>> TheCUBE presents Ignite 22 brought to you by Palo Alto Networks. >> Welcome back to Las Vegas, everyone. We're glad you're with us. This is theCUBE live at Palo Alto Ignite 22 at the MGM Grant in Las Vegas. Lisa Martin here with Dave Vellante, day one of our coverage. We've had great conversations. The cybersecurity landscape is so interesting Dave, it's such a challenging problem to solve but it's so diverse and dynamic at the same time. >> You know, Lisa theCUBE started in May of 2010 in Boston. We called it the chowder event, chowder and Lobster. It was a EMC world, 2010. BJ Jenkins, who's here, of course, was a longtime friend of theCUBE and made the, made the transition into from, well, it's still data, data to, to cyber. So >> True. And BJ is back with us. BJ Jenkins, president Palo Alto Networks great to have you back on theCUBE. >> It is great to be here in person on theCube >> Isn't it great? >> In Vegas. It's awesome. >> And we can tell by your voice will be, will be gentle. You, you've been in Vegas typical Vegas occupational hazard of losing the voice. >> Yeah. It was one of the benefits of Covid. I didn't lose my voice at home sitting talking to a TV. You lose it when you come to Vegas. >> Exactly. >> But it's a small price to pay. >> So things kick off yesterday with the partner summit. You had a keynote then, you had a customer, a CISO on stage. You had a keynote today, which we didn't get to see. But talk to us a little bit about the lay of the land. What are you hearing from CISOs, from CIOs as we know security is a board level conversation. >> Yeah, I, you know it's been an interesting three or four months here. Let me start with that. I think, cybersecurity in general is still front and center on CIOs and CISO's minds. It has to be, if you saw Wendy's presentation today and the threats out there companies have to have it front and center. I do think it's been interesting though with the macro uncertainty. We've taken to calling this year the revenge of the CFO and you know these deals in cybersecurity are still a top priority but they're getting finance and procurements, scrutiny which I think in this environment is a necessity but it's still a, you know, number one number two imperative no matter who you talked to, in my mind >> It was interesting what Nikesh was saying in the last conference call that, hey we just have to get more approvals. We know this. We're, we're bringing more go-to-market people on board. We, we have, we're filling the pipeline 'cause we know they're going to split up deals big deals go into smaller chunks. So the question I have for you is is how are you able to successfully integrate those people so that you can get ahead of that sort of macro transition? >> Yeah I, you know, I think there's two things I'd say about uncertain macro situations and Dave, you know how old I am. I'm pretty old. I've been through a lot of cycles. And in those cycles I've always found stronger companies with stronger value proposition separate themselves actually in uncertain, economic times. And so I think there's actually an opportunity here. The message tilts a little bit though where it's been about innovation and new threat vectors to one of you have 20, 30, 40 vendors you can consolidate become more effective in your security posture and save money on your TCOs. So one of the things as we bring people on board it's training them on that business value proposition. How do you take a customer who's got 20 or 30 tools take 'em down to 5 or 10 where Palo is more central and strategic and be able to demonstrate that value. So we do that through, we're making a huge investment in our people but macroeconomic times also puts some stronger people back on the market and we're able to incorporate them into the business. >> What are the conditions that are necessary for that consolidation? Like I would imagine if you're, if you're a big customer of a big, you know, competitor of yours that that migration is going to be harder than if you're dealing with lots of little point tools. Do those, do those point tools, are they sort of is it the end of the subscription? Is it just stuff that's off the books now? What's, the condition that is ripe for that kind of consolidation? >> Look, I think the challenge coming into this year was skills. And so customers had all of these point products. It required a lot more human intervention as Nikesh was talking about to integrate them or make them work. And as all of us know finding people with cybersecurity skills over the last 12 months has been incredibly hard. That drove, if you know, if you think about that a CIO and a CISO sitting there going, I have all all this investment in tools. I don't have the people to operate 'em. What do I need to do? What we tried to do is elevate that conversation because in a customer, everybody who's bought one of those, they they bought it to solve a problem. And there's people with affinity for that tool. They're not just going to say I want to get consolidated and give up my tool. They're going to wrap their arms around it. And so what we needed to do and this changed our ecosystem strategy too how we leverage partners. We needed to get into the CIO and CISO and say look at this chaos you have here and the challenges around people that it's, it's presenting you. We can help solve that by, by standardizing, consolidating taking that integration away from you as Nikesh talked about, and making it easier for your your high skill people to work on high skill, you know high challenges in there. >> Let chaos reign, and then reign in the chaos. >> Yes. >> Andy Grove. >> I was looking at some stats that there's 26 million developers but less than 3 million cybersecurity professionals. >> Talked about that skills gap and what CISOs and CIOs are facing is do you consider from a value prop perspective Palo Alto Networks to be a, a facilitator of helping organizations deal with that skills gap? >> I think there's a short term and a long term. I think Nikesh today talked about the long term that we'll never win this battle with human beings. We're going to have to win it with automation. That, that's the long term the short term right here and now is that people need people with cybersecurity skills. Now what we're trying to do, you know, is multifaceted. We work with universities to standardize programs to develop skills that people can come into the marketplace with. We run our own programs inside the company. We have a cloud academy program now where we take people high aptitude for sales and technical aptitude and we will put them through a six month boot camp on cloud and they'll come out of that ready to really work with the leading experts in cloud security. The third angle is partners, right, there are partners in the marketplace who want to drive their business into high services areas. They have people, they know how to train. We give them, we partner with them to give them training. Hopefully that helps solve some of the short-term gaps that are out there today. >> So you made the jump from data storage to security and >> Yeah. >> You know, network security, all kinds of security. What was that like? What you must have learned a lot in the last better part of a decade? >> Yeah. >> Take us through that. >> You know, so the first jump was from EMC. I was 15 years there to be CEO of Barracuda. And you know, it was interesting because EMC was, you know large enterprise for the most part. At Barracuda we had, you know 250,000 small and mid-size enterprises. And it was, it's interesting to get into security in small and mid-size businesses because, you know Wendy today was talking about nation states. For small and mid-size business, it's common thievery right? It's ransomware, it's, and, those customers don't have, you know, the human and financial resources to keep up with the threat factor. So, you know, Nikesh talked about how it's taken 'em four and a half years to get into cybersecurity. I remember my first week at Barracuda, I was talking with a customer who had, you know, breached data shut down. There wasn't much bitcoin back then so it was just a pure ransom. And I'm like, wow, this is, you know, incredible industry. So it's been a good, you know, transition for me. I still think data is at the heart of all of this. Right? And I have always believed there's a strong connection between the things I learned growing up at EMC and what I put into practice today at Palo Alto Networks. >> And how about a culture because I, you know I know have observed the EMC culture >> Yeah. >> And you were there in really the heyday. >> Yeah. >> Right? Which was an awesome place. And it seems like Palo Alto obviously, different times but you know, similar like laser focus on solving problems, you know, obviously great, you know value sellers, you know, you guys aren't the commodity >> Yeah. For Product. But there seemed to be some similarities from afar. I don't know Palo Alto as well as I know EMC. >> I think there's a lot. When I joined EMC, it was about, it was 2 billion in in revenue and I think when I left it was over 20, 20, 21. And, you know, we're at, you know hopefully 5, 5 5 in revenue. I feel like it's this very similar, there's a sense of urgency, there's an incredible focus on the customer. you know, Near and Moche are definitely different individuals but the both same kind of disruptive, Israeli force out there driving the business. There are a lot of similarities. I, you know, the passion, I feel privileged as a, you know go to market person that I have this incredible portfolio to go, you know, work with customers on. It's a lucky position to be in, but very I feel like it is a movie I've seen before. >> Yeah. And but, and the course, the challenges from the, the target that you're disrupting is different. It was, you know, EMC had a lot of big, you know IBM obviously was, you know, bigger target whereas you got thousands of, you know, smaller companies. >> Yes. >> And, and so that's a different dynamic but that's why the consolidation play is so important. >> Look at, that's why I joined Palo Alto Networks when I was at Barracuda for nine years. It just fascinated me, that there was 3000 plus players in security and why didn't security evolve like the storage market did or the server market or network where working >> Yeah, right. >> You know, two or three big gorillas came to, to dominate those markets. And it's, I think it's what Nikesh talked about today. There was a new problem in best of breed. It was always best of breed. You can never in security go in and, you know, say, Hey it's good I saved us some money but I got the third best product in the marketplace. And there was that kind of gap between products. I, believe in why I joined here I think this is my last gig is we have a chance to change that. And this is the first company as I look from the outside in that had best of breed as, you know Nikesh said 13 categories. >> Yeah. >> And you know, we're in the leaders quadrant and it's a conversation I have with customers. You don't have to sacrifice best of breed but get the benefits of a platform. And I, think that resonates today. I think we have a chance to change the industry from that viewpoint. >> Give us a little view of the voice of the customer. You had, was it Sabre? >> Yeah. >> That was on >> Scott Moser, The CISO from Sabre. >> Give us a view, what are you hearing from the voice of the customer? Obviously they're quite a successful customer but challenges, concerns, the partnership. >> Yeah. Look, I think security is similar to industries where we come up with magic marketing phrases and, you know, things to you know, make you want to procure our solutions. You know, zero trust is one. And you know, you'll talk to customers and they're like, okay, yes. And you know, the government, right? Joe, Joe Biden's putting out zero trust executive orders. And the, the problem is if you talk to customers, it's a journey. They have legacy infrastructure they have business drivers that you know they just don't deal with us. They've got to deal with the business side who's trying to make the money that keeps the, the company going. it's really helped them draw a map from where they're at today to zero trust or to a better security architecture. Or, you know, they're moving their apps into the cloud. How am I going to migrate? Right? Again, that discussion three years ago was around lift and shift, right? Today it's about, well, no I need cloud native developed apps to service the business the way I want to, I want to service it. How do I, so I, I think there's this element of a trusted partner and relationship. And again, I think this is why you can't have 40 or 50 of those. You got to start narrowing it down if you want to be able to meet and beat the threats that are out there for you. So I, you know, the customers, I see a lot of 'em. It's, here's where I'm at help me get here to a better position. And they know it's, you know Scott said in our keynote today, you don't just, you know have layer three firewall policies and decide, okay tomorrow I'm going to go to layer seven. That, that's not how it works. Right? There's, and, and by the way these things are a mission critical type areas. So there's got to be a game plan that you help customers go through to get there. >> Definitely. Last question, my last question for you is, is security being a board level conversation I was reading some stats from a survey I think it was the what's new in Cypress survey that that Palo Alto released today that showed that while significant numbers of organizations think they've got a cyber resiliency playbook, there's a lot of disconnect or lack of alignment at the boardroom. Are you in those conversations? How can you help facilitate that alignment between the executive team and the board when it comes to security being so foundational to any business? >> Yeah, it's, I've been on three, four public company boards. I'm on, I'm on two today. I would say four years ago, this was a almost a taboo topic. It was a, put your head in the sand and pray to God nothing happened. And you know, the world has changed significantly. And because of the number of breaches the impact it's had on brand, boards have to think about this in duty of care and their fiduciary duty. Okay. So then you start with a board that may not have the technical skills. The first problem the security industry had is how do I explain your risk profile in a way you can understand it. I'm, I'm on the board of Generac that makes home generators. It's a manufacturing, you know, company but they put Wifi modules in their boxes so that the dealers could help do the maintenance on 'em. And all of a sudden these things were getting attacked. Right? And they're being used for bot attacks. >> Yeah. >> Everybody on their board had a manufacturing background. >> Ah. >> So how do you help that board understand the risk they have that's what's changed over the last four years. It's a constant discussion. It's one I have with CISOs where they're like help us put it in layman's terms so they understand they know what we're doing and they feel confident but at the same time understand the marketplace better. And that's a journey for us. >> That Generac example is a great one because, you know, think about IOT Technologies. They've historically been air gaped >> Yes. >> By design. And all of a sudden the business comes in and says, "Hey we can put wifi in there", you know >> Connect it to a home Wifi system that >> Make our lives so much easier. Next thing you know, it's being used to attack. >> Yeah. >> So that's why, as you go around the world are you discerning, I know you were just in Japan are you discerning significant differences in sort of attitudes toward, towards cyber? Whether it's public policy, you know things like regulation where you, they don't want you sharing data, but as as a cyber company, you want to share that data with you know, public and private? >> Look it, I, I think around the world we see incredible government activity first of all. And I think given the position we're in we get to have some unique conversations there. I would say worldwide security is an imperative. I, no matter where I go, you know it's in front of everybody's mind. The, on the, the governance side, it's really what do we need to adapt to make sure we meet local regulations. And I, and I would just tell you Dave there's ways when you do that, and we talk with governments that because of how they want to do it reduce our ability to give them full insight into all the threats and how we can help them. And I do think over time governments understand that we can anonymize the data. There's, but that, that's a work in process. Definitely there is a balance. We need to have privacy, we need to have, you know personal security for people. But there's ways to collect that data in an anonymous way and give better security insight back into the architectures that are out there. >> All right. A little shift the gears here. A little sports question. We've had some great Boston's sports guests on theCUBE right? I mean, Randy Seidel, we were talking about him. Peter McKay, Snyk, I guess he's a competitor now but you know, there's no question got >> He got a little funding today. I saw that. >> Down round. But they still got a lot of money. Not of a down round, but they were, but yeah, but actually, you know, he was on several years ago and it was around the time they were talking about trading Brady. He said Never trade Brady. And he got that right. We, I think we can agree Brady's the goat. >> Yes. >> The big question I have for you is, Belichick. Do you ever question Has your belief in him as the greatest coach of all time wavered, you know, now that- No. Okay. >> Never. >> Weigh in on that. >> Never, he says >> Still the Goat. >> I'll give you my best. You know, never In Bill we trust. >> Okay. Still. >> All right >> I, you know, the NFL is a unique property that's designed for parody and is designed, I mean actively designed to not let Mr. Craft and Bill Belichick do what they do every year. I feel privileged as a Boston sports fan that in our worst years we're in the seventh playoff spot. And I have a lot of family in Chicago who would kill for that position, by the way. And you know, they're in perpetual rebuilding. And so look, and I think he, you know the way he's been able to manage the cap and the skill levels, I think we have a top five defense. There's different ways to win titles. And if I, you know, remember in Brady's last title with Boston, the defense won us that Super Bowl. >> Well thanks for weighing in on that because there's a lot of crazy talk going on. Like, 'Hey, if he doesn't beat Arizona, he's got to go.' I'm like, what? So, okay, I'm sometimes it takes a good good loyal fan who's maybe, you know, has >> The good news in Boston is we're emotional fans too so I understand you got to keep the long term long term in mind. And we're, we're in a privileged position in Boston. We've got Celtics, we've got Bruins we've got the Patriots right on the edge of the playoffs and we need the Red Sox to get to work. >> Yeah, no, you know they were last, last year so maybe they're going to win it all like they usually do. So >> Fingers crossed. >> Crazy worst to first. >> Exactly. Well you said, in Bill we trust it sounds like from our conversation in BJ we trust from the customers, the partners. >> I hope so. >> Thank you so much BJ, for coming back on theCUBE giving us the lay of the land, what's new, the voice of the customer and how Palo Alto was really differentiated in the market. We always appreciate your, coming on the show you >> Honor and privilege seeing you here. Thanks. >> You may be thinking that you were watching ESPN just now but you know, we call ourselves the ESPN at Tech News. This is Lisa Martin for Dave Vellante and our guest. You're watching theCUBE, the Leader and live emerging in enterprise tech coverage. (upbeat music)

