products, which is done by a nephew, Jay by another tree, which heights scares a logarithmic any with the size of the system. But this is in the case if we had an infinite amount of resources on the LPGA food but for dealing for larger problems off more than 100 spins, usually we need to decompose the metrics into ah smaller blocks with the block side that are not you here. And then the scaling becomes funny non inner parts linear in the and over you and for the products in the end of you square eso typically for low NF pdf cheap P a. You know you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance started path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product. By increasing the size of this at the tree and this can be done by organizing Yeah, click the extra co components within the F p G A in order which is shown here in this right panel here in order to minimize the finding finance of the system and to minimize the long distance that the path in the in the fpt So I'm not going to the details of how this is implemented the PGA. But just to give you a new idea off why the Iraqi Yahiko organization off the system becomes extremely important toe get good performance for simulator organizing mission. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should result for solving escape problems, free connected person, randomly person minus one, spin last problems and we sure, as we use as a metric the numbers >>of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with Nina successful BT against the problem size here and and in red here there's propose F B J implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior. It's similar to the car testing machine >>and security. You see that the scaling off the numbers of metrics victor product necessary to solve this problem scales with a better exponents than this other approaches. So so So that's interesting feature of the system and next we can see what is the real time to solution. To solve this, SK instances eso in the last six years, the time institution in seconds >>to find a grand state of risk. Instances remain answers is possibility for different state of the art hardware. So in red is the F B G. A presentation proposing this paper and then the other curve represent ah, brick, a local search in in orange and center dining in purple, for example, and So you see that the scaring off this purpose simulator is is rather good and that for larger politicizes, we can get orders of magnitude faster than the state of the other approaches. >>Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FBT implementation would be faster than risk Other recently proposed izing machine, such as the Hope you know network implemented on Memory Sisters. That is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the >>restricted Bosman machine implemented a PGA proposed by some group in Brooklyn recently again, which is very fast for small promise sizes. But which canning is bad So that, uh, this worse than the purpose approach so that we can expect that for promise sizes larger than, let's say, 1000 spins. The purpose, of course, would be the faster one. >>Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better cut values that have been previously found by any other >>algorithms. So they are the best known could values to best of our knowledge. And, um, or so which is shown in this paper table here in particular, the instances, Uh, 14 and 15 of this G set can be We can find better converse than previously >>known, and we can find this can vary is 100 times >>faster than the state of the art algorithm and cp to do this which is a recount. Kasich, it s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. >>So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g A onda and carefully routing the trickle components within the P G A. And and we can draw some projections of what type of performance we can achieve in >>the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape problems respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital and, you know, free to is shown in the green here, the green >>line without that's and, uh and we should two different, uh, prosthesis for this productions either that the time to solution scales as exponential off n or that >>the time of social skills as expression of square root off. So it seems according to the data, that time solution scares more as an expression of square root of and also we can be sure >>on this and this production showed that we probably can solve Prime Escape Program of Science 2000 spins to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP or optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this, what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account out on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will >>be just based on the simple common line access for the simulator and in which will have just a classical approximation of the system. We don't know Sturm, binary weights and Museum in >>term, but then will propose a second version that would extend the current arising machine to Iraq off eight f p g. A. In which we will add the more refined models truncated bigger in the bottom question model that just talked about on the supports in which he valued waits for the rising problems and support the cement. So we will announce >>later when this is available, and Farah is working hard to get the first version available sometime in September. Thank you all, and we'll be happy to answer any questions that you have.