(upbeat music) >> Good morning everybody. Welcome back to Las Vegas. This is day four of theCUBE's wall-to-wall coverage of our Super Bowl, aka AWS re:Invent 2022. I'm here with my co-host, Paul Gillin. My name is Dave Vellante. Sanjay Poonen is in the house, CEO and president of Cohesity. He's sitting in as our guest market watcher, market analyst, you know, deep expertise, new to the job at Cohesity. He was kind enough to sit in, and help us break down what's happening at re:Invent. But Paul, first thing, this morning we heard from Werner Vogels. He was basically given a masterclass on system design. It reminded me of mainframes years ago. When we used to, you know, bury through those IBM blue books and red books. You remember those Sanjay? That's how we- learned back then. >> Oh God, I remember those, Yeah. >> But it made me think, wow, now you know IBM's more of a systems design, nobody talks about IBM anymore. Everybody talks about Amazon. So you wonder, 20 years from now, you know what it's going to be. But >> Well- >> Werner's amazing. >> He pulled out a 24 year old document. >> Yup. >> That he had written early in Amazon's evolution about synchronous design or about essentially distributed architectures that turned out to be prophetic. >> His big thing was nature is asynchronous. So systems are asynchronous. Synchronous is an illusion. It's an abstraction. It's kind of interesting. But, you know- >> Yeah, I mean I've had synonyms for things. Timeless architecture. Werner's an absolute legend. I mean, when you think about folks who've had, you know, impact on technology, you think of people like Jony Ive in design. >> Dave: Yeah. >> You got to think about people like Werner in architecture and just the fact that Andy and the team have been able to keep him engaged that long... I pay attention to his keynote. Peter DeSantis has obviously been very, very influential. And then of course, you know, Adam did a good job, you know, watching from, you know, having watched since I was at the first AWS re:Invent conference, at time was President SAP and there was only a thousand people at this event, okay? Andy had me on stage. I think I was one of the first guest of any tech company in 2011. And to see now this become like, it's a mecca. It's a mother of all IT events, and watch sort of even the transition from Andy to Adam is very special. I got to catch some of Ruba's keynote. So while there's some new people in the mix here, this has become a force of nature. And the last time I was here was 2019, before Covid, watched the last two ones online. But it feels like, I don't know 'about what you guys think, it feels like it's back to 2019 levels. >> I was here in 2019. I feel like this was bigger than 2019 but some people have said that it's about the same. >> I think it was 60,000 versus 50,000. >> Yes. So close. >> It was a little bigger in 2019. But it feels like it's more active. >> And then last year, Sanjay, you weren't here but it was 25,000, which was amazing 'cause it was right in that little space between Omicron, before Omicron hit. But you know, let me ask you a question and this is really more of a question about Amazon's maturity and I know you've been following them since early days. But the way I get the question, number one question I get from people is how is Amazon AWS going to be different under Adam than it was under Andy? What do you think? >> I mean, Adam's not new because he was here before. In some senses he knows the Amazon culture from prior, when he was running sales and marketing prior. But then he took the time off and came back. I mean, this will always be, I think, somewhat Andy's baby, right? Because he was the... I, you know, sent him a text, "You should be really proud of what you accomplished", but you know, I think he also, I asked him when I saw him a few weeks ago "Are you going to come to re:Invent?" And he says, "No, I want to leave this to be Adam's show." And Adam's going to have a slightly different view. His keynotes are probably half the time. It's a little bit more vision. There was a lot more customer stories at the beginning of it. Taking you back to the inspirational pieces of it. I think you're going to see them probably pulling up the stack and not just focused in infrastructure. Many of their platform services are evolved. Many of their, even application services. I'm surprised when I talk to customers. Like Amazon Connect, their sort of call center type technologies, an app layer. It's getting a lot. I mean, I've talked to a couple of Fortune 500 companies that are moving off Ayer to Connect. I mean, it's happening and I did not know that. So it's, you know, I think as they move up the stack, the platform's gotten more... The data centric stack has gotten, and you know, in the area we're working with Cohesity, security, data protection, they're an investor in our company. So this is an important, you know, both... I think tech player and a partner for many companies like us. >> I wonder the, you know, the marketplace... there's been a big push on the marketplace by all the cloud companies last couple of years. Do you see that disrupting the way softwares, enterprise software is sold? >> Oh, for sure. I mean, you have to be a ostrich with your head in the sand to not see this wave happening. I mean, what's it? $150 billion worth of revenue. Even though the growth rates dipped a little bit the last quarter or so, it's still aggregatively between Amazon and Azure and Google, you know, 30% growth. And I think we're still in the second or third inning off a grand 1 trillion or 2 trillion of IT, shifting not all of it to the cloud, but significantly faster. So if you add up all of the big things of the on-premise world, they're, you know, they got to a certain size, their growth is stable, but stalling. These guys are growing significantly faster. And then if you add on top of them, platform companies the data companies, Snowflake, MongoDB, Databricks, you know, Datadog, and then apps companies on top of that. I think the move to the Cloud is inevitable. In SaaS companies, I don't know why you would ever implement a CRM solution on-prem. It's all gone to the Cloud. >> Oh, it is. >> That happened 15 years ago. I mean, begin within three, five years of the advent of Salesforce. And the same thing in HR. Why would you deploy a HR solution now? You've got Workday, you've got, you know, others that are so some of those apps markets are are just never coming back to an on-prem capability. >> Sanjay, I want to ask you, you built a reputation for being able to, you know, forecast accurately, hit your plan, you know, you hit your numbers, you're awesome operator. Even though you have a, you know, technology degree, which you know, that's a two-tool star, multi-tool star. But I call it the slingshot economy. This is like, I mean I've seen probably more downturns than anybody in here, you know, given... Well maybe, maybe- >> Maybe me. >> You and I both. I've never seen anything like this, where where visibility is so unpredictable. The economy is sling-shotting. It's like, oh, hurry up, go Covid, go, go go build, build, build supply, then pull back. And now going forward, now pulling back. Slootman said, you know, on the call, "Hey the guide, is the guide." He said, "we put it out there, We do our best to hit it." But you had CrowdStrike had issues you know, mid-market, ServiceNow. I saw McDermott on the other day on the, on the TV. I just want to pay, you know, buy from the guy. He's so (indistinct) >> But mixed, mixed results, Salesforce, you know, Octa now pre-announcing, hey, they're going to be, or announcing, you know, better visibility, forward guide. Elastic kind of got hit really hard. HPE and Dell actually doing really well in the enterprise. >> Yep. >> 'Course Dell getting killed in the client. But so what are you seeing out there? How, as an executive, do you deal with such poor visibility? >> I think, listen, what the last two or three years have taught us is, you know, with the supply chain crisis, with the surge that people thought you may need of, you know, spending potentially in the pandemic, you have to start off with your tech platform being 10 x better than everybody else. And differentiate, differentiate. 'Cause in a crowded market, but even in a market that's getting tougher, if you're not differentiating constantly through technology innovation, you're going to get left behind. So you named a few places, they're all technology innovators, but even if some of them are having challenges, and then I think you're constantly asking yourselves, how do you move from being a point product to a platform with more and more services where you're getting, you know, many of them moving really fast. In the case of Roe, I like him a lot. He's probably one of the most savvy operators, also that I respect. He calls these speedboats, and you know, his core platform started off with the firewall network security. But he's built now a very credible cloud security, cloud AI security business. And I think that's how you need to be thinking as a tech executive. I mean, if you got core, your core beachhead 10 x better than everybody else. And as you move to adjacencies in these new platforms, have you got now speedboats that are getting to a point where they are competitive advantage? Then as you think of the go-to-market perspective, it really depends on where you are as a company. For a company like our size, we need partners a lot more. Because if we're going to, you know, stand on the shoulders of giants like Isaac Newton said, "I see clearly because I stand on the shoulders giants." I need to really go and cultivate Amazon so they become our lead partner in cloud. And then appropriately Microsoft and Google where I need to. And security. Part of what we announced last week was, last month, yeah, last couple of weeks ago, was the data security alliance with the biggest security players. What was I trying to do with that? First time ever done in my industry was get Palo Alto, CrowdStrike, Wallace, Tenable, CyberArk, Splunk, all to build an alliance with me so I could stand on their shoulders with them helping me. If you're a bigger company, you're constantly asking yourself "how do you make sure you're getting your, like Amazon, their top hundred customers spending more with that?" So I think the the playbook evolves, and I'm watching some of these best companies through this time navigate through this. And I think leadership is going to be tested in enormously interesting ways. >> I'll say. I mean, Snowflake is really interesting because they... 67% growth, which is, I mean, that's best in class for a company that's $2 billion. And, but their guide was still, you know, pretty aggressive. You know, so it's like, do you, you know, when it when it's good times you go, "hey, we can we can guide conservatively and know we can beat it." But when you're not certain, you can't dial down too far 'cause your investors start to bail on you. It's a really tricky- >> But Dave, I think listen, at the end of the day, I mean every CEO should not be worried about the short term up and down in the stock price. You're building a long-term multi-billion dollar company. In the case of Frank, he has, I think I shot to a $10 billion, you know, analytics data warehousing data management company on the back of that platform, because he's eyeing the market that, not just Teradata occupies today, but now Oracle occupies or other databases, right? So his tam as it grows bigger, you're going to have some of these things, but that market's big. I think same with Palo Alto. I mean Datadog's another company, 75% growth. >> Yeah. >> At 20% margins, like almost rule of 95. >> Amazing. >> When they're going after, not just the observability market, they're eating up the sim market, security analytics, the APM market. So I think, you know, that's, you look at these case studies of companies who are going from point product to platforms and are steadily able to grow into new tams. You know, to me that's very inspiring. >> I get it. >> Sanjay: That's what I seek to do at our com. >> I get that it's a marathon, but you know, when you're at VMware, weren't you looking at the stock price every day just out of curiosity? I mean listen, you weren't micromanaging it. >> You do, but at the end of the day, and you certainly look at the days of earnings and so on so forth. >> Yeah. >> Because you want to create shareholder value. >> Yeah. >> I'm not saying that you should not but I think in obsession with that, you know, in a short term, >> Going to kill ya. >> Makes you, you know, sort of myopically focused on what may not be the right thing in the long term. Now in the long arc of time, if you're not creating shareholder value... Look at what happened to Steve Bomber. You needed Satya to come in to change things and he's created a lot of value. >> Dave: Yeah, big time. >> But I think in the short term, my comments were really on the quarter to quarter, but over a four a 12 quarter, if companies are growing and creating profitable growth, they're going to get the valuation they deserve. >> Dave: Yeah. >> Do you the... I want to ask you about something Arvind Krishna said in the previous IBM earnings call, that IT is deflationary and therefore it is resistant to the macroeconomic headwinds. So IT spending should actually thrive in a deflation, in a adverse economic climate. Do you think that's true? >> Not all forms of IT. I pay very close attention to surveys from, whether it's the industry analysts or the Morgan Stanleys, or Goldman Sachs. The financial analysts. And I think there's a gluc in certain sectors that will get pulled back. Traditional view is when the economies are growing people spend on the top line, front office stuff, sales, marketing. If you go and look at just the cloud 100 companies, which are the hottest private companies, and maybe with the public market companies, there's way too many companies focused on sales and marketing. Way too many. I think during a downsizing and recession, that's going to probably shrink some, because they were all built for the 2009 to 2021 era, where it was all about the top line. Okay, maybe there's now a proposition for companies who are focused on cost optimization, supply chain visibility. Security's been intangible, that I think is going to continue to an investment. So I tell, listen, if you are a tech investor or if you're an operator, pay attention to CIO priorities. And right now, in our business at Cohesity, part of the reason we've embraced things like ransomware protection, there is a big focus on security. And you know, by intelligently being a management and a security company around data, I do believe we'll continue to be extremely relevant to CIO budgets. There's a ransomware, 20 ransomware attempts every second. So things of that kind make you relevant in a bank. You have to stay relevant to a buying pattern or else you lose momentum. >> But I think what's happening now is actually IT spending's pretty good. I mean, I track this stuff pretty closely. It's just that expectations were so high and now you're seeing earnings estimates come down and so, okay, and then you, yeah, you've got the, you know the inflationary factors and your discounted cash flows but the market's actually pretty good. >> Yeah. >> You know, relative to other downturns that if this is not a... We're not actually not in a downturn. >> Yeah. >> Not yet anyway. It may be. >> There's a valuation there. >> You have to prepare. >> Not sales. >> Yeah, that's right. >> When I was on CNBC, I said "listen, it's a little bit like that story of Joseph. Seven years of feast, seven years of famine." You have to prepare for potentially your worst. And if it's not the worst, you're in good shape. So will it be a recession 2023? Maybe. You know, high interest rates, inflation, war in Russia, Ukraine, maybe things do get bad. But if you belt tightening, if you're focused in operational excellence, if it's not a recession, you're pleasantly surprised. If it is one, you're prepared for it. >> All right. I'm going to put you in the spot and ask you for predictions. Expert analysis on the World Cup. What do you think? Give us the breakdown. (group laughs) >> As my... I wish India was in the World Cup, but you can't get enough Indians at all to play soccer well enough, but we're not, >> You play cricket, though. >> I'm a US man first. I would love to see one of Brazil, or Argentina. And as a Messi person, I don't know if you'll get that, but it would be really special for Messi to lead, to end his career like Maradonna winning a World Cup. I don't know if that'll happen. I'm probably going to go one of the Latin American countries, if the US doesn't make it far enough. But first loyalty to the US team, and then after one of the Latin American countries. >> And you think one of the Latin American countries is best bet to win or? >> I don't know. It's hard to tell. They're all... What happens now at this stage >> So close, right? >> is anybody could win. >> Yeah. You just have lots of shots of gold. I'm a big soccer fan. It could, I mean, I don't know if the US is favored to win, but if they get far enough, you get to the finals, anybody could win. >> I think they get Netherlands next, right? >> That's tough. >> Really tough. >> But... The European teams are good too, but I would like to see US go far enough, and then I'd like to see Latin America with team one of Argentina, or Brazil. That's my prediction. >> I know you're a big Cricket fan. Are you able to follow Cricket the way you like? >> At god unearthly times the night because they're in Australia, right? >> Oh yeah. >> Yeah. >> I watched the T-20 World Cup, select games of it. Yeah, you know, I'm not rapidly following every single game but the World Cup games, I catch you. >> Yeah, it's good. >> It's good. I mean, I love every sport. American football, soccer. >> That's great. >> You get into basketball now, I mean, I hope the Warriors come back strong. Hey, how about the Warriors Celtics? What do we think? We do it again? >> Well- >> This year. >> I'll tell you what- >> As a Boston Celtics- >> I would love that. I actually still, I have to pay off some folks from Palo Alto office with some bets still. We are seeing unprecedented NBA performance this year. >> Yeah. >> It's amazing. You look at the stats, it's like nothing. I know it's early. Like nothing we've ever seen before. So it's exciting. >> Well, always a pleasure talking to you guys. >> Great to have you on. >> Thanks for having me. >> Thank you. Love the expert analysis. >> Sanjay Poonen. Dave Vellante. Keep it right there. re:Invent 2022, day four. We're winding up in Las Vegas. We'll be right back. You're watching theCUBE, the leader in enterprise and emerging tech coverage. (lighthearted soft music)

(digital pulsing music) >> We're back at VeeamON 2022. My name is Dave Vellante. I'm here with my co-host David Nicholson. I've got another mass boy coming on. Patrick Osborne is the vice president of the storage business unit at HPE. Good to see you again, my friend. It's been a long time. >> It's been way too long, thank you very much for having me. >> I can't even remember the last time we saw each other. It might have been in our studios in the East Coast. Well, it's good to be here with you. Lots have been going on, of course, we've been following from afar, but give us the update, what's new with HPE? We've done some stuff on GreenLake, we've covered that pretty extensively and looks like you got some momentum there. >> Quite a bit of momentum, both on the technology front and certainly the customer acquisition front. The message is certainly resonating with our customers. GreenLake is, that's the transformation that's fueling the future of Hewlett Packard Enterprise. So the momentum is great on the technology side. We're at well over 50 services that we're providing on the GreenLake platform. Everything from solutions and workloads to compute, networking and storage. So it's been really fantastic to see the platform and being able to really delight the customers and then the momentum on the sales and the customer acquisition side, the customers are voting with their dollars, so they're very happy with the platform, certainly from an operational perspective and a financial consumption perspective and so our target goal, which we've said a bunch of times is we want to be the hyperscaler on on-prem. We want to provide that customer experience to the folks that are investing in the platform. It's going really well. >> I'll ask you a question, as a former analyst, it could be obnoxious and so forth, so I'll be obnoxious for a minute. I wrote a piece in 2010 called At Your Storage Service, saying the future of storage and infrastructure as a service, blah, blah, blah. Now, of course, you don't want to over-rotate when there's no market, there was no market for GreenLake in 2010. Do you feel like your timing was right on, a little bit late, little bit early? Looking back now, how do you feel about that? >> Well, it's funny you say that. On the timing side, we've seen iterations of this stops and start forever. >> That's true. Financial gimmicks. >> I started my career at Sun Microsystems. We talked about the big freaking Web-tone switch and a lot of the network is the computer. You saw storage networks, you've seen a lot, a ton of iterations in this category, and so, I think the timing's right right now. Obviously, the folks in the hyperscaler class have proved out that this is something that's working. I think for us, the big thing that's really resonating with the customers is they want the operational model and they want the consumption model that they're getting from that as a service experience, but they still are going to run a number of their workloads on-prem and that's the best place to do it for them economically and we've proved that out. So I think the time is here to have that bifurcated experience from operational and financial perspective and in the past, the technology wasn't there and the ability to deliver that for the customers in a manner that was useful wasn't there. So I think the timing's perfect right now to provide them. >> As you know, theCUBE has had a presence at HPE Discover. Previous, even HP Discover and same with Veeam. But we got a long history with HP/HPE. When Hewlett Packard split into two companies, we made the observation, Wow, this opens up a whole new ecosystem opportunity for HPE generally, in storage business specifically, especially in data protection and backup, and the Veeam relationship, the ink wasn't dry and all of a sudden you guys were partnering, throwing joint activities, and so talk about how that relationship has evolved. >> From my perspective, we've always been a big partnering company, both on the route to market side, so our distributors and partners, and we work with them in big channel business. And then on the software partnership side, that's always evolving and growing. We're a very open ecosystem and we like to provide choice for our customers and I think, at the end of the day, we've got a lot of things that we work on jointly, so we have a great value prop. First phase of that relationship was partnering, we've got a full boat of product integrations that we do for customers. The second was a lot of special sauce that we do for our customers for co-integration and co-development. We had a huge session today with Rick Vanover and Frederico on our team here to talk about ransomware. We have big customers suffering from this plague right now and we've done a lot together on the engineering side to provide a very, very well-engineered, well thought out process to help avoid some of these things. And so that wave, too, of how do we do a ton of co-innovation together to really delight our customers and help them run their businesses, and I think the evolution of where we're going now, we have a lot of things that are very similar, strategically, in terms of, we all talk about data services and outcomes for our customers. So at the end of the day, when we think about GreenLake, like our virtual machine backup as a service or disaster recovery, it's all about what workloads are you running, what are the most important ones, where do you need help protecting that data? And essentially, how can we provide that outcome to you and you pay it as an outcome. And so we have a lot of things that we're working on together in that space. >> Let's take a little bit of a closer look at that. First of all, I'm from California, so I'm having a really hard time understanding what either of you were saying. Your accents are so thick. >> We could talk in Boston. >> Your accents are so thick. (Dave laughing) I could barely, but I know I heard you say something about Veaam at one point. Take a closer look at that. What does that look like from a ransomware perspective in terms of this concept of air gaping or immutable, immutable volumes and just as an aside, it seems like Veeam is a perfect partnership for you since customers obviously are going to be in hybrid mode for a long time and Veeam overlays that nicely. But what does it look like specifically? Immutable, air gap, some of the things we've been hearing a lot about. >> I'm exec sponsor for a number of big HPE customers and I'll give you an example. One of our customers, they have their own cloud service for time management and essentially they're exploited and they're not able to provide their service. It has huge ripple effect, if you think about, on inability to do their service and then how that affects their customers and their customers' employees and all that. It's a disaster, no pun intended. And the thing is, we learn from that and we can put together a really good architectures and best practices. So we're talking today about 3-2-1-1, so having three copies of your data, two different types of media, having an offline copy, an offsite copy and an offline copy. And now we're thinking about all the things you need to do to mitigate against all the different ways that people are going to exploit you. We've seen it all. You have keys that are erased, primary storage that is compromised and encrypted, people that come in and delete your backup catalog, they delete your backups, they delete your snapshots. So they get it down to essentially, "I'm either going to have one set of data, it's encrypted, I'm going to make you pay for it," and 40 percent of the time they pay and they get the data back, 60 percent of the time they pay and they get maybe some of the data back. But for the most part, you're not getting your data back. The best thing that we can do for our customers that come with a very prescriptive set of T-shirt configuration sizes, standardization, best practices on how they can take this entire ecosystem