>>thank you very much for this opportunity to talk about Stamp with a bio design program, which is entrepreneurship education for the medical devices. My name is Julia Key Can. Oh, I am Japanese. I have seen the United States since two doesn't want on the more than half of my life after graduating from medical school is in the United States. I hope I can contribute to make them be reached between Japan that you were saying right I did the research in the period of medical devices with a patient all over the world today is my batteries met their country finished medication stamp of the city. Yeah, North Korea academia, but also a wrong. We in the industry sectors sometimes tried to generate new product which can generate revenue from their own research outward, it is explained by three steps. The first one is the debut river, which is the harbor Wrong research output to the idea which can be product eventually. That they are hard, though, is the best body, which is a hot Arboria. From idea to commercial for the other one is that we see which is a harder to make a martial hold up to become a big are revenue generating products for the academia that passed the heart is a critical on the essential to make a research output to the idea. Yeah, they're two different kind of squash for the developing process in the health care innovation, Why's bio and by all the farmer under the other one is medical device regarding the disciplining method is maybe in mechanical engineering. Electrical engineering on the medical under surgical by Obama is mainly chemical engineering, computer science, biology and genetics. However, very important difference off these to be the innovation process. Medic is suitable on these digital innovation and by Obama, is suitable discovery process needs. Yeah, in general transformation of medical research between the aroma academia output to the commercial product in the medical field is called bench to bed. It means from basically such to critical applications. But it is your bio on the path. Yeah, translation. Medical research for medical devices is better. Bench on back to bed, which means quicker Amit needs to bench on back to Greek application. The difference off the process is the same as the difference off the commercialization. Yeah, our goal is to innovate the newer devices for patient over the war. Yeah, yeah, there are two process to do innovation. One is technology push type of innovation. The other one is news, full type of innovation. Ignore the push stop Innovation is coming from research laboratory. It is suitable for the farm on the bios. Happy type of innovation. New, useful or used driven type of type of innovation is suitable for medical devices. Either Take this topic of innovation or useful type of innovation. It is important to have Mini's. We should think about what? It's waas Yeah, in 2001 stop for the Cube, API has started to stop with Bio Design program, which is on entrepreneurship education for medical devices. Our mission is educated on empowering helps technology, no based innovators on the reading, the transition to a barrier to remain a big innovation ecosystem. Our vision is to be a global leader in advancing Hearst technology innovation to improve lives everywhere. There are three steps in our process. Off innovation, identify invent on England. Yeah, yeah. The most important step is the cluster, which is I didn't buy. I didn't buy a well characterized needs is the Vienna off a grating vision. Most of the value off medical device development is due to Iraq Obina unmet needs. So we focused in this gated by creates the most are the mosque to find on the Civic on appropriate. Yeah, our barrels on the student Hickory World in March, disparate 19 that ideally include individual, which are background in many thing engineering on business. Yeah, how to find our needs. Small team will go to the hospital or clinic or environment to offer them the healthcare providers with naive eyes. The team focused. You look to keep all the um, it needs not technology. This method is senior CTO. It's a rocket car approach which can be applied all that design, thinking the team will generate at least 200 needs from economic needs. Next stick to identify Pace is to select the best. Amit Knees were used for different aspect, which can about it the nominees. These background current existing solutions market size on the stakeholders. Once we pick up ur madness from 200 nominees, they can move to the invention pates. Finally, they can't be the solution many people tend to invent on at the beginning base without carefree evaluating its unmet knees to result in a better tend to pouring love. Their whole idea, even amid NIS, is not what this is. Why most of the medical device innovation fail due to the lack off unmet needs. To avoid this Peter Hall, our approach is identify good needs. First on invention is the sex to generate the idea wrong. Unmet knees. We will use seven Rules off race Tony B B zero before judgment encourage wild ideas built on the ideas off. Others. Go Conte. One conversation time. Stay focused on the topic. The brainstorming is like association game. Somebody's idea can stimulate the others ideas. After generating many ideas, the next step is sleeping of idea whether use five different Dustin to embody the ideas. Intellectual property regulatory. Remember National Business Model on technology How, after this election step, we can have the best solution with system it needs, and finally team will go to the implementation pace. This place is more business oriented mothers. The strategy off