together and make it really easy for the customers to implement. But I wouldn't say, it's never bulletproof, but essentially, do as much as you can to avoid having to pay that ransomware. >> So 3-2-1-1, three copies, meaning local. >> Patrick: Yeah. >> So you can do fast recovery if you need to. Two different types of media, so tape fits in here? Not necessarily flashing and spinning disks. Could it be tape? >> A lot of times we have customers that have almost four different types. So they are running their production on flash. We have Alletras with HPE networking and servers running specific workloads, high performance. We have secondary storage on-prem for fast recovery and then we have some form of offsite and offline. Offsite could be object storage in the cloud and then offline would be an actual tape backup. The tape is out of the tape library in a vault so no one can actually access it through the network and so it's a physical copy that's offline. So you always have something to restore. >> Patrick, where's the momentum today, specifically, we're at VeeamON, but with regard to the Veeam partnership, is it security and ransomware, which is a new thing for this world. The last two years, it's really come to the top. Is it cloud migration? Is it data services and data management? Where's the momentum, all of the above, but maybe you could help us parse that. >> What we're seeing here at Hewlett Packard Enterprise, especially through GreenLake, is just an overall focus on data services. So what we're doing is we've got great platforms, we always had. HPE is known as an engineering company. We have fantastic products and solutions that customers love. What we're doing right now is taking, essentially, a lot of the beauty of those products and elevating them into an operational experience in the cloud, so you have a set of platforms that you want to run, you have machine critical platform, business critical, secondary storage, archival, data analytics and I want to be able to manage those from the cloud. So fleet management, HCI management, protocol management, block service, what have you, and then I want a set of abstracted data services that are on top of it and that's essentially things like disaster recovery, backup, data immutability, data vision, understanding what kind of data you have, and so we'll be able to provide those services that are essentially abstracted from the platforms themselves that run across multiple types of platforms. We can charge them on outcome based. They're based on consumption, so you think about something like DR, you have a small set of VMs that you want to protect with a very tight RPO, you can pay for those 100 VMs that are the most important that you have. So for us driving that operational experience and then the cloud data service experience into GreenLake gives customers a really, gives them a cloud experience. >> So have you heard the term super cloud? >> Patrick: Yeah. (chuckles) >> Have you? >> Patrick: Absolutely. >> It's term that we kind of coined, but I want to ask you about it specifically, in terms of how it fits into your strategy. So the idea is, and you kind of just described it, I think, whether your data is on-prem, it's in the cloud, multiple clouds, we'll talk about the edge later, but you're hiding the underlying complexities of the cloud's APIs and primitives, you're taking care of that for your customers, irrespective of physical location. It's the common experience across all those platforms. Is that a reasonable vision, maybe, even from a technical standpoint, is it part of HPE strategy and what does it take to actually do that, 'cause it sounds nice, but it's probably pretty intense? >> So the proof's in the pudding for us. We have a number of platforms that are providing, whether it's compute or networking or storage, running those workloads that they plum up into the cloud, they have an operational experience in the cloud and now they have data services that are running in the cloud for us in GreenLake. So it's a reality. We have a number of platforms that support that. We're going to have a set of big announcements coming up at HPE Discover. So we led with Alletra and we have a block service, we have VM backup as a service and DR On top of that. That's something that we're providing today. GreenLake has over, I think, it's actually over 60 services right now that we're providing in the GreenLake platform itself. Everything from security, single sign on, customer IDs, everything, so it's real. We have the proof point for it. >> So, GreenLake is essentially, I've said it, it's the HPE cloud. Is that a fair statement? >> A hundred percent. >> You're redefining cloud. And one of the hallmarks of cloud is ecosystem. Roughly, and I want to talk more about you got to grow that ecosystem to be successful in cloud, no question about it. And HPE's got the chops to do that. What percent of those services are HPE versus ecosystem partners and how do you see that evolving over time? >> We have a good number of services that are based on HPE, our tried and true intellectual property. >> You got good tech. >> Absolutely, so a number of that. And then we have partners in GreenLake today. We have a pretty big ecosystem and it's evolving, too. So we have customers and partners that are focused, our customers want our focus on data services. We have a number of opportunities and partnerships around data analytics. As you know, that's a really dynamic space. A lot of folks providing support on open source, analytics and that's a fast moving ecosystem, so we want to support that. We've seen a lot of interest in security. Being able to bring in security companies that are focused on data security. Data analytics to understand what's in your data from a customer perspective, how to secure that. So we have a pretty big ecosystem there. Just like our path at HPE, we've always had a really strong partnership with tons of software companies and we're going to continue to do that with GreenLake. >> You guys have been partner-friendly, I'll give you that. I'm going to ask Antonio this at Discover in a couple of weeks, but I want to ask you, when you think about, again, to go back to AWS as the prototypical cloud, you look at a Snowflake and a Redshift. The Redshift guys probably hate Snowflake, but the EC2 guys love them, sell a lot of compute. Now you as a business unit manager, do you ever see the day where you're side by side with one of your competitors? I'm guessing Antonio would say absolutely. Culturally, how does that play inside of HPE? I'm testing your partner-friendliness. How would you- >> Who will you- >> How do you think about that? >> At the end of the day, for us, the opportunity for us is to delight our customers. So we've always talked about customer choice and how to provide that best outcome. I think the big thing for us is that, from a cost perspective, we've seen a lot of customers coming back to HPE repatriation, from a repatriation perspective for a certain class of workloads. From my perspective, we're providing the best infrastructure and the best operational services at the best price at scale for these costumers. >> Really? It definitely, culturally, HPE has to, I think you would agree, it has to open up. You might not, you're going to go compete, based on the merit- >> Absolutely. >> of your product and technology. The repatriation thing is interesting. 'Cause I've always been a repatriation skeptic. Are you actually starting to see that in a meaningful way? Do you think you'll see it in the macro numbers? I mean, cloud doesn't seem to be slowing down, the public cloud growth, I mean, the 35, 40 percent a year. >> We're seeing it in our numbers. We're seeing it in the new logo and existing customer acquisition within GreenLake. So it's real for us. >> And they're telling you? Pure cost? >> Cost. >> So it's that's simple. >> Cost. >> So, they get the cloud bill, you do, too. I'd get the email from my CFO, "Why the cloud bill so high this month?" Part of that is it's consumption-based and it's not predictable. >> And also, too, one of the things that you said around unlocking a lot of the customer's ability from a resourcing perspective, so if we can take care of all the stuff underneath, the under cloud for the customer, the platform, so the stores, the serving, the networking, the automation, the provisioning, the health. As you guys know, we have hundreds of thousands of customers on the Aruba platform. We've got hundreds of thousands of customers calling home through InfoSight. So we can provide a very rich set of analytics, automated environment, automated health checking, and a very good experience that's going to help them move away from managing boxes to doing operational services with GreenLake. >> We talk about repatriation often. There was a time when I think a lot of us would've agreed that no one who was born in the cloud will ever do anything other than grow in the cloud. Are you seeing organizations that were born in the cloud realizing, "Hey, we know what our 80 percent steady state is and we've modeled this. Why rent it when we can own it? Or why rent it here when we can have it as operational cost there?" Are you seeing those? >> We're seeing some of that. We're certainly seeing folks that have a big part of their native or their digital business. It's a cost factor and so I think, one of the other areas, too, that we're seeing is there's a big transformation going on for our partners as well, too, on the sell-through side. So you're starting to see more niche SaaS offerings. You're starting to see more vertically focused offerings from our service provider partners or MSPs. So it's not just in either-or type of situation. You're starting to see now some really, really specific things going on in either verticals, customer segmentation, specific SaaS or data services and for us, it's a really good ecosystem, because we work with our SP partners, our MSP partners, they use our tech, they use our services, they provide services to our joint customers. For example, I know you guys have talked to iland here in the past. It's a great example for us for customers that are looking for DR as a service, backup as a service hosting, so it's a nice triangle for us to be able to please those customers. >> They're coming on to tomorrow. They're on 11/11. I think you're right on. The one, I think, obvious place where this repatriation could happen, it's the Sarah Wong and Martin Casano scenario where a SaaS companies cost a good sold become dominated by cloud costs. And they say, "Okay, well, maybe, I'm not going to build my own data centers. That's probably not going to happen, but I can go to Equinix and do a colo and I'm going to save a ton of dough, managing my own infrastructure with automation or outsourcing it." So Patrick, got to go. I could talk with you forever. Thank you so much for coming back in theCUBE. >> Always a pleasure. >> Go, Celts. How you feeling about the, we always talk sports here in VeeamON. How are you feeling about the Celts today? >> My original call today was Celtics in six, but we'll see what happens. >> Stephen, you like Celtics? Celtics six. >> Stephen: Celtics six. >> Even though tonight, they got a little- >> Stephen: Still believe, you got to believe. >> All right, I believe. >> It'd be better than the Miami's Mickey Mouse run there, in the bubble, a lot of astronauts attached to that. (Dave laughing) >> I love it. You got to believe here on theCUBE. All right, keep it right- >> I don't care. >> Keep it right there. You don't care, 'cause you're not from a sports town. Where are you in California? >> We have no sports. >> All right, keep it right there. This is theCUBE's coverage of VeeamON 2022. Dave Vellante for Dave Nicholson. We'll be right back. (digital music)

(pulsing music) (digital music) >> Welcome back to the Seaport in Boston. You're watching theCUBE's coverage of Red Hat Summit 2022. A lot of bummed out Bruins fans, but a lot of happy Celtics fans. We're optimistic for tonight, Boston's crazy sports town, but we're talking tech, we're talking open source. Dr. Amanda Purnell is here. She's the director of data and analytics innovation at the US Department of Veteran Affairs, and Ben Cushing is the chief architect for federal health and life sciences at Red Hat. Folks, welcome to theCUBE, thank for coming on. >> Thank you for having us. >> So glad to be here. >> So we heard your keynote this morning, project Arches. Now you were telling us just briefly about your previous life as a clinician. >> That's right. >> That's really interesting, because you know what the outcome has to be. So talk about that project in your perspective. What the goals were and how you actually got it done. >> I could tell the long view. I'm a psychologist by training. I spent the first 10 years of my VA career providing care to veterans. So engaging in healthcare behavior change, providing training to providers and really trying to understand what is the care pathway for veterans, what's the experience of veterans along each of those touchpoints, and it became clear to me over time that there were opportunities for us to improve the transitions of care and provide better information at the right time to improve those decisions that are being made at the point of care. Ben and I were just talking before we began today, part of the core of the development of Arches was beginning with human-centered design. We wanted to interview and better understand what was the experience across the VA of many different stakeholders and trying to access meaningful information, understand in that moment what do I need to make a decision with a veteran or what do I need to make a decision with my care team and how can I improve the quality of care for veterans? And so, hundreds of interviews later, it became clear to us that we wanted to help those individuals already working for the VA to continue to improve excellence of care and one of those ways that we're trying to do that is using technology to make life easier for our veterans and for our clinicians. >> I always like to say, they say, "Follow the money." I like to follow the data. And you said something in your keynote about nurses have to have access to information and it just gets to an architectural question, because as a caregiver, you have to get insights and data and you need it fast, 'cause you're saving lives, but a lot of times, architectures are very centralized. They're monolithic and you have to beg, borrow, steal, break through blockers to get to the data that you need. How do you square that circle in today's world? Maybe you could talk about that, and then specific to Arches, how you dealt with that. >> I can dive into that a little bit. I have to say, Amanda had touched on this during the keynote, VA was one of the first, if not the first, healthcare organizations in the world to actually adopt electronic health records and because of that, they just have this incredibly rich amount of historical data and the challenge, as you pointed out, is gaining access to it. So there are a number of programs within VA designed specifically for that. And they are bringing data not just from the data warehouses, but also data from the electronic health records that are running inside of VA right now, and then also third party community data sets, as well as applications that run inside the VA. Now the value here really happens when you produce insights. Data by itself is useless. >> Lot of data out there. They're plentiful. >> You need to create knowledge and then you need that knowledge to inform your process that comes next. Those actions are really what matters. All of healthcare is process and activity and data is really just a historical record. I mean, all data that we look at is happening in the past and then as we're reading it, we're producing knowledge, again, to inform our process. Arches, the program itself, is right in that space at the knowledge layer of actually taking that data and turning it into actual insight and something that is usable and insightful for clinicians to affect the ability to deliver better care and also to actually improve their own working experience. A lot of the models that are getting built out are specifically designed to help their workflow, help them reach better outcomes for the veterans, but also for themselves, because if we can care for the providers, it'll certainly help them care for the patients even more so. >> So how does it work? I mean, from the provider's perspective, how was their life improved by Arches? >> That's a great question. We want to make it easier to access the information. So as Ben noted, the average person providing care in the field doesn't know how to code, doesn't know how to pull a unique request for an individual data point, and what we're trying to do with Arches is provide a user interface that allows for both a non-technical person and a very technical person to access information, and then what gets provisioned in front of a provider is something that is farther abstracted from the underlying data layer and more like here's a specific insight. So I use the example in my keynote of chronic kidney disease. So what's provisioned to the provider in that moment is this person is at higher risk for chronic kidney disease based on this basic information. So it's surfacing just the right amount of information to allow for that care pathway to be improved, but the physician doesn't need to see all of the layers of code underneath. They need to trust that it's worthwhile, but they don't need to know all the background abstractions. >> So it's a self-service, essentially, infrastructure in that sense. You're hiding the underlying complexities. You gave an example in your keynote of an individual who realized that they were under counting the probability of a potential disease for African Americans. >> Yes. >> I believe she just rewrote the algorithm. >> She did. >> Describe that process, because in a lot of organizations, injecting that new algorithm may have required new data sources, would take an act of the Pope to do. How did it work in Arches? >> This is what I get excited about with Arches is that we have the opportunity to empower enthusiastic people like Dr. Joshi to discover an insight and she's a talented informaticist, so she could do the technical work and provision a container for her to work in, for her to do the data analysis, the underground stuff that we're not letting the average provider have to cope with. We were able to provision the tools that she need, the environment that she needed to be able to test and develop the new insight, confirm that they're there and then begin to validate that and test it in other facilities. So our thinking is, how do we bring the resources to the users rather than saying to the users, "This is what's available. Good luck." (chuckles) >> So we've been talking a lot about, I'm sorry, go ahead. >> I want to add on to that. What we're actually experiencing inside of healthcare right now is the emergence of of learning health systems. >> Yes. >> And this is a great example of that. The terrifying number is, it takes 17 years for new knowledge that gets created with healthcare research, whether it's NIH or VA or elsewhere, it takes 17 years for those practices to make their way into practice. Generally the way that happens is through the education of new staff. And so the dissemination of that knowledge is just so freaking slow that we cannot move nimbly enough to take on that new knowledge and actually implement it in clinical space. What Amanda's describing is something that now happens in months. New knowledge getting produced and then actually getting disseminated out, both the insights, whether they are those probabilities, predictions and recommendations and the actual processes, which are getting automated, as well. So if you think about healthcare as just a process, you can automate a whole lot of that and we can move that needle really fast and actually take that 70-year number down to a couple months. >> In the early days when we were all talking about AI and getting excited about digital, I would often ask the question, will machines be able to make better diagnoses than doctors and to your point, Ben, that's not the right question. >> Exactly. >> It isn't the right question. >> The question is, how can machines compress the time to better patient outcomes- >> Yes. >> in concert with humans and that's what we're seeing now. >> That's right, it's surfacing those insights to start a conversation. >> We've been talking a lot about artificial intelligence for the last two days. As clinician or someone with a clinical background, how do you see the clinical experience changing as machines grow more intelligent? >> I think that there's a learning curve for people to feel confident in an artificial intelligence. It makes sense. So someone spent decades, perhaps, of their life obtaining medical training, doing fellowships, doing additional training that they have trust in that deep training. There are times, however, where a technology is able to surface something that we didn't know that we didn't know and it's important, as we make use of artificial intelligence, that we clearly validate it with independent means and that we clearly also bring in additional analysis to understand what are the elements and then test that new technology in an environment before we scale it widely, so that clinicians can see, yes, this was useful. If it wasn't useful, how can we make it even better? So it goes back to what we were talking about earlier that we have to bring in human-centered design to figuring out how do we make use of AI or machine learning models and make sure that there's trust in those models and that we can clearly articulate value for the clinicians and care teams on the ground. >> Is that a natural evolution of Arches? >> This is all built around it. Arches is the technology platform, but there's no magical technology that's going to change how humans interact. And so the way we think about each project is we think about what are the technological components and what are the human factors components? And we have to think about the entire care pathway. I'll go back to that example, the chronic kidney disease. She identified that we were under identifying African Americans for chronic kidney disease. So she changed the algorithm. Not only did she change the algorithm, we also had to think about who would be informed of those changes, how would that change, who would be connected to the veteran in that point of care and build out the care pathway in the care team and that's really how you actually influence an outcome. Surfacing an insight is important, but it's one part of a much larger picture. >> So what is Arches? You said it's a technology platform built on open source. At least, there's a lot of open source in there. And it's got API connectors to all the legacy technologies that you need it to. Can you describe, paint a picture of what it actually is? >> Arches is evolving as it should. So it's designed to meet the unique needs that aren't being met by other infrastructure in the VA. So we started first by identifying the need for cloud compute, so it's in the cloud, it has open source technology so that we're not stuck with any one provider and also has the ability to use containers to be