business implementations on the business planning. Yeah, yeah, students want more than 50 starting up are spinning off from by design program. Let me show one example This is a case of just reputations. If patient your chest pain, most of that patient go to family doctor and trust. The first are probably Dr before the patient to General Securities. General Card, obviously for the patient Director, Geologist, Director, API geologist will make a reservation. Horta uses it. Test patient will come to the clinic people for devices in machine on his chest. Well, what? Two days? Right? That patient will visit clinic to put all the whole decency After a few days off. Analysis patient Come back to Dr to hear the result Each step in his money to pay. This is a minute, Knees. This is a rough sketch off the solutions. The product name is die. A patch on it can save about $620. Part maybe outpatient right here. >>Yeah, yeah. Life is stressful. We all depend on our heart with life source of our incredible machine. The body, however, sometimes are hard Need to check up. Perhaps you felt dizzy heart racing or know someone who has had a serious heart problem The old fashioned monitors that used to get from most doctors or bulky And you can't wear them exercising or in the shower. If appropriate for you, sudden life will provide you the eye rhythm. Zero patch to buy five inch band aid like patch would. You can apply to your chest in the comfort of your own home or in the gym. It will monitor your heart rate for up to 14 days. You never have to come into a doctor's office as you mail back. Patched us shortly after you were receiving. Easy to understand report of your heart activity, along with recommendations from a heart specialists to understand the next steps in your heart. Health sudden life bringing heart monitoring to you. >>This is from the TV broadcasting become Ah, this is a core value we can stamping on his breast. He has a connotation of the decent died. Now the company names Iris is in the public market cap off. This company is more than six billion di parts is replacing grasp all or that you see the examination. However, our main product is huge. The product lifecycle Very divisive, recent being it's. But if we can educate the human decision oil because people can build with other people beyond space and yeah, young broader stop on by design education is now runs the media single on Japan. He doesn't 15 PBS probably star visited Stamp of the diversity and Bang. He announced that Japan, by design, will runs with vampires. That problem? Yeah, Japan Barzan program has started a University of Tokyo Osaka University and we've asked corroborating with Japanese government on Japanese medical device Industry s and change it to that. Yeah, this year that it's batch off Japan better than parachute on. So far more than five. Starting up as being that's all. Thank you very much for your application.

>>Hi, everyone. This is L. A from Visa Research today. I would like to tell you about my work with Salim. Earlier. Took from Boston University about how to construct group with invisible inversion from heart problems on ice Arjuna graphs over I say model E eso Let me start this talk by tell you, uh, what is a group with invisible inversion? A group was invisible Inversion is defined by Hulkenberg and Mona In 2003 It says a representation off a group should satisfy two properties. The first is literally that inversion. It's heart. Namely that giving an including off group element X computing Uh, the including off its inverse his heart. The second is that the composition is still easy, namely given the including off X and Y computing the including off X plus y is easy here we're seeing. Plus, is the group operation. So let me explain this definition by going through our favorite example where discreet log it's hard, namely in the Multiplicity group of finance field. We include a group element A as G today, namely, put it into the exponents and more, uh, cute. So given G energy today finding a it's hard. So this group representation at least satisfy one way, as you mean this great look. It's hard. So let's look at at whether this a group satisfied group was invisible inversion. So it turns out it is not because given due to the A finding G to the minus A, it's still easy. So if we say this is the representation off the universe, then computing this reputation is simple. So this is a no example. Off group was invisible invasion. So the work off Falkenburg and Mona started by looking. How can we find group was invisible inversion? And what are the applications off such a group? Representation, >>It turns out, in their sisters. They did not find any group reputation representation that satisfy this property. But instead they find out that if you can find such a group and then they they have >>a cryptographic applications, namely building direct directed transitive signatures a year later in the work off Iraq at or they also find that if you can have this kind of group with invisible inversion there, you can also construct broadcast encryption with a small overhead, and this is before we know how to construct the broadcast encryption with small overhead over Terry's elliptic curve. Paris. So let's look at another attempt off constructing group with invisible inversion. So instead off defining. Still, let's look at a group where we put >>the including in the exponents and instead of defining due to the minus A as the inversion Let's define due to the one over a as the the inverse off do today. So it turns out you can also define that. And it happens that in many groups, minimally, if you more, uh, some special value a que then given G energy to the A, then competing due to the one over A is also conjectured to be hard. But if you define the group element in the experiment in that way, then multiplication in >>the group exponents is also hard, and so we cannot compose. So this is another no example where group inversion is actually difficult to compute. But composition is difficult to compute, uh, either. So for this kind of group, they cannot use this to build directly transitive signatures or broadcast encryption. So now let's make this attempt, uh, visible by allowing thio. So so thio have ability to compute composition. Namely, we represent the including off A as the follows. So first we help you today >>and then we also give an office Kate the circuit which contains a and n such that I take a group element X, and it can output due to the to a model end. So it turns out giving this circuit you have a feasibility off doing composition and in the work off yamakawa at all to show that if and that the underlying off station is io and assuming and it's an R s a moderately then Thistle >>is actually a good construction off group with invisible university. So technically, assuming I oh, we have already know candidates for group was in physical inversion. Uh, but that work still leaves the open problem off constructing group with invisible inversion without using general purpose sophistication. And in this talk, I would like to talk to tell you about a group was inversion candidate from some new certainly problems And the brief logic off this talk is the following. So elliptical insurgencies can be represented by graph, uh, and the graphs has a ship off volcanoes. For example, this one if you look imagine you're looking for a volcano from top to down and this is the Creator, and this is like the direction off going down the volcano. And arguably this is the reason which attracts me to looking to. I certainly problems, and also I certainly graphs can be an I certainly can be used to represent a group called Idea Class Group >>and then eventually we will find some group >>problems on this graph, which we conjecture to be hard. And they use map thes harness to the harness off inverting group elements in the ideal classroom. So this will be the high level overview off this talk. >>So what are a little bit curve? Assertiveness? So to talk about elliptic curve, I certainly okay spend the whole day talking about its mathematical definition and the many backgrounds off elliptic curve. But today we only have 15 minutes. So instead, let me just to give you a highlight help have overview off what I certain this and I certainly is a mapping from when a little bit of curve to another, and I certainly is an interesting equivalence relation between elliptic curves. It's interesting in its mathematical theory, over a finite field and elliptic curve can be identified by its J environment. And later, >>when we talk about elliptic, curve will think about their represented by their environment, which is a number in the finance field >>and given to elliptic curves and namely, given their environments, we can efficiently decide whether these two groups assertiveness, namely in polynomial time. And given these backgrounds, let me now jump to the exciting volcanoes. So it turns out >>the relation among I certainly occurred. Assertiveness curbs can be represented by the I certainly graphs, which looks like volcanoes. So let's first look at the graph on the left and let's fix a degree for that. I certainly so I certainly has different degrees. So let's for simplicity. Think about their crimes. So let's fix a degree Air say equals 23 >>and we will let each of the note in the graph to represent a different elliptic curve, namely a different Jane environment, and each is represent an air degree by certainly so if you fix the degree ill and I certainly is their religions, uh, they just look like what I said, like what kind of going from top to bottom and if, let's say, fix all the >>elliptic curve on the creator or, in general, all the elliptic curves on the same layer off the volcano, Then you allowed to have different degrees. So this is degree L and this is degree M, etcetera, etcetera. And then the graph actually looks like it's almost fully connected. Eso imagine all of them are connected by different degrees. And the graph structure is actually described not too long ago in the pH. Diseases off Davico Hell in 1996 and later it gets popularized in a paper in 2002 because they say, Hey, this looks like a volcano. So now the I certainly will. Kind of is they used in many reference by according the graph. >>So let me tell you a little bit more about the relation off. I certainly and the idea class group. So the short story is, if you fix a layer on the uncertainty graph, say the creator. So actually, all the notes has a 1 to 1 mapping to the group element in an ideal >>class group. The foremost Siri is the ideal class group acts on the, uh, set off a surgeon is which have the same in the more it is a Marine. But we will not go into their, uh in the talk today. So let me give you a simple example. So this is, ah, concrete representation off an ideal class group off seven group elements. And if we fix a J zero j environment off one off the grade curve, let's say this guy represents the identity in the idea class group. And then we let J one to represent one off the class group elements. Then it's inverse is just going one step back from the origin in the opposite direction S O. This is a very important picture we will