able to move insights out of Arches to an enterprise solution. We're also bringing in multi-cloud strategy, which also something had been discussed quite a bit at this conference, to make sure that we're not saying only one cloud provider can be the solution for veterans' needs. Our mission is serving veterans and so we want to have access to all the technology and not just one and so we're looking at how do we expand the scope to make sure that we have the most variety possible so we can meet the needs of veterans. >> I can add a little bit to it, as well. Think of Arches as a program. It's an incubation space under the office of innovation. So it's a place where the governance allows for trying new ideas and really pushing the envelope for VA in general. There's not a lot of organizations, if any at VA, that allow for that type of incubation and so Arches is in a unique position to create new technologies and new novel approaches to solving big problems. And then the next step to that is moving the work from Arches out into the enterprise, as you called it out. So for instance, the system of engagement where the actual clinicians interact with patients, the model needs to find its way there and we can't do that in a way that disturbs the current workflow that the clinicians have. We need to be able to bring the model to where the clinician is, have those recommendations, probabilities and predictions surfaced to the clinician in a way that is precise to their existing workflow. They need it at the time they need it. Arches itself is not delivering that part of it. It's more like the place where the innovation happens and the incubation really occurs and then it's about taking this container, really, and moving out to other systems that are already deployed out to the hospitals, the edge, and in the cloud. >> And the federated governance occurs in Arches or elsewhere? >> It happens across the continuum. It's starting in Arches. the clinical validation that happens there is wickedly important, because the clinicians need to know that what they're working with is actually legit. And so when they know that the researchers and the clinicians who are involved in that incubation period have done their work, they can feel confident with the recommendations they're getting from the machine learning models that are getting deployed to one of them. >> So many questions, so little time. What's the business impact? How would you describe that? >> For me, it's an emotional impact. People have a sense of, "I have a place to develop a solution and I can get in there quick, and I can test out an idea. I could potentially partner with an external partner or if I have the talents and skills to do it myself." It's empowering all of those innovators who have great ideas to work together to test and develop and validate solutions, and they're not waiting years to get the idea off the ground. >> Amazing. >> Go ahead, bring it. >> Is Arches open source? >> Arches is a platform and it has open source component. So that the underlying infrastructure of technology is open source. >> Why was it important to you that this be built on an open source platform? >> It's important for us that we not marry ourself to any one technology and that we allow for, as much as possible, transparency and many different tools and the right tools for the right solution. So we didn't want to find ourselves connected to only one way of doing things. We want to have versatility to have the right tool for the right problem at the right time. >> I'm so sorry, we're out of time. This is so interesting and I really appreciate you here guys, coming on and sharing your insights for theCUBE audience. All right, keep it right there. This is Dave Vellante for Paul Gillin. We're in day two of Red Hat Summit 2022. You're watching theCUBE. (digital pulsing music) >> Due to the pandemic, the federal government declared a public health emergency, which created an urgency for healthcare coverage. >> One of the biggest-

>>Mm. Welcome back to the seaports in Boston City is abuzz. Bruins tonight, Celtics Tomorrow night. We're all excited. We're talking open source, which is a very exciting topic. Every company is using open source. I mean, it is the mainspring of innovation. I'm Dave along with my co host, Paul Dillon. And you're watching the cubes. Coverage of Red Hat. Summer 2022. Raj Raj Masinga is here. He's hybrid and emerging Platforms lead at Accenture and Ghetto Greber. Who's red hats? Business group lead eccentric. Gentlemen, welcome to the Cube. Thanks for coming >>on. Thank you. >>Thank you, Raj. We saw in the keynote up there today with Stephanie. She's coming on tomorrow. Rockstar Stephanie. Cheers. Also a Boston sports fan, and I have to work at it, but you can talk about the history with red hat. How long have you guys been at this? And give us a journey update. >>Well, first of all, thanks for having us here. Um, yes, we are big fans of Red Hat and especially Stephanie. I get to I had the pleasure of working with a very closely, um, our relationship with Red Hat goes many, many years, decades I think. And but Paul, come here will tell you that. You know, we've been focused a lot with the formation of our new business unit in Cloud. First around, migrating to the public cloud. But now, as we focus more and more around how our clients begin to operate in the public cloud in the cloud ecosystem hybrid is coming much more into focus. And Red Hat is very much a key client of a key partner of us. So we go way back. But this is all about us doubling down and increasing our partnership and deepening it with them. >>So, uh, you talked today about hybrid Cloud is everything. And it seems like a couple of years ago there was focuses more on moving to the public cloud and getting off of private infrastructure. Has there been a change in the ways in which customers are thinking, are they gonna be hanging onto their private infrastructure longer, perhaps, than was expected a couple of years ago? >>I think the first of all, it's a very different industry by industry. If you look at retail or consumer goods, I think there's a big movement in terms of percentages of workloads that are getting moved onto public cloud. If you look at industries like banking or utilities or government, more regular financial services, more regulated industries. I think we are finding a much larger percentage of their workloads because of regulatory reasons and security reasons, etcetera. Our need to remain either on premise or in private cloud. So I think it very much depends on the industry. But regardless the hype, you know, especially with the movement to edge now hybrid is going to be, you know, permeating everything. So I think by industry depends. But but the edges driving a whole new flywheel. >>You know, we started the Cuban 2010, so the cloud was, you know, modern cloud. Anyway, it was like, say, four years in into it and at the time, to your point Raj Financial Services, there was an evil word. No way we're ever going to the cloud. No, that's changed, obviously. But then, when the financial crisis hit, >>you >>had so initially it was a lot of tyre kicking experimentation. When the financial crisis hit, you had a lot of CFO saying, Okay, let's shift Capex to Apex and so that was sort of a bridge. And then after we came out, it was like this spate of innovation. And then we saw that during the pandemic, where cloud migration was a high priority and or it was the lifeline. And now it sounds like customers are kind of rethinking to your earlier conversation. What is cloud? It's that operating model. So I wonder if you could sort of Can you confirm that's kind of the journey that customers are taking? Where are they today? What does it mean? There? You know the the operating model. What do they consider cloud? >>Um, you actually, you see it? It's like it's really try forward to the cloud. Uh, but where it was in the beginning, If it doesn't hype about Public Cloud, they become more and more aware that it's hybrid because they have to bring the legacy system and process into the cloud as well. And it takes more time that actually they have fought before. So it's like there was a process of learning and also like in the steps moving forward to the operating model because they also understand I cannot operate a cloud like I was operating in the classical way like my old data centre and everything. It needs all the capabilities it needs, all the skills and especially if you go in a hybrid world. And it's a hybrid operation between the classic traditional but also the new ways of how you operate into a cloud. And you really see also the financial services. Now, uh, we had, uh I mean watch presented at keynote. We had a client in Germany. He made a decision, a very traditional financial services clients providing the service to chairman saving banks. And they did this decision and I would say, if you have spoken to them 10 years ago, they will not go into the cloud. But now they went to the cloud via private cloud, and now they got the confidence about how to operate in it. And now they move forward into a public cloud. But from a private cloud into the public cloud. Today, after security, they have up Skilling on skills and people and they understand the process and what's really required and needed in order to have such an environment. >>Generally, what's the strategy with regard to modernisation organisations? More building like an abstraction layer? Uh, with microservices and then connecting to the cloud. Or are they actually rewriting applications to make them cloud native? What are you What are you advising clients from a strategy standpoint, and I know it depends, but, you know, it's >>a It's a great question. I think the genesis to that strategy is how they view infrastructure, Right? So you know, everyone is, has this kind of, I don't know that this is almost mythical opinion out there with cloud. You don't need to worry about your infrastructure. All the providers will worry about it, and you just need to move it there. But the opposite is true. It's really critical what your infrastructure strategy is as you move to the cloud, because depending on what workload you have, you know it can be on any one of the continuum that you described. So the first thing is, where do you want to house your workload? Is the question and that will drive. How what do you want to do with your application? Whether you want to just maintain it the way it is, Do you want to simply modernise it, keeping where it is, or do you want to completely risk in it or even eliminated. So so I think the entire basically the answer to your question around. Do we? What do we do with the application? Is fundamentally driven by what is your infrastructure strategy and what that workload needs to do for you. >>So I know you want to jump in, but I got to follow up. You're saying hardware matters because we heard Paul Corvino today talking about this hardware renaissance. I'm actually I just ran a power panel called. Does hardware still matter? You're saying it matters? >>Yeah. And and it doesn't. And infrastructure doesn't always. I mean, now that you can do infrastructure as code, right? I mean, I was at the Del summit last time and read That is a huge partner of Dell now, right? Which, you know, uh, was much more, uh, partnered with VM ware. But I think the whole ecosystem is opening up, and even the hardware providers are looking at this in a much more nimble way. But yes, it's very much part of the conversation. They haven't gone away. >>During your keynote. You outlined sort of your strategy. Going forward is called cloud first. Yes. What does cloud first mean? >>Well, um, we we want to make sure that when we talk about transformation of business with our clients, So extension always goes with the idea of an industry lens of solving a specific problem for a client. What is the business problem we solve? And increasingly, what we want to message and drive to our clients is if you're thinking about, regardless of what the business is technologies absolutely critical to whatever transformation you're doing and when. When you look at technology, you have to think cloud first because that's where all the innovation is happening. That's where all the, um um, investments are being driven. Whether it's an I mean, it's a software vendor, but it's a hardware vendor with its uh, so you have to think cloud first when you think about transforming your business. >>Uh, what is How does modernisation play into that? You know, a lot of vendors are throwing a lot of resources that the modernisation market VM ware, Tanzania and IBM and such, uh, how interesting our customers really a Modernising legacy applications >>hugely right, because fundamentally, I think everything is now driven by our experiences. What we now are used to in terms of, uh, interfacing with applications are interfacing with function sets or interfacing with technology. So there is a lot of inherent, um, legacy technology that doesn't have that experience. So when you think about transforming, you have to come at it from an experience point of view. And when you think in those terms modernisation or even rebuilding the same, even if it's the same function set, uh, re skinning it and modernisation is critical for the purposes of engagement. >>What's the number one challenge that customers that you're working with face in terms of modernisation? Is it trying to figure out like Rogers sort of laying out the portfolio? What do I do with it? Do I modernise it? Do I retire it? Do I let it just die on the vine? What's their number one challenge? >>Uh, mainly it depends also on the industry, but it's, uh, I would say, for the highly regulated, certainly regulations. They always have an own interpretation of the regulations. Regulation means for them, but normally it's not really what they understand. But I think this is more and more coming to Annie's and more people understand what it really means, but it's also what we see a lot. They think first about technology, but not what kind of business problem they want, Uh, and they want to solve. So it's like, instead of having a technology neutral discussion is really do want to achieve, um, to have really start on this side and then having this discussion away, which, obviously it's one of the key, even because they start to the cloud even without having a strategy without having a vision. If you have a clear vision, if you have a clear strategy, you know where you want to go, and then you can make your business case. You can make you architecture and then you decide on technology. And then, of course, on this journey, all the things about security compliance coming to the plane, Yeah, and I think I think that's the easiest approach. But clients struggle to understand. Of course, I mean, the technology is changing rapidly. Even new products and release cycle new life cycles, the complexity of all the tools hardware we mentioned before network is changing new working coming up. It's really hard to keep pace or keep up with the base of the technology and what's happening even for us. And then you understand the complexity and bring this complexity back to simplicity, but not without losing. We have this also keynote the efficiency and, uh, flexibility for an engineer, because that's what he needs >>to your clients. Have the skill sets to do all that such a self serving question to you guys. But but no, do they? I mean, there's a skills shortage. There's a a battle for talent. So how are they >>dealing? I mean, it's obvious the battle for talent is here. I mean, everybody is looking for the best talent, and if we need, if you need a full stack. Engineer, for example, is very hard to get a full stack engineer on your ground. You call really cloud native. So you have to up skill people to re skilled people. There's also a change coming into it and the changes not to forget. So it's what we say most time. The technology is an easy part, but the change change the organisation, change up skilled organisation. That's the hard part because you need to change from from one mindset to another, and we know from the from the past. What change? People are not open to change in general, so we need to change the mindset. >>I wanna go back to Hybrid Cloud because we have Dani from Red Hat was on earlier and he said, Edges really redefining the definition of hybrid cloud. It's it's more complex architecture, and it's changing the nature of how we think about hybrid Cloud. Are you seeing that with your customers? Are they changing their thinking about what hybrid means in that context? >>Yeah, completely. You know, I was I was We did a bunch of, uh, research recently, and I had I just wanted to make sure I called this. I mean, there's a flexible report that came out that says 80% of all enterprises now are on hybrid 89% multi cloud redheaded. A report that said 80% of our businesses are expected to, um, uh, increase their use of open source. Right, So So, yes, hybrid is everywhere. Edge is driving it, but there's a There's another critical element to that movement. The complexity of our clients. Estates are increasing because whether it's hybrid or whether it's edge or whatever, they are now. You know, given if you're a C i or a C T o. Your estate is really complex now. So one of the things that we now need to do is how do we simplify that? So, you know, we think and we've been talking with red hat about this. We need to come up with a clean, you know, we keep calling it, you know, single pane of glass for a enterprise that allows them to look at their estate in a way that allows them to then simply make some innovative decisions across the entire state. So, yes, edges driving hybrid. But the key thing that we now need to overcome is how do we manage that complexity? >>We have new term. Uh, I call it Super Cloud, but the session is a better word. Medic cloud. That's gonna what I think of that century. I think of deep industry expertise. Of course, we have that, but with the partnership from redhead, it's a very it's horizontal in the sense that it can go anywhere. So how do you guys work in in terms of within Accenture plugging into your deep industry expertise? And how does that horizontal redhead >>fit. That's a really good question. So, you know, one of the things, you know. First of all, we came out with a announcement today about our expanded relationship with Red Hat. One of the key elements in that announcement is how we are looking and bringing in red Hat into our industry business motions. So we actually have decided to pick a certain number of industries. You know, financial services is one. Telco is another. We are thinking about utilities in Europe. Public health is a is another one that we are looking at. And as we come up with our offerings, you heard me and Stephanie talk about joint offerings earlier on the keynote. Um, these offerings are industry offerings, but in those offerings we have embedded and we are, they're powered by redhead technology. Um, that allows these industry solutions to drive innovation through their technology. Um, yes. Red hat can be, for the most part, a horizontal cross industry, you know, technology. But you have to really bring them into specific industrial solutions because of the way we go to market. And I think Red hat brings innovation, uh, in a way that these industries haven't seen before. >>So I mean, how do you stay out of their way? Because they have a services operation that they're trying to grow. And that's your business as well. So where the lines of demarcation >>back to your question? I I don't I don't think there is a limiting opportunity. Read? Had, you know Stephanie Me, Paul, we're all talking about How do we collectively increase both our armies? You know, I I Yes, there might be occasional overlaps in the trenches, but when you look at the bigger picture, it is not a problem at all. >>I wouldn't think so. I mean, the way you're describing Rogers exactly the way it should work. You lead with the business, figure out the business problem, how you're gonna solve that. The technology will take care of itself. Technologies come and they go. And you want to use modern technologies, obviously. But if you don't get the business piece right, forget no technology is gonna save you >>exactly, right. And and the complexities of what the businesses today are facing is getting more and more difficult. And I think actually, technologies like red hat, you know, they're the whole concept of open source, I think is very creative around driving innovations from the market. I >>want to ask you that because Paul Kermie is you know, the storey was sort of an homage to open source. How much do customers really care about open source >>customers care about innovation and and anything that drives innovation to their business, whether it's whether it comes from technology, whether it comes from crowdsourcing, whether it comes from, you know, uh, marketing doesn't matter. I think when you look at the key hunger for innovation and how open source drives innovation, it becomes part of the business conversation. And, uh and I think that's been one of the mantras that Paul has had from day one about how this is such a great platform for innovation. And I think that's >>something customers asked for. They say we must develop this using open source platforms and tool sets. >>Um, it depends. I think I think there are some technology CEO s R c T O s that are much more religious about what? Their technologies that needs to be there are others that are that are much more business oriented. Um, so yes, there are. You know, if it's more in telecom field, I think telecom or some of the more, uh, technology driven fields, they will ask for open source. In others, they we bring, bring it through as part of offering. >>Here's the nuance that I see and you mentioned Paul Cormier. Accenture, especially. I mean, you look at your ascendancy as a company, you for years would take known processes and codify them in software. And you made, you know, a lot of great innovations doing that. And people who made a lot of >>money >>today, this new normal, he calls it. I call it the new abnormal. You don't know what's around the corner. You have to build flexibility into your business, and that is something that open source enables. Uh, so that's sort of this, Really Not really. We don't want to speak about it too much. Business resiliency and flexibility is that that is the new normal. I don't see how you can do it without without open sources expertise. >>I completely agree that I and I think, um, it's actually an asset. So you know, in some ways, selfishly, by having open source in a solution stack some of the innovation gets them much more democratised, right? So? So it can come from a much broader sweet. So the load is not only an extension to come up with all the innovation we can, we can actually come up with a more democratised way of bringing that innovation in. So I think that's that's >>great. And it doesn't always go back to the community. I mean, Amazon built a $70 billion business on open source, but not all right, guys. Thanks so much for coming. Thank you very much for having a pleasure. All right, keep it right there. This is Dave Volonte for Paul Dillon. The Cubes. Continuous coverage of Red Hat Summit 2022 from the seaport in Boston. We'll be right back. >>Mm mm.

>>To the Seaport in Boston, Massachusetts, everybody's buzzing. The Bruins are playing tonight. They tied it up. The Celtics tied it up last night. We're excited. We don't talk about the red Sox. Red Sox are getting struggles, but you know, we have good distractions. Paul goer is here. He's the president and chief executive officer at red hat and also a Boston fan of great to see, of course, you too. >>Nice to see you guys, you know, it's been a, it's been a while. >><laugh> yeah, we saw you, you know, online and virtually for a couple of years there, but, uh, you know, we've been doing red hat summit for a long, long time. Yeah, of course we were talking earlier. It's just much more intimate, kind of a VIP event, a few more suit jackets here. You know, I got my tie on, so I don't get too much grief. I usually get grief when I wear a tie of red hat summit, but it's a different format this year. Compressed keynotes. Your keynote was great. The new normal, sometimes we call it the new abnormal <laugh>, uh, but you know, how do you feel? >>I, I, I, I feel great. First of all, you know, combination today, virtual audience in, in house audience here today. I think we're gonna see a lot of that in the future. I mean, we designed the event around that and I, I think it, I think it played pretty well. Kudos, kudos to our team. You're right. It's, it's, it's a bit more intimate even the way it was set up, but those are the conversations we like having with our customers and our partners, much more partner centric, uh, as well right now, as well. >>You know, we were talking about, you know, hybrid cloud. It was kind of, you know, it was a good marketing term. And, but now it's, it's, it's become the real thing. I've said many times the, the definition of cloud is changing. It's expanding it's no, the cloud is no longer this remote set of services, you know, somewhere up in the cloud, it's on prem connecting to a cloud across clouds, out to the edge and you need capabilities that work everywhere. And that's what red hat did. The market's just swimming toward you. >>Yeah. I mean, you look at it, you know, I was, uh, you know, if you look at it, you know, the clouds are powerful unto themselves, right? The clouds are powerful unto themselves. They're all different. Right? And that that's, I mean, hardware vendors were, were similar, but different, same thing. You need that connective tissue across, across the whole thing. I mean, as I said, in my keynote today, I remember talking to some of our CIOs and customers 10 years ago and they said, we're going 90% of our apps tomorrow to one cloud. And we knew that wasn't practical because of course the clouds are built from Linux. So we knew it was underneath the hood and, and what's happened. It's taken some time, but as they started to get into that, they started to see, well, maybe one cloud's more suited for one application than the other, these apps. You may have to keep on premise, but you know, what really exploded at the, the, the hybrid thing, the edge. Now they're putting things at the edge, the GM announcement tell you, I know you're gonna talk to Francis. Yeah, yeah. Later. I mean, that's, that's a mini data center in, in every cloud, but that's still under the purview of the CIO, you know? So, so, so that's what hybrid's all about is tying all those pieces together, cuz it got more powerful, but it also more complex. >>You mentioned being the connective tissue, but we don't hear as much talk about multi-cloud seems to me, as we used to this conference has been all about hybrid cloud. You don't really talk about multi-cloud. How important is that to the red hat strategy, being that consistent layer? >>It's probably my mistake or our mistake because multi's more prevalent and more important than just hybrid alone. I mean, hybrid hybrid started from on-premise to one part to any one particular cloud. That was the, the first thought of hybrid. But as I said, as, as, as um, some of the cloud providers became so big, um, every, every CIO I talked to, whether they know whether they know it or not most do are in a multi environment for a whole bunch of reasons, right. You know, one cloud provider might be better in a different part of the world. And another one cloud provider might have a better service than another. Some just don't like to be stuck to one it's it's really hybrid multi. We should, we should train ourselves to every time we say hybrid, say multi, because that's really, that's really what it is. It, I think that happened overnight with, with Microsoft, you know, with Microsoft they've, they've, they've really grown over the last few years, so has Amazon for that matter. But Microsoft really coming up is what really made it a, a high, a multi world. >>Microsoft's remarkable what, what they're doing. But I, I, I have a different thinking on this. I, I heard Chuck Whitten last week at, at the Dell conference he used, he said used the phrase a multicloud, uh, by default versus multi-cloud by design. And I thought that was pretty interesting because I've said that multi-cloud is largely multi-vendor, you know? And so hybrid has implications, right? We, we bring and a shesh came up with a new term today. Metacloud I use Supercloud I like Metacloud better because something's happening, Paul. It feels like there's this layer abstraction layer that the underlying complexity is hidden. Think about OpenShift. Yeah. I could buy, I could get OpenShift for free. Yeah. I mean, I could, and I could cobble together and stitch together at 13, 15 dozens of different services and replicate, but I don't, I don't want that complexity. I want you to hide that complexity. I want, I'd rather spend money on your R and D than my engineering. So something's changing. It feels like >>You buy that. I totally buy that. I mean, you know, I, I, I'm gonna try to not make this sound like a marketing thing because it's not, not fair enough. Right. I mean, I'm engineer at heart, you know that, so, >>Okay. >>I really look to what we're trying to do is we're building a hybrid multi cloud. I mean that we, I look at us as a cloud provider spanning the hybrid multi all the way out to the edge world, but we don't have the data centers in the back. Like the cloud providers do in and by that is you're seeing our products being consumed more like cloud services because that's what our customers are demanding. Our, our products now can be bought out of the various marketplaces, et cetera. You're seeing different business models from us. So, uh, you're seeing, uh, committed spend, for example, like the cloud providers where a customer will buy so much up front and sort of just work it down. You're seeing different models on how they're consumed, consumption, based pricing. These, these are all things that came from the cloud providers and customers buying like that. >>They now want that across their entire environment. They don't wanna buy differently on premise or in one cloud and they don't wanna develop differently. They don't wanna operate differently. They don't wanna have to secure it differently. Security's the biggest thing with, with our, with our customers, because hybrid's powerful, but you no longer have the, you know, your security per perimeter, no longer the walls of your data center. You know, you're, you're responsible as a CIO. You're responsible for every app. Yeah. No matter where it's running, if that's the break in point, you're responsible for that. So that's why we've done things like, you know, we cried stack rocks. We've, we've built it into the container Kubernetes platform that spans those various footprints because you no longer can just do perimeter security because the perimeter is, is very, very, very large right now >>Diffuse. One of the thing on the multi-cloud hyper skills, I, I, red hat's never been defensive about public cloud. You, I think you look at the a hundred billion dollars a year in CapEx spend that's a gift to the industry. Not only the entire it industry, but, but the financial services companies and healthcare companies, they can build their own hybrid clouds. Metacloud super clouds taking advantage of that, but they still need that connective tissue. And that's where >>We products come in. We welcome our customers to go to, to the public cloud. Um, uh, look, it's it's. I said a long time ago, we said a long time it was gonna be a hybrid. Well, I should have said multi anybody said hybrid, then it's gonna be a hybrid world. It is. And it doesn't matter if it's a 20, 80, 80, 20, 40, 60, 60, 40. It's not gonna be a hundred percent anywhere. Yeah. And, and so in that, in that definition, it's a hybrid multi world. >>I wanna change the tune a little bit because I've been covering IBM for 40 years and seen a lot of acquisitions and see how they work. And usually it follows the same path. There's a commitment to leaving the acquire company alone. And then over time that fades, the company just becomes absorbed. Same thing with red hat. It seems like they're very much committed to, to, to leaving you alone. At least they said that upon the acquisition, have they followed through on that promise? >>I have to tell you IBM has followed through on every commitment they've made, made to us. I mean, I, I owe it, I owe a lot of it to Arvin. Um, he was the architect of the deal, right. Um, we've known each other for a long time. Um, he's a great guy. Um, he, uh, he, he believes in it. It's not, he's not just doing it that way because he thinks, um, something bad will happen if he doesn't, he's doing it that way. Cuz he believes in that our ecosystem is what made us. I mean, I mean, even here it's about the partners in the ecosystem. If you look at what made REL people think what made red hat as a company was support, right. Support's really important. Small piece of the value proposition life cycle supports certainly their life cycle a 10 year life cycle just came out of a, a, a customer conference asking about the life cycle and could we extend it to 15 years? You know? Um, the ecosystem is probably the most important part of, of, of, of the, of the overall value proposition. And Arvin knows in IBM knows that, you know, we have to be neutral to be able to do everything the same for all of our ecosystem partners. Some that are IBM's competitors, even. So, >>So we were noticing this morning, I mean, aside from a brief mention of power PC and the IBM logo during, at one point, there was no mention of IBM during the keynote sessions this morning. Is that intentional? Or is that just >>No, no, it it's, it's not intentional. I mean, I think that's part of, we have our strategy to drive and we're, we're driving our, our strategy. We, we, we IBM great partner. We look at them as a partner just as we do our, our many other partners and we won't, you know, we wouldn't, we wouldn't do something with our products, um, for I with IBM that we wouldn't offer to our, our entire ecosystem. >>But there is a difference now, right? I don't know these numbers. Exactly. You would know though, but, but pre 2019 acquisition red hat was just, I think north of 3 billion in revenue growing at maybe 12% a year. Something like that, AR I mean, we hear on the earnings calls, 21% growth. I think he's publicly said you're north of 5 billion or now I don't know how much of that consulting gets thrown in. IBM likes to, you know, IBM math, but still it's a much bigger business. And, and I wonder if you could share with us, obviously you can't dig into the numbers, but have you hired more people? I would imagine. I mean, sure. Like what's been different from that standpoint in terms of the accelerant to your >>Business. Yeah. We've been on the same hiring cycle percentage wise as, as we, we always were. I mean, I think the best way to characterize the relationship and where they've helped is, um, Arvin, Arvin will say, IBM can be opinionated on red hat, but not the other way around <laugh>. So, so what that, what that means is they had a lot of, they had, they had a container based Linux platform. Yeah, right, right. They, they had all their, they were their way of moving to the cloud was that when we came in, they actually stopped that. And they standardized on OpenShift across all of their products. We're now the vehicle that brings the blue software products to the hybrid cloud. We are that vehicle that does it. So I think that's, that's how, that's how they, they look about it. I mean, I know, I mean in IBM consulting, I know, I know they have a great relationship with Microsoft of course. >>Right. And so, so that's, that's how to really look at it. They they're opinionated on us where we not the other way around, but that, but they're a great partner. And even if we're at two separate companies, we'd do be doing all the same things we're doing with them. Now, what they do do for us can do for us is they open a lot of doors in many cases. I mean, IBM's been around for over a hundred years. So in many cases, they're in, in, in the C-suite, we, we may be in the C suite, but we may be one layer down, one, two layers down or something. They, they can, they help us get access. And I think that's been a, a part of the growth as well as is them talking into their, into, into their >>Constituents. Their consulting's one of the FA if not the fastest growing part of their business. So that's kind of the tip of the spear for application modernization, but enough on IBM you said something in your keynote. That was really interesting to me. You said, you, you, you didn't use the word hardware Renaissance, but that my interpretation was you're expecting the next, you know, several years to be a hardware Renaissance. We, we certainly have done relationships with arm. You mentioned Nvidia and Intel. Of course, you've had relationships with Intel for a long time. And we're seeing just the spate of new hardware developments, you know, does hardware matter? I'll ask you, >>Oh, oh, I mean the edge, as I said, you're gonna see hardware innovation out in the edge, software innovation as well. You know, the interesting part about the edge is that, you know, obviously remade red hat. What we did with REL was we did a lot of engineering work to make every hardware architecture when, when it was, when, when the world was just standalone servers, we made every hardware architecture just work out of the box. Right? And we did that in such, because with an open source development model. So embedded in our psyche, in our development processes is working upstream, bringing it downstream 10 years, support all of that kind of thing. So we lit up all that hardware. Now we go out to the edge, it's a whole new, different set of hardware innovation out at the edge. We know how to do that. >>We know how to, we know how to make hardware, innovation safe for the customer. And so we're bringing full circle and you have containers embedded in, in Linux and REL right now as well. So we're actually with the edge, bringing it all full circle back to what we've been doing for 20 plus years. Um, on, on the hardware side, even as a big part of the world, goes to containers and hybrid in, in multi-cloud. So that's why we're so excited about, about, about the edge, you know, opportunity here. That's, that's a big part of where hybrid's going. >>And when you guys talk about edge, I mean, I, I know a lot of companies will talk about edge in the context of your retail location. Okay. That's fine. That's cool. That's edge or telco that that's edge. But when you talk about, um, an in vehicle operating system, right. You know, that's to me the far edge, and that's where it gets really interesting, massive volumes, different architectures, both hardware and software. And a lot of the data may stay. Maybe it doesn't even get persisted. May maybe some comes back to the club, but that's a new >>Ballgame. Well, think about it, right? I mean, you, if you listen, I think you, right. My talk this morning, how many changes are made in the Linux kernel? Right? You're running in a car now, right? From a safety perspective. You wanna update that? I mean, look, Francis talked about it. You'll talk to Francis later as well. I mean, you know, how many, how many in, in your iPhone world Francis talked about this this morning, you know, they can, they can bring you a whole new world with software updates, the same in the car, but you have to do it in such a way that you still stay with the safety protocols. You're able to back things out, things like that. So it's open source, but getting raw upstream, open source and managing itself yourself, I just, I'm sorry. It takes a lot of experience to be able to be able to do those kinds of things. So it's secure, that's insecure. And that's what that's, what's exciting about it. You look at E the telco world look where the telco world came from in the telco world. It was a hardware stack from the hardware firmware operating system, every service, whether it was 9 1, 1 or 4, 1, 1 was its own stack. Yep. In the 4g, 3g, >>4g >>Virtualized. Now, now it's all software. Yeah. Now it's all software all the way out to the cell tower. So now, so, so now you see vendors out there, right? As an application, as a container based application, running out, running in the base of a cell tower, >>Cell tower is gonna be a little mini data >>Center. Yeah, exactly. Because we're in our time here asking quickly, because you've been at red hat a long time. You, you, you, uh, architected a lot of the reason they're successful is, is your responsibility. A lot of companies have tried to duplicate the red hat model, the, the service and support model. Nobody has succeeded. Do you think anybody ever will or will red hat continue to be a unicorn in that respect? >>No, I, I, I think, I think it will. I think open source is making it into all different parts of technology. Now I have to tell you the, the reason why we were able to do it is we stayed. We stayed true to our roots. We made a decision a long time ago that we weren't gonna put a line, say everything below the line was open and above the line was closed. Sometimes it's hard sometimes to get a differentiation with the competition, it can be hard, but we've stayed true to that. And I, to this day, I think that's the thing that's made us is never a confusion on if it's open or not. So that forces us to build our business models around that as well. But >>Do you have a differentiated strategy? Talk about that. What's your what's your differentiation >>Are, are, well, I mean, with the cloud, a differentiation is that common cloud platform across I differentiate strategy from an open source perspective is to, to sort make open source consumable. And, and it's even more important now because as Linux Linux is the base of everything, there's not enough skills out there. So even, even a container platform like open source op like OpenShift, could you build your own? Certainly. Could you keep it updated? Could you keep it updated without breaking all the applications on top? Do you have an ecosystem around it? It's all of those things. It was, it was the support, the, the, the hardening the 10 year to predictability the ecosystem. That was, that was, that is the secret. I mean, we even put the secret out as open. >>Yeah, <laugh> right. Free, like a puppy, as they say. All right, Paul, thanks so much for coming back in the cubes. Great to see you face to face. Nice to see you guys get it. All right. Keep it right there. Dave Valante for Paul Gill, you're watching the cubes coverage of red hat summit, 2022 from Boston. Be right back.

>>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.