use exactly the J environments to represent and the idea class group elements eso This is exactly the reputation we're gonna take, except we're gonna work with over the icy modeling. So after giving some mathematical background off elliptical by certainly in a certain graph now, let's talk about competition of problems >>and before jumping into I say model E, let me start from the, uh, more traditionally studied. I certainly problems over the finite field. The first problem is if I fix a degree, air and I give you a J environment off elliptic curve. Ast one off the note. That's first. Take an easy question. Is it easy to find all off? >>It's certainly neighbors off degree will say there is a polynomial. >>The answer is yes. And the technically there are two different ways. Uh, I will not go to the details off what they are, but what we need to know is they require serving, uh, polynomial off degree or air squares. Let's look at another problem that so imagine I select to random >>curves from an I certainly graph. So think about this. Uncertainty graph is defined over a large field, and they are super polynomial limited graphs off them. I'm choosing to random curves. >>The question is, can you find out an explicit I Certainly between them naming and Emily passed from one to the other. It turns out this >>problem is conjecture to be hard even for quantum computers, and this is exactly what was used in the post to quantum key exchange proposals in those works. So they have different structures could aside the seaside. They're just a different types off in the book is a Marine off the question is off the same nature finding and passed from one curve to the other. So these are not relevant to our work. But I would like to introduce them for for some background, off the history off. I certainly problems, >>So you have a work we need to >>study. I certainly problems over in, I say endogenous. And so the first question is even how to define. And I certainly, uh oh, and I certainly graph over the ring like, uh, over and I say modular. Same. So >>there is a general way off defining it in the special case. So in this talk, I will just talk about the special case because this is easier to understand. So think about I have the have the ability off peaking too. I certainly volcan als over multi and multi cube. That has exactly the same structure. And then I just use a C a c r T composition to stick them together. So namely a J >>zero. The value is the CRT off the J zero over. They're over the small fields P and the Cube and the N S equals to P times Q. And by the way, thes gene variants will be exactly the way to represent an ideal class group off such a size in this example is the ideal class group off, uh, with discriminate minus 250 bucks. Okay, so now let's look at what this magical over this representation. So let's look at back to the problem we start from namely, finding all the insurgents neighbors at this time over. And I see model E eso. I give you the J environment off easier and ask you to find a one off the its neighbors finding the J environment off one off its neighbors. So it turns out, even this problem is hard. And actually, we can prove this problem is as hard as factory and naive. Way off. Explaining off What's going on is that the two methods that work over the finite field that doesn't work anymore, since they both required to solve high degree polynomial model end, and that this is hard where when end is in, I certainly I say modelers. So to be useful for constructing a group off invisible inversion, we actually need to look at this called a joint neighbors. Such problems, namely, if I give you a curve zero, which represents the identity, then another crib, which represents a the group element. Your task is to find its inverse namely one off the E two candidate beneath zero. Yeah, eso it turns out this problem. We also conjectured to it to be hard and we don't know how to base it on how this a factoring, uh, again, the not even reason is the way to solve it over the finite field doesn't work because they both required to solve polynomial off degree higher than one over in i. C model is. And this is exactly the reason that we believe the group inversion is hard over deserve visitation Now. Finally, we also would like to remind the readers that for death according to the definition off group with invisible inversion, we would also like the group elements to be easy to compose. No, that's not. Make another observation that over. If you're finding the joint neighbor off, I certainly off different degree. Say, if I give you a J invent off Iwan and Jane Barrett off you to ask you to find the J environment off the three and they happened to off co prime degree I. Certainly then there is a way to find their joint neighbor because they're cold prime. And there's only one solution to solving the modular polynomial that I haven't defined out. But this is the way we make sure that composition is easy. Normally we output, including that are a cold prime so that they can be composed to summarize that we propose a group candidate group with invisible inversion from any particular I. Certainly it requires a chapter because you need to know the prime factors off. I seem odd early to set up the whole system and generated the including in our me assumption is that certain joint neighbors such problem on the I certainly graphs defined over S a moderately it's hard again group within physical inversion has the application of constructing broadcasting, corruption directed transitive signatures, and it's a very interesting problem to explore