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Welcome back here to AWS reinvent 2019. Great show going on here in Las Vegas, where the Sands were live here on the Cube. Once again, covering it from wall to wall will be here until late tomorrow afternoon. David John Walls were doing by Joined by David. She coaches who is the vice president of product management for hybrid idea Century Lake. Good to see you, You guys and Brandon sweetie, who's the SPP of worldwide cloud sales at Veum With you be with you. This is gonna be a New England sports segment actually surrounded by ruin. Celtics, >>ESPN in Vegas, >>I remind you, the Washington Nationals are the reigning world. Serious shit. Wait a moment. Wait. Shark forever. A moment in time I got stuff. Let's talk about your relationship between via wearing set free like And what brings you here? A WSB offering. You're putting you guys that run on AWS. >>Maybe Maggie jumping and jumping. So look VM wear a long time player in the infrastructure space. Obviously incredible relationship with AWS. Customers want to transform their operations. They want to move to the cloud way have Vienna, where Claude, on a video B s. We continue to take tremendous ground helping customers build and build more agile infrastructure. Make that happen, Van. Where was built on our partners. Right centrally great partner MSP. And we think about helping customers achieve their business outcomes. Key partners like centrally make it happen. You've been a long term partner and done a lot of great things with us. >>Yeah, and really what? What Central Lincoln VM Where have done? I mean, really, we sort of created the manage private cloud market in the early days of managing the Empire solutions for customers, but really were and where we differentiate in other working with GM wear on AWS is really with elements of our network or the ability to take those kinds of solutions and make sure that they're connected to the right networks and that they're tied in and integrated with the customer's existing enterprise and where they want to go as they start to distribute the workload more widely. Because we run that network, we see a lot of the Internet traffic. We see a lot of threat patterns. We see a lot of things emerged with our cyber security capabilities and manage service is. So we add value there. And because of that history with BM wear and in sort of creating that hosted private cloud environment, there's There's a lot of complexity, friendliness inside of our service offer, where we can manage the inn where we can manage it in a traditional model that is cloud verified. And then you could manage it as it starts to move on to the AWS platform. Because, as we all know, and as even you know, Andy has referenced in different points, there's a just about every kind of workload can go to eight of us. But there are still certain things that can't quite go there. And building a hybrid solution basically puts customers in a position to innovate is what a hybrid solution is all about. >>That kind of moves the needle on some of those harder to move working in the M, where is such an obvious place to start? So you try to preserve that existing customer of'em, where customer experience but at the same time you want to bring the cloud experience. So how How is that evolving? >>Yes, it's a couple things, right? So l Tingley customers, they all want to move to the cloud for all the reasons we want security, agility, governance, et cetera. Right, but fundamentally need help. And so partners, like essentially help figure out which workloads are cloud ready, right? And figure that out and then to you, get to know the customer. Really well, begin the relationships that you have, right, and you can help them figure out which workloads am I gonna move right? And then that leads into more relationships on How do I set up d r. Right? How do I offer other service is through eight of us against those work clothes. >>There's a lot of things where being a manage service's provider for a V M were based platform or being. Amanda's service is provided for an AWS platform. There's a lot of things that you have in common, right? First and foremost is that ability toe run your operations securely. You've got to be secure. You know, you need to be able to maintain that bond of trust you need to be auditable. Your your your operations model needs to be something that transparent to the customer. You need to not just be about migrating workloads to the new and exciting environment, but also helping to transform it and take advantage of whether it's a V M where feature tool or next generation eight of us feature it's will. It's not just my great lift and shift, but then helped to transform what that that downstream, long term platform could do. You certainly want Teoh be in a posture where you're building a sense of intimacy with the customer. You're learning their acronyms. You're learning their business processes. You're building up that bond of trust where you can really be flexible with that customer. That's where the MSP community can also come in, because there's a lot of creative things we can do commercially. Contracting wise binding service's together into broader solutions and service level agreements that can go and give the customer something that they could just get by going teach individual technology platform under themselves >>and their ways >>where the service provider community really chips in. >>I think you're right and we think about helping Dr customers success manage service providers because of those engine relationship with customers. We've had tremendous success of moving those workloads, driving consumption of the service and really driving better business outcomes based on those relationships you have. >>So let's talk about workloads, guys. Course. Remember Paul Maritz when he was running the M word? He said Eddie Eddie Workload. Any application called it a device. He called it a software mainframe and Christian marketing people struck that from the parlance. But that's essentially what's happened pretty much run anything on somewhere. I heard Andy Jassy Kino talking about people helping people get off on mainframes. And so I feel like he's building the cloud mainframe. Any work less? But what kind of workloads are moving today? It's not. Obviously, he acknowledged, some of the hard core stuff's not gonna move. He didn't specify, but it's a lot of that hard core database ol TV transit transaction, high risk stuff. But what is moving today? Where do you see that going? >>Don't talk about some customers. >>Yeah, >>so a lot of joint customers we have that. I think you fall into that category. In fact, tomorrow on Thursday, we're actually leading a panel discussion that really dives into some customers. Success on the AWS platform that Central Lincoln are managed service is practice has been able to help them achieve what's interesting about that We have. We have an example from the public sector. We have an example from manufacturing and from from food and beverage example from the transportation industry and airlines. What's really interesting is that in all those use cases that will be diagramming out tomorrow, where VM Where's part of all of them, right? And sometimes it's because I am. Where is a critical part of their existing infrastructure? And so we're trying to be able to do is design, you know, sort of systems of innovation, systems of engagement that they were running inside of an AWS or broadly distributed AWS architecture. But it still needs network integration, security and activity back to the crown jewels and what's kept in a lot of those workloads that already running on the BM where platform So that's a lot of ways. See that a good deal with regards to your moving your sort of innovative workloads, your engagement workload, some of your digital experience, platforms you were working with an airline that wants to start building up a series of initiatives where they want to be able to sell vacation packages and and be very creative in how they market deliver those pulling through airline sails along the way. They're gonna be designing those digital initiatives in AWS, but they need access to flight flight information, schedule information, logistics information that they keep inside of there there. Bm where environment in the centralized data center. And so they're starting to look at workloads like that. We started to look at the N word cloud on eight of us being whereas it a zit in and of itself as a workload moving up to eight of us. There's a range of these solutions that we're starting to see, but a lot of it is still there, and he had the graphic up. There were still, in the very early days of clouded option. I still see a lot of work loads that are moving AWS theater in that system of engagement. How can I digitally engaged with my customers better? That's where a lot of the innovation is going on, and that's what a lot of the workload that are running in launching our >>I mean, we're seeing tremendous momentum and ultimately take any workload, wailed, moving to the cloud right and do it in an efficient and speedy path. And we've got custom moving thousands of workloads, right? They may decide over time to re factor them, but first and foremost, they could move them. They relocate them to the cloud. They can save a lot of costs. Out of that, they can use the exact same interface or pane of glass in terms. How they manage those work clothes, whether they're on Kramer, off Prem. It gives them tremendous agility. And if they decide over time, they have to re factor some workloads, which can be quite costly. They have that option, but there's no reason they shouldn't move. Every single worker today >>is their eyes, their disadvantage at all. If if you're left with ex workloads that have to stay behind, as opposed to someone who's coming up and getting up and running totally on the cloud and they're enjoying all those efficiencies and capabilities, are you a little bit of a disadvantage because you have to keep some legacy things lingering behind, or how do you eventually close that gap to enjoy the benefits of new technologies. Yeah, >>there's a sort of an old saying that, you know, if you're if you're if you're an enterprise, you know, that means you've had to make a lot of decisions along the way, right? And so presumably those decisions added value. It's your enterprise, or else she wouldn't be in enterprise. So it really comes out, too. Yeah, to those systems of records of those legacy systems way talk about legacy systems >>on Lian I t. Is the word legacy. I know it's a positive. United is the word legacy. A majority of >>your legacy is what the value you built up a lot of that, whether it's airline flight data or scheduling, best practices are critical. Crown jewels kind of data systems are really important. It really comes down to it. You're on enterprise and you're competing against somebody that is born in the cloud. How well integrated is everything. And are you able to take advantage of and pace layer your innovation strategy so that you can work on the cloud where it makes sense. You can still take advantage of all the data and intelligence you build up about your customers >>so talking earlier, You guys, it seems like you guys do you see that? That cloud is ultimately the destination of all these workloads. But, you know, Pac thinking about PacBell Singer, he talked about the laws of physics, the laws of economics and the laws of the land so that he makes the case for the hybrid >>Murphy's Law. >>Yeah, so that makes the case for the hybrid world. And it seems like Amazon. To a certain extent, it's capitulating on that, and it seems like we got a long way to go. So it's almost like the cloud model will go to your data wherever it iss. You guys, I think, helped facilitate that. How do you look at that? >>Yes. I mean, part of that answer is how much data centers are becoming sort of an antiquated model right there. There there is a need for computing and storage in a variety of different locations. Right, And there's that we've been sort of going through these cycles back and forth of you use the term software mainframe and the on the Palmer. It's kind, a model of the original mainframe decentralizing out the client server now centralizing again to the cloud as we see it starting to swing back on the other direction for towards devices that are a lot smarter. Processors that are, you know, finally tuned for whatever Internet of things use case that they're being designed for being able to put business logic a whole lot closer to those devices. The data. So I think that is what one of things that I think that said that one of the BM wears. A couple of years ago, data centers were becoming centers of data. And how are you able to go and work with those centers of data? First off, link them all together, networking lies, secure them all together and then manage them consistently. I think that's one of the things I am has been really great about that sort of control playing data plane separation inside your product design that makes that a whole lot more feet. >>I mean, it is a multi cloud, and it's a hybrid cloud world, and we want to give customers of flexibility and choice to move their workloads wherever they need, right based on different decisions, geographic implications, et cetera, security regimens and mean fundamentally. That's where we give customers a tremendous, tremendous amount of flexibility. >>And bringing the edge complicates >>edge, data center or cloud. >>It's so maybe it's not a swing back, you know, because it really has been a pendulum swing, mainframe, decentralized swing back to the cloud. It feels like it's now this ubiquitous push everywhere. >>Pendulum stops. >>Yeah, >>because there's an equal gravitational pull between the power of both locals >>and compute explodes everywhere. You have storage everywhere. So bring me my question of governance, governance, security in the edicts of the organization. You touched on that. So that becomes another challenge. How do you see that playing out what kind of roles you play solving that problem >>on the idea of data governance? Governance? Yeah. I mean the best way to think about our. In our opinion, the best way to think about data governance is that is really with abstraction. Layers and being ableto have a model driven approach to what you're deploying out into the cloud, and you can go all in with the data model that exists in the attraction layers in the date and the model driven architecture that you can build inside things like AWS cloud formations or inside things like answerable and chef and been puppet, their model, different ways of understanding what your application known state should be on. That's the foundational principle of understanding what your workloads are and how you can actually deliver governance over them. Once you've modelled it on and you then know how to deploy it against a variety different platforms, then you're just a matter of keeping track of what you've modelled, where you've deployed it and inventorying those number of instances and how they scale and how healthy there that certainly, from a workload standpoint, I think governance discipline that you need in terms of the actual data itself. Data governance on where data is getting stored There's a lot of innovation here at the show floor. In terms of software to find storage and storage abstractions, the embers got a great software to find storage capability called the San. We're working with a number of different partners within the core of our network, starting to treat storage as sort of a new kind of virtualized network function, using things like sifts and NFS and I scuzzy as V n F that you can run inside the network we want. We have had an announcement here earlier in the week about our central bank's network storage offer. We're actually starting to make storage and the data policy that allows you to control words replicated and where it's stored. Just part of the network service that you can add is a value add >>or even the metadata get the fastest path to get to it if I need to. If I prefer not to move it, you're starting to see you're talking about multiplied this multi cloud world. It seems like the connections between those clouds are gonna be dictated by that metadata and the intelligence tow. You know what the right path is, >>And I think we want to provide the flexibility to figure out where that data needs to reside. Cross cloud on, Prem off from, and you can just hear from the conversation, David, level of intimacy some of our partners have with customers to work through those decisions. Right, if you're gonna move those workloads effectively and efficiently, is where we get a lot of value for our joint customers. >>I mean, she's pretty fundamental to this notion of digital transformation that's ultimately what we've been talking about. Digital transformation is all about data putting data at the core, being able to access that, get insights from it and monetize, not directly, but understand how data affects the monetization of your business. That's what your customers >>and I think we >>wantto. Besides, I think we want to simplify how you want to spend more time looking up. Your applications are looking down your infrastructure, right? Based on all the jury, are drivers across the different business needs. And again, if we can figure out how to simplify that infrastructure, then people could spend more time on the applications because that's how they drive differentiation in the market, right? And so let's simplify infrastructure, put it where it needs to be. But we're going to give you time back to drive innovation and focus on differentiating yourself. >>You know, it's interesting on the topic of digital transformation reindeer. So right, sort of an interesting little pattern that plays out for those of us that have been in the service of writer community for a little while that a lot of the digital transformation success stories that you see that really get a lot of attention around the public cloud like eight of us. The big major moves into going all in on the public cloud tend to come from companies that went all in on the service provider model 10 years ago, the ones that adopted the idea. I'm just gonna have somebody do this non differentiating thing for me so that I can focus on innovation, are then in a better position to go start moving to the cloud as opposed to companies that have been downward focused on their infrastructure. Building up skill sets, building up knowledge base, building up career, path of people that, actually we're thinking about the technology itself as part of their job description have had a hard time letting go. It sort of the first step of trusting the service provider to do it for you lead you to that second step of being able to just leverage and go all in on the public lab. >>And customers need that help, right? And that's where if we can help activate moving those workloads more quickly, we provide that ability, put more focus on innovation to Dr Outcomes. >>I know you're talking about legacy a little bit ago and that the negative connotation, I think. Tom Brady, Don't you think I wanna run number seven? I haven't had a home smiling Would always do it back with more. We continue our coverage here. Live with the cube, where a w s rivet 2019.

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Welcome back to Boston, Everybody. Money >> belong here with my co host, a student of John >> Kerry's. Also here today You watching the Cuban leader and on the ground tech coverage. This is day one of active fio 19 data driven content Conference hashtag data driven 19 red cara minuses. Here is the senior vice president of fantasy Sports A draftkings Greg. Thanks for coming on. What a cool title. >> Yeah, it's It's, you know, I was joking with my wife. Anytime you could be working fantasy sports, it's a great place to be. Everybody's a little bit jealous. >> So the formula is easy, right? Offer big giant prizes and everybody comes And that's all there is suing. Anybody can come >> in. I just have the dream job right now. >> So hugely competitive market. You guys, you become the >> leader. We were in the radio. Check out your websites. I mean, take us through the draft kings and your ascendancy. How you got here? >> So, you know, company started in 2012 initially around sort of the major big American sports on DH. Then really a CZ. We started scale that we saw there was a huge consumer interest in the product players that would come on. We're very, very, very sticky. Um, and we've just been kind of, you know, pushing, pushing on growing that using these. So the initial founders are three former analyst. So come on. It's always been sort of a very analytically driven company. So they looked at what we were dealing with, and it was we had L TVs that were way higher than our cracks. So let's keep marketing and growing and growing and growing and finding out ways to offer a better product. So, over 2015 we did a major marketing blitz, blew up the company Absolutely huge. Um, and since then we've been just constantly innovating, adding new sports, adding new features on DH, adding ways toe on the product. And then even more recently, just about a year ago, we expanded also into online sports betting over New Jersey has that's become a legal product across the U. S. So it's been a great time to be at the company a lot of fun. >> What what was your first sport was like Amazon started in books and then, you know, scaled out what was your first sport. So it's actually the first sport was >> baseball because of the time that they actually launched. So is the middle of April. Sporting calendar is a little bit thin. Right then, so is it was baseball to start, and then once football season started, that's really when things take on >> 2015 is when you started the marketing blitz and I remember just here in the ads and it was just intense, like a while. This company's going for it. So you sort of took >> all the chips I went >> all in and it worked. Yeah, I mean, it's part of the, you know, the lifeblood of the company. It's We're a company that ends up being taking risks, but we take calculated risks. So at any given point, you sort of say, like, Hey, what is the what is the range of outcomes over here? We're not playing for second place. We want to be a market leader, so you have to take risks in order, be a market leader. So let's take calculated risks. Let's make sure we're not being insane, but you know we did the math. We figured out what? This is A This is a worthwhile shot. We pushed him for it. Andi really took off from their love to bet on >> sure things. Yeah, well, Greg, we know the people that play the fantasy for it feel that data is what differentiates whether they're going to live in, you know, winner lose. Talk to us a little bit about the data journey inside your business And how that helped differentiate draftkings in the market. Yes. So we think Death draftkings >> is one of the most analytically based companies in the, you know, definitely in the market, but also into sort of like General Cos right now we use our analytics platform to inform pretty much everything we dio on. Go to your point. You're joking. You know, it seems like fantasy sports is easy throughout some giant prizes there, and everything will take care of itself. You know, running a fantasy sports car company. If you throw out a contest that's too big, you lose a ton of money. There's a lot of asymmetric risk in the business where if we're right, we make a little bit more. But if we're wrong. We lose a ton very, very, very fast. So our ability to be very, very sound analytically is what allows us to sort of pushed the envelope and grow, grow, grow but not, you know, lose our heads along the way. You know, some of the fun of that is really, you know, when we first ran, I think one of the most game changing contest we ran was actually back in October of 2014. It was the very first millionaire maker contest I could still remember. It was Week five of the 2020 14 NFL season where we said, Hey, this it's crazy. We need crazy things that happen in order for it to work. But if we're on a $20 contest to enter with $1,000,000 top prize and 2,000,000 of total prizes, it could go viral, go absolutely crazy. And if it loses, here's how it'll losing. Here's how much will hurt us. It's a worthwhile risk. Let's go for it. So that sort of energy of, you know, doing discipline analysis and constantly sort of them. Taking the risk on the back of it is what allowed us to build >> up the brand value that you would have got out of that was sort of worth that risk in part anyway. And you wouldn't have to hurt presumably. >> Exactly. We knew our downside. As long as you know your downside, you're normally in a pretty good spot to take those risks. >> So where do you >> see this All going mean? So the company has grown. You're at this kind of critical mass now, Like we said, highly competitive, you know, knock down. You know, if you take your eye off the ball. So how do you guys keep this going? >> So we have a huge challenge ahead of us over the next couple of years, as sports betting becomes legal across the US, we need to make sure that we are one of the top competitors in that market. Sports betting in the US, we expect to be an absolutely enormous market. It will probably be significantly larger than the fantasy sports market in terms of absolute revenue and even, you know, on order of magnitude more competitive. So we need to be executing each step along the way a CZ markets open up. We need to be able to get into getting two market very, very fast. And that means our tech team needs to be working feverishly to make sure that we can hit the requirements that each legislator and each regulator puts on market entry in their state. We didn't mean making sure we're constantly figuring out what are the product elements that are absolutely critical for our for our users. Is it Maura around the live betting experiences that around the different markets that you offer? It's around pricing. And how do we find these things, these different lovers and told them to make sure that we're putting out a great product for users. And if we do that and throw a great product after users were pretty sure we can make you want >> to be one stop shopping presumably, right? I mean, all sports, right? But But then you've got these niche sports betting. I mean eggs, invest. Example. I could think of this horse racing. You know where it is alive. It's gonna video. It's got commentators on the ground that you know the business really well. Is >> that Is that the strategy to go sort of horizontal and so be a one stop shop or you >> gonna sort of pick your spots? What is the day to tell you? >> You know, I think we're constantly talking about it. One of the things that allowed our fantasy sports business to grow so fast was going a little bit more horizontal. So we offered Gulf in Mass at a time period when the primary competitors and the space vandal did not. On DH, we built that product into one of our largest sports. It's, you know, right up there with MLB in terms of the actual size that that comes in a Z have gone also horizontal, we pulled in other places, like NASCAR. Mm, a great sports that people are interested in. It gets more users into our platform. And honestly, if uses are interested in a product, we don't want them to have to go elsewhere. We want to be able to have the offerings that any sort of, you know, critical mass type environment is going toe is gonna have >> Well, it's that experience, right? Well, I like to shop in Amazon. You do, too, because I >> trusted. And it's the same user experience. So, Greg, one of things >> I'm hearing from you is something that everybody tries for, but it's really challenging that speed. How do you react that fast and move the company into new markets and new offerings and keep innovating? You know, culturally technology wise, you know, How does Draftkings do that? You know, I think a za company, you know, from really every single person that we recruit in higher We've been actually execution Aly disciplined throughout the company's history. It's It's something that our founders did a great job of instilling in the culture right at the gates. I mean, we've tried to foster all the way along the way, which is all the best strategies of the world. They're going to fail if you can't execute well and every single person down the company knows that. And we try to, you know, enable each person to be as autonomous as possible in their ability to execute their their portion of the business that allows us to move really, really, really fast. You know, we disseminate that responsibility quickly, and each leader and sort of each person knows what they have to do to execute. There's a high degree of accountability behind that, you know, I'd like to say there's some. There's some magic recipe that's, um, secret sauce, but it's a lot of just great people doing great work everyday. Well, Greg, you know it's any your competitors that they look at, You know, Boston's been been doing pretty well in Draftkings era, you know, for the last few years. ES o Boston's been a great market for us. We've expanded Conover here on DH. The sports teams have been fantastic, although the Bruins it was a little bit sad about Game seven over there, but it happens. >> So his m o be the flagship news that no, I wouldn't say >> that MLB was first, primarily just of the time of the year when we launched. NFL is always going to go, are not always going to be, but for the for the foreseeable future is the dominant US sport on will remain the dominant US for >> no reason. I mean, kids there watch MLB anymore. Maybe the maybe the playoffs and the games. It was a game. I think I'm some Father's day was like almost five hours long, you know, gets called. You can come in and out. But you know what some of the trends. You see soccer. Is that growing NFL? Obviously huge. Do you see so niche sports like lax coming on. >> So, uh, you know, starting point NFL has been huge. We actually launched a new product Ah, little over a year ago called Showdown, which allowed you start to do fantasy for a single game as opposed to the combination of games that's taken off fantastically because that's tapping into more of the I'm going to sit down and watch this game, and I would love to have a fantasy team on that on this game. That's really expanded the audience like that. That >> was genius because, look, if you're >> out of the running, it doesn't matter because I'm weak. On top of that N b A and NHL on fire. The embassy put out a great product is an actual sport league. You know, the Finals were great. You hate to see the injuries, but it was a great final. Siri's very competitive. The NHL Finals has been very, very competitive. Golf is growing phenomenally as a sport, way farm or interesting golf than I ever anticipated when I first started with the company and it's one of the most exciting things. When the Masters comes each year, every screen has turned to it and we see a huge player. Player number is kind of coming into that one. Beyond that, you know NASCAR. What's been interesting? NASCAR's been having a tough couple years, but the Truck series for us? We launched it this year and the trucks have been great. I don't know if you've watched NASCAR Trucks. They're wildly entertaining. Uh, you know, Emma, you got the big fighter. So every sport sort of has its moments. It's a matter of like picking those moments and figuring out how to make >> the most of them. Do you see boxing at all making a comeback? >> So we have thought about how to get boxing into a into a fantasy. We don't have it at the moment. We're putting a lot of thought into it, so we are actually seeing through. We've seen, you know, we've been in the M M A space and we've seen the growth out from there where that sports doing great and you look at places like Bela Tor. The Professional Fighters league is other leagues, and then boxing is the next step. There's a lot of interest there. I don't think they have the right products yet to be able to kind of engage with that extra way. So that's one of things we're working on. Also, you need a marquee fighter. You always need a marquee fighter. Kind of helped bring in the interest over on that side. So, um, be interesting to see with Taki on sort of the downside of his career. At this point on DH, Mayweather hasn't been fighting much. Will be interesting to see. Who's that next meeting with Adam. But >> I grew up in an era >> of Marquis fighters. What? They would fight, you know, they literally fight 6 70 times a year, you know, and you had used huge names on DSO, and then mm comes along and he's really hurt, >> but it feels like it's tryingto so to resuscitate. Yeah. I mean, I think these things could >> be a little bit cyclical. Like you get one Marquis fighter out there like so my wife, this Filipino. So I'm a huge backing out fan now way watch every fight. Even when we were living in remote locations that forces watching at weird hours. He's a type of athlete that could bring popularity of the sport. So if there was a major U. S. Fighter that gains that degree of sort of, you know that that degree of fame people will be into it, I think >> Do do do your analytics sort of have a probe into the activity at the at the fan level at the sports level, not just the fantasy level or the betting level? Is that a sort of ah ah predictor for you? Yet we >> see a lot of correlations between how many people play our sport are fantasy game, and how many people actually follow the underlying sport. Way can also see trends in terms of If I'm from Boston, I probably pick more patriots in my fantasy lineups than, uh, normal on DH. You can actually see that as people play different sports that you know, the number one Q. Be drafted in in Boston is almost always gonna be Tom Brady. And once you leave that you start seeing Aaron Rodgers pop up. Let's really, really fast. So you see these little micro trends where it's like you are still a sports fan of your local team in your local environment, but it manifest itself in the fantasy. >> So what you think that is? Do you think it's fan affinity >> or do you think it's just the sort of lack of knowledge out inside? You're sort of a circle of trust. >> I think it's probably a combination. I mean, I could say is, you know, following the Celtics in the mid to thousands, I knew the depth of the Celtics pension, how they would use their rotation better than anybody else, Probably better than anybody else in the coaches would probably disagree. But it's like I knew that James Posey was a huge value play on Saturday nights. I knew. I kind of with I feel the Eddie House nights. Uh, so, you know, on your local team, you probably know those players at the not the top top echelon All Stars, but the guy's right beneath. You know them a little bit better and probably more comfortable using >> what's your favorite sport. >> So my favorite sport, from a fantasy perspective, is I play all the basket. I play all football, played basketball just during play offs, and I played baseball. But baseball I'm strictly a fantasy player. I don't really follow the sport to play. I'm just playing fantasy. Okay, >> That's great. So, what do you think? The conference. Here. >> You have you Have you had any timeto interact? I know you were swamped after coming off the stage. >> You know, it looks like a great turnout over here. There's a lot of enthusiasm amongst them from people. I was a little bit late to the late to show up this morning, so I got a bit Swanson eager to go and be able to catch up a bit more. >> Okay, Well, Greg, thanks so much for coming on. The Cuba's great to have your every pleasure meeting you. >> All right, people. Right there. Still, when I >> was back with our next guest, John for it is also in the house. You wanted The Cube from active field data driven 19. Right back

>> Live from Miami Beach, Florida, It's theCUBE covering VeeamON 2019. Brought to you by Veeam. >> Welcome back to Miami, everybody. This is Dave Vellante with Peter Burris. We're here at day one at VeeamON 2019. This is CUBE's 3rd year of doing VeeamON. We started in New Orleans, it was a great show. Last year was Chicago, and here, Miami at the Fontainbleau hotel. Marc Crespi is here, he's the vice president of sales engineering for the Americas at ExaGrid Systems Cube. Hello Marc, good to see you again. >> Good to see you. >> Thanks for coming on. So, give us the update. What's happening with ExaGrid? You guys got new headquarters in Marlborough. Marlborough's happening these days, right? We got the new shopping spa, and the mayor's going crazy, so give us the update on ExaGrid. >> Yes, so we just moved into a beautiful new headquarters in Marlborough and share it with some great other companies. The company continues to grow rapidly, double digit growth year over year, one of the few companies in this category that's growing that quickly. So everything's great. >> What's driving the growth? >> Well, customers are looking to fix the economics of backup. They've been spending too much money on it for a lot of years, so they look at products now, they want them to be simple, easy to use, and very cost-effective and we drive that trend very hard. >> Yeah I mean that doesn't really describe- what you just described, simple, easy to use, and cost-effective really doesn't describe backup for the past 20 years. So what are you doing specifically to make it simple, cost-effective, and easy to use? >> Well, first of all, by working with companies like Veeam. Veeam is a very easy-to-use product, it's very intuitive and then our product integrates very well with it so the products work together very well and makes just a very simple solution. >> What do you see as other big trends in backup? showed a slide today, 15 billion dollars. A big chunk of that, maybe close to half of it was backup and recovery, there's all kind of other stuff: data management, analytics, etc, etc, etc. What do you see, obviously cloud, you talked about the big superpowers, what are the big trends that are driving your business and more importantly, your customers transformation? >> Well, customers are looking to reduce the amount of data that they actually have to move. So, incremental technology's a really big- themes of pioneer in that, obviously doing incremental backups and that saves time and effort, saves space, along with data deduplication, it really makes for cost-effective storage solution. >> Talk a little bit more about why you're growing, how you sort of uniquely compete in the marketplace with some of the big whales. >> Sure, so our most unique feature is our architecture, and it has both technical aspects and economic aspects. Because we're a scale-out architecture, meaning that with every capacity increase of your data, we're not just adding storage, we're adding CompuPower network memory, etc. so that we keep the backup times very, very, very low. That also makes for a very cost-effective architecture because what we've done is you can scale out pretty much infinitely and we've also eliminated the concept of the end of a life of products. So we never force our customers into mandatory refreshes so their economics are very predictable over a long period of time. >> What do you see as the biggest use cases today that are driving your business? I mean, obviously, backup and recovery, I talked earlier about some of these emerging data management, cloud obviously, is this big, Edge, you seeing much going on there. What are some of those workloads and use cases that you see? >> I think probably one of the biggest use cases these days is what I would call instant recoveries, meaning that rather than doing a traditional restore, which could take a long number of minutes to hours. Customers will actually run production workloads off of the backup target as a way to get users back productive more quickly than would've been done in the past. >> Yeah, and that's key because you see in RPO and RTO's sort of companies putting more and more pressure on the IT groups to shrink those times, presuming you're seeing that in conjunction with digital, digital business, digital transformation. You talked about architecture before. What about your architecture and maybe with your partnership with Veeam allows customers to shrink those RPO and RTO times? >> I think the other aspect of our architecture that's very unique is what we called adaptive deduplication. One of the things we looked at when we architected the product was deduplication is obviously a very effective technology, but what are potential cons. Things that would make it less effective in backup. And one of the things we realized was if you put deduplication in the middle of the backup window and due to deduplication while the backups are running, then you could interfere with the speed of disk. So we do something called adaptive deduplication which means that we allow the object from the backup software to land and then we deduplicate and replicate them in parallel, but we make sure that we're not throttling the backups. So, we provide disk speeds even though we use deduplication. >> Okay. So, that's an example of one of the things you're doing to sort of improve it. How about Veeam integration? Is there anything specific there that you're doing that we should know about? >> Well, part of it is because of adaptive deduplication and because we maintain complete copies of backups. We uniquely support instant Veeam recovery like no other vendor can. Furthermore, we run what's called the Veeam data remover which is actually Veeam technology runs inside of our appliance and sets up a optimized communication protocol with the Veeam software that allows us to do a number of great things. >> Wait, double click on on that. So, is it an efficient protocol or is there other sort of accelerators that you've got in there? >> The protocol is optimized, and then we do some other acceleration around how you do synthetic folds and things of that sort that are unique to the data mover. >> And you have news with Veeam this week, do you not? >> Yes, we do. We're announcing something called ExaGrid backup with Veemam and what it is in a nutshell is the ability for a customer to purchase both technologies from their preferred reseller by just ordering one part number. So it dramatically simplifies the acquisition of the two technologies and allows customers to simplify the buying process. >> So Veeam, I know, is all channel sales. How about you guys? How do you go to market? >> We also are, yes. >> So, talk more about your go-to market. What do you have? Like, an overlay sales force that it helps facilitate? You got partners? Maybe you can talk more about your ecosystem. >> Well, we have a worldwide sales force and our sales people, the people that do the selling, work directly with our partners, so we don't have a specialized channel workforce, but we have a specialized channel strategy, and our entire sales team is very well trained on the channel, how to work with the channel, and make them happy and successful. >> So, backup for a long time time was kind of an afterthought. It was non-differentiated. You just did what you needed to make sure the devices could be recovered. >> Yeah, you bolted it on. >> You bolted it on. >> Right. >> Increasingly, it's becoming recognized as a central capability to any digital business, because if your data goes away or your data's no longer available, your digital business is gone. >> Right. >> That suggests we're going to get a greater degree of differentiation in the types of devices, in the types of systems, etc, that are going to become part of a backup solution. First of all, do you agree with that? And then secondly, go back to the use cases, where do you guys see yourselves fitting into that increasingly federated backup capability? >> Well, I certainly do agree with it. I mean, it's always been a necessity, but now even with things like Ransomware and the cryptoviruses, and things of that sort, it's even more important than it's ever been. It's no longer just data loss, etc. So, we fit into that trend and we'll continue to fit into that trend by continuing to drive the economics through the floor. Customers want that level of protection, it's a little bit like insurance. You need the protection, but you don't want to pay a dollar more than you have to, right? So you want to put it on an economic diet, and the way our technology evolves, we come out with denser, faster systems at a lower cost per terabyte just about every year. And we'll continue to do that. >> So do you anticipate then that there's going to be specialized use cases or are you just going after taking costs out of the equation? >> It's not so specialized because it's very horizontal. Everybody does it and everybody backs up all their data. So, we don't specialize in any one area of the data center like database or anything of that sort. We go wherever the customer needs us to go inside their data center. >> It's in the data center, sorry David, it's in the data center. >> In the data center, we also have a cloud offering, we have partners that will offer disaster recovery as a service, so they'll have data centers that manage on behalf of the customers, and we also have an offering that goes into Amazon web services. And, shortly, we'll be coming out with one for Azure. >> And that is what? A software based offering that uses the cloud as a target? >> Correct, it's a virtual appliance that you can replicate into the cloud. >> All right. We don't have much time left tonight, we have a really important topic to cover, which is, we talked about last year, but I want to bring it up again, which is sports. >> Yup. Why don't we talk Boston sports, we could talk about Warriors. I got a question for you, but- >> I'll watch >> I asked you last year, and I think it was May, we were in Chicago, I said "Would you have traded Tom Brady?" At a time when the sentiment was, he was done. And you said "No way, absolutely not." You, Peter McKay, and Patrick Osmond all said emphatically no, you made the right call. So good job. >> Thank you. >> Your thoughts? >> Would never trade him. He can play until he's 100 for all I care. As long as he keeps performing at such a high level, why would you lose him? >> And then, of course, the Red Sox, 108 wins, that was an amazing gift that they gave us. So, I don't know if you're a baseball fan. >> I am. >> All right, I got to ask you, Peter. Are the Warriors the greatest basketball team in the history of basketball? >> Well, let's see... >> Brendan says yes. >> They are the best basketball team at a time of the most competitive NBA. Some of the rules have changed, but the athletes are better, they're more conditioned, they are more knowledgeable by how to play this game, and they are the best team in basketball without Kevin Durant and without Boogie Cousins. >> Yeah. >> So ... hard to argue. >> They're sweeping Portland without Durant which is pretty amazing. So Brendan, for years, has been trying to tell me that. You know, Brendan is our local basketball genius so, I don't know. >> Now, would the Warriors have beaten say a Bill Russell Celtics team with the Celtics- Bill Russell Celtics team rules? Maybe not. >> Yeah, I don't know. I would say I'm starting to come around to Brendan's way of thinking. But, Marc, we'll give you the last word here. VeeamON 2019, great venue here in Miami, very hip, hip company, hip venue, ExaGrid growing, double digit growth rate, so congratulations on that. Your final thoughts? >> Just great to be here, I always like coming to Veeam events, they're always very well attended, I get to meet a lot of customers and really enjoy it. >> Marc Crespi, thanks very much for coming to theCUBE. It's great to see you again. >> Thank you. >> All right, keep it right there everybody. Peter and I will be back with our next guest right after this short break. This is VeeamON 2019 and you're watching theCUBE.

>> live from Boston, Massachusetts. It's the queue covering your red. Have some twenty nineteen brought to you by bread hat >> and welcome back to the Red Hat Summit. We're live in the B, C, E C, the Boston Convention and Exposition Center, along with two metal men. I'm John Walls were joined by Stephanie Cheer us. Who is the vice president? GM and Red had Enterprise. Lennox? Yes. Good to see here. >> Nice to see you teach >> back in Boston, right back >> in Boston. Home turf. >> You feel at home here? I would give you a big day for you. Right. Relic comes out generally available now a big impact on the marketplaces. Talk about that baby that you've given birth through here today. >> Wear so excited and, you know, having put in all the time. Part of this is representing all the work the team has done and the communities have done. When you think about all the work that goes into a Lennox distribution, it is everybody. It's the communities, It's the partners. So we released the Red Hat Enterprise Lennox eight beta in November mid November. We've had forty thousand downloads of that beta since November. People who have provided feedback and comments, suggestions, all of that fed into what we've released today as the Red Hat Enterprise Lennox eight. General availability. So it's a big day, and part of it is we're just so proud of how we've done it and what we've done. And we've really redefined what are not the value of an operating system with Red Hat Enterprise on its eight >> Dannic students, even saying earlier. Excuse me still, but you're saying there's many years in the making, right? Twenty fourteen It was That was the last was when. Seven. >> That's right. It's been five years. >> And so Hobart Theatre, editor of Process That You went through especially, you know, through that beta stage of a little interested in that are a lot interested in that. In terms of of the changes that were still made at that time that once you heard from users and actually put it into practice, >> yes, so we one of the things that part of our subscription model is getting feedback from customers. It's critical for us and tow advocate for those asks upstream because, of course, everything we do is done upstream. So this is part of the way we build, I would say relate was quite different in the sense that I focus all the features and functions we put into it into two pockets. We wanted to make sure that it helped customers with all the changes that have happened in the industry, helped them run their business better. So things like, Is it hard to find Lenox skills? How did we build a Web console to make that easier? Is it hard to orchestrate a data center? We put in a new capability that's a rules based engine, as a software is a service offering in every rail subscription that takes all that we have learned in the market to how to run an efficient Lennox data center. And it sends that out an assassin offering toe every rail subscription owner right that helps them be more efficient. And then there's the whole set of features and functions we put in to help customers grow the business things like container tooling so they can take that one step into containers right from the operating system. Application streams pull in new versions, so I look at everything we've done. Is it relate, really focuses on running the business better, more efficiently and helping grow the business. It's combination of those two things, and the feedback has been great, right? The relic Beta was great. Some tweaks, some tuning. Some. I like how this is too hard. Take out the friction. That's what we were working on since November. >> Stephanie. It is fascinating to me because, you know, I remember last year Saturn with the right hat team. They talked about just that. The amount of change that goes in tow. Lennox, you know, talk about, you know, it's twenty one point six million lines of code. Over the last two years, a third of the code base has changed, and it's something that you know, since it's open source. There's a lot of visibility by the community has been coming for years, yet something you've been working on for five years. We know how much change there's been in the industry. You just talk a little bit about how you balance those dynamics of, you know, that the caves of released cycle. I understand there's going to be a very systematic approach going forward, as how releases are how right that looks at things >> and and one of the roles that we see that we play in the industry is sitting between all the innovation and the outlook work that's being done in the communities and the enterprise, customers who need to know that they're going to run this hardware and it's gonna work. They're going to run this application and it's going to work, and we serve to bridge that gap in between. We advocate for our customers upstream. We make sure that innovation has tried true and tested by the time it reaches them in rail and we sit in that bridge. So to your point, we're constantly getting input from customers about things that are critical to them, things like life cycle capabilities. Now in an upstream community, they probably don't care about a ten year life cycle. But if you're running it on the floor of a data center, they do and we bridge that gap, feeding that back and forth, and it is a bit of a balance. We need to make sure we're pulling in the next generation of things that are important. But we're also protecting what's important to accustom, earthy, enterprise level and honestly stew. It's a constant given take and a constant balance. But, you know, there are a few things that we hold on principle one, it will always be upstream first to it will always serve our customers. In the enterprise. We do it on their behalf. So you know, the beauty of open source is everyone can play in the three million communities that exist in all of that innovation, the challenges everyone can play. So now how do you take that and run your business on it? That's where we come in. So this is why it's so important in this subscription, we constantly get that input from customers. >> Yeah, absolutely way. When we look at this face in the cloud world, I'm kind of used tto running on the latest and greatest on platform. Takes care of it. And as we you know, customer state, they're living in that hybrid and multi cloud world, and we need to bridge from the old. Okay, I'm running in minus two because I haven't finished testing it yet. I want to make sure I've got the latest security one of Les trois and care of the latest features. So I need to be ableto balance both of those, and it's challenging. >> It is challenging and to your point balancing, that is, you know, we had focused on relate because we really wanted to change the >> value. >> Um, but now moving forward, What we've heard from customers is it's a real business advantage for them to know when they're going to get a new release so they can time it with their hardware updates and their eyes. V update. So, as you mentioned as we head into rally, much more predictable life cycle will have minor releases every six months, major releases every three years. And, you know, as an engineer, you always say what I want to have this and I want to have this and and then sometimes it can divert your schedule. What we've heard from our customers is No, no, no. My schedule is really important. I need to plan. I need to predict. So now we put the schedule first. Going forward will put in everything we can into that version and prioritize what we can. But schedule became very important customers. So, to your point, predictable life cycle is important in relative, >> so huge impact in the business that way, you're giving them stability and certainty and predictability. Let's talk about the economic impact, if you will, because you did a fascinating study. I DC did it for you about this global economic impact that's being realised by rail. And the figures are there beyond impressive. They're staggering in terms of positive economic contributions. Wouldn't talk about that a little bit. >> Yeah, absolutely. You know, when I when I think about what we all want to do every day, we all want to have impact. It's not always easy to measure impact. And so when we worked with I D. C. And we asked them to go off and do this study, it really was about measuring and economic impact in the world, and I was even flabbergasted at the numbers. But if you look at all the applications and the software that run on rent had Enterprise Lennox, collectively, it will touch ten trillion dollars of business revenue this year. That's amazing. I think partly partly that speaks to several things. It speaks to the importance of Lennox and the market and where it stands with respect to being running core business and mission critical work made what dollars in sense touch, as well as where the new applications are being written. That's the importance of Lennox. I think it's also an astounding statement to say Lennox is built around an ecosystem. It's built by communities, and when you start to make that self sustaining, that's the kind of impact that it can have. But it's incredible. >> Yeah, I loved we had one of the customers we had already was DBS Bank, and they talked about the financial industry on DH. You know, security and innovation and helping to become a technology company themselves. And it's not sitting in a silo. And they had insourced rather than outsourced, and its partnership with Red Hat that that helps enable a lot of that transformation for, you know, company that people don't necessarily think of, you know, banking as you know, that driver of technology innovation, >> right? And when they looked at when they looked at for customers, for customers who use it just is, you say, because they kind of are now technology companies. How do they look at the value of rail? Roughly, it was about a fifty fifty split between savings and productivity, which feeds into savings and growth right, new revenue being driven. So it really ties back to clinics being Yes, what we run and how do we maximize efficiency for it? And yet how do we grow our business? So it's it's It's absolutely, I mean the use of the software that's being run on Red Hot enterprise Lennox will will reach economic benefits for those customers of a trillion dollars a year. That's huge. That's huge. So it's great. >> Yeah, So out of that ten trillion, I don't know if you could put it in the buckets if you can, but just or maybe the most impressive buckets, if you will, is it through efficiency is the truth time say, visit through better higher production? Uh, I mean, where are those big chunk gains being realised? >> So they provided a breakout of productivity and cost savings in the center and then revenue growth. And honestly, it's a fifty fifty split between savings and growth, and I think that's a huge statement, right about not only what can be done to do cost savings, because that starts to change the way you know everyone starts to think of. A commodity is no once I get into a commodity, I'm going to just save money, and I'm going to pull every cent out. But when its strategic, that's when you grow. And so to me, seeing a fifty percent split pea to and what I can save with it and what I can grow with it. The operating system is anything but a commodity, right? It's a complete strategic decision for a company. So it was great, >> right? So Stephanie would talk. Talk about economic impact. Something I always loved to talk about at this show is what's happening with jobs. Six year we've been doing this show in the early years. It was that Lennox operating model is just becoming pervasive. You look at what happened in the cloud, lookit what topping and software to find, whether it be networking or other piece of the environment. If you understand Lennox, chances are those operating models or what they're using in your that time to get up to speed on those new skills is going to be smaller, can talk about what you're seeing kind of thie ecosystem of jobs, not just, you know, red hat. You know the customers using it, but but even beyond. >> Yeah, so we see that. I mean that this study will show that but nine hundred thousand jobs are being driven by the rail ecosystem. That's massive. That's massive. And and while many of those companies air global, a lot of that is domestic. So I think that as we look at the skills group, that air moving forward and you look at even the operating system adoption and they're operating system adoption of Lennox and those skills customers right now are saying Lennox skills are hard to find. We're working to make it easier. But nine hundred thousand jobs, that's all. That's a lot of work being driven by this ecosystem alone. >> Well, you said jobs where you just talked about difficulty in some respects. What about educating the modern workforce or or an updated workforce? I mean, what kind of impact can you have on that? Or do you want tohave on that in terms of finding the right people in order to keep driving you forward? Because I think a lot of people share that concern is just coming up with that, that brain power, if you will, that that firepower to keep this innovative cycle to keep it rolling like it like it is. Where you going for that? How you doing that? >> You know, I think I think there's a couple. There's clearly things we can do in the product we added in something called Web Console. It's built off the upstream called cockpit, but it comes in and it is. You know, you can run your Lennox service now from your phone off of a Web portal, and it'Ll be shown in a demo tomorrow morning, which is is just the coolest toe Launch up your system Jets grade, and we worked very closely to make sure that the gooey and the feel and the way it was done with similar toe windows. Because many companies certainly have Windows installations, they have Lennox installations. The more we can make the most of the skills that customers have and be able to have that be cross compatible is really important, and clearly we have. The market has recognized the importance of developers not only as influencers but developed, but developing the next applications. What will come down the pipeline in? Let's face it, many customers, we're seeing all. I didn't know my developer was doing this, but they're coming in with real, you know, growth opportunities for the business. So we have really put in a play for developers. We have developer subscriptions that they can use. So a very focused effort with our team to reach out to the developers, make sure they have the tools they need, the capabilities they need. We've put in build a pod, man and scope eo right into the rail sub so that, you know, they can start to build their containers right from the OS. >> All right, So, Stephanie, we've talked a bunch about relate. And I know that Hunza session you're going to be in the keynote today. >> Yes. Give us >> a You know, a key nugget or two that, you know, it might be overlooked if if if you didn't shine a light on it, you know, love to get your take on what you're geeking out on when it comes to relate. >> Yeah, So I'm actually one of the things and and I know you'LL have a deep dive on this later. One of the things that I love about it is we have pulled in This relate launch is very much to me. A Portfolio launch Redhead is a portfolio company of enterprise software. It's not a product company. We're not just an OS company, although that's important. We're portfolio company. So what you'LL see in the relative announcement is really how it ties to the rest of the portfolio. Red Hat Enterprise Lennox Core OS As part of feeding into open shift, that's important. Having universal base image be the way we allow developers. We allow eyes ves to build containers that are ready to deliver that well experience on open shift Iran. Well, that's huge for us. Pulling in capabilities like management within sites, pulling that directly into every sub. Every rail. Six seven eight sub. Right to me, we've taken Rail eight is the first real step where we launch a product, but it's a portfolio launch. And, uh, and that's partly why it makes me so excited, right? I mean, being in relics like being being in all the products, that red hat, because where the foundation of it, that's what I hope people walk away feeling right that the OS is important and its core to the whole portfolio that red hat can deliver, >> but we look forward to the keynote tonight. Yes. You're gonna knock it out of the park as you always do. Thanks for joining us. And maybe if you have a little expertise on the side, give Brad Stevenson call Celtics coach. I think you could use a win right now. Every celtics on thin ice right now, but Red Hat very much Bruins once. All right? Okay. All right, >> I'll take it. >> It's a win, right, Stephanie? Thank you. Thanks, Joe. It's a pleasure to have you back with more for the redhead summit. You're watching the cue. >> How well

>> Live from Las Vegas, it's theCUBE, covering Dell Technologies World 2019. Brought to you by Dell Technologies and its ecosystem partners. >> Hey, welcome back to Las Vegas. Lisa Martin with Dave Vellante of theCUBE on our second day of wall-to-wall coverage of Dell Technologies World 2019, and we're welcoming one our guests back to theCUBE. We've got Rüya Barrett, VP of product marketing from the Data Protection Division. Rüya, it's great to have you back on the program. >> Great to be here, thank you for having us. >> And from Under Armor, a brand everybody knows, Nick Hennessy, Senior Manager, Compute and Storage. Nick, welcome, it's great to have you here. >> Great, thank you guys very much. >> So Rüya, we'll start with you. We've had, this is, you can hear all the energy behind us. And if you can hear dogs barking, by the way, that's normal. We've got some dogs next to our-- Lots of energy yesterday and today. Everything about data as this asset, and I think Michael said yesterday, that it's inexhaustible. You guys did an interesting recent survey with over 2,000 IT decision makers. With respect to data and getting their hands on it, what are some of the really interesting things that you've learned about that? >> Yeah, there were some really great takeaways. Great question. One, it's not a surprise to anyone, People have more data than ever to manage. There was over 586% growth in the last two years in terms of how much data on the average customers are managing. So that's a given, not a big surprise. One of the key things that we saw was that they value data. These people surveyed value data more than ever. So it was 96% value data more than they ever did, and 36% of them have already started monetizing data. So it's critical for accounts now, and one of the issues that they brought up for not being able to recover data, around data protection, was that if they can't recover data, they have new concerns now. Loss of opportunity, loss of bringing products to market, loss of competitive advantage, which are issues that we have never heard before because this is the third time we did the survey. We did it first in 2014, 2016, and we just did the 2018 survey. So those were some of the key really big takeaways for me from that survey that we did. >> So if they value it, they've got to protect it. >> Yeah. >> Alright, so Nick, Under Armour, a brand I mentioned everybody knows and wears. You guys have a great brand reputation. And you have some great brand ambassadors. I've got to mention Steph Curry. We have established Nick as a Lakers fan. And I have to point out, Dave, that you're wearing a Warriors colored tie today. Just got to say. >> I won't be if the Celtics make it to the finals though. >> But also Tom Brady's a brand ambassador. We've got Tommy boy covered, Lindsey Vonn. So you've got this great brand of reputation. How does Under Armour, to Rüya's point, value that data and leverage that data to keep and grow that brand reputation? >> Well, you know one of the things about data is, at Under Armour, we call the data is the new gold. So to us, it's very important, especially to our consumers, stuff that we're gathering at the retail stores, and kind of tracking all that stuff. So in order for us to protect that data, we're using Dell Technologies as sweeter products. And it's been working out great for us. >> So paint a picture, Nick, what are you protecting? What's the infrastructure look like, the applications, I know big SAP shop. But what's it look like, what are you protecting? >> So in terms of data, we're protecting over a thousand virtual machines, Two plus petabytes of data, everything in our five regional hubs. So it's quite a bit, it's quite a chore, especially for a small team like we have. >> So you mentioned data is the new gold. I have this idea that it's even more valuable than gold 'cause you can only use gold once. You can't spend it multiple places, data. And I think, correct me if I'm wrong, but Under Armour's ascendancy really coincided with strong technology ethos, very strong use of data, understanding of customers, and technology of sports clothing. So how are you using data to drive competitive advantage? >> Yeah, so very interesting. The brand and the culture is very infectious. So it's like, rah rah, let's go out and get it. That works into how we work IT in our everyday lives. So we kind of take that and kind of run with it. >> So what were you doing before you guys started working with Dell EMC? Talk to us about some of the challenges that you faced before you were using a different solution, so some of those opportunity costs that Rüya mentioned, in terms of if we can't monetize this, we're going to miss opportunities to identify new products our customers want, bring it to market. Walk us through your journey. >> Yeah, so I joined Under Armour about four years ago. And we really set the foundation with our three-year road map. Year one, build the foundation. It was really aligning what we were going to do, right, aligning with Dell Technologies, we're using all of your products. Year two was really architecting the future. And that's where things such as data protection really helped us out. We needed stuff that was easy to deploy, things that, for a small team to manage, that we don't have to think about it. We can sleep easy at night. It really aligned with our road map. >> So historically, data protection has been insurance. Rüya, you and I have talked about this for a long long time. Nobody likes to buy insurance, but you got to do it. Are you trying to move beyond that sort of one use case equation into new areas of value, whether it's compliance, whether it's data analytics. Are you able to use the corpus of data that you're protecting, and the management of that data in new ways? And if so, how? >> Yeah, in terms of the management for our small teams, we need something really easy. But security always comes to mind, so that's built into the product as well. But things moving to the cloud, scalability, things that we want to do in the future, we're really setting that up now. And us doing a huge storage refresh a couple months ago, we really flattened out, and we're using all brand new products. Now we're ready to scale the cloud. >> Rüya, you say that in the customer base, that people are trying to move beyond just straight back-up. >> Definitely. >> It's becoming increasingly new world, digital transformation, hybrid clouds. What are you seeing? >> Oh my god, yeah there's a ton of demand right now for customers to be able to leverage data, regardless of where it lives. So primary data, secondary data, tertiary copies, cloud data. How do you really start gaining business insights regardless of where data is? And how do you make sure that it's constantly recoverable under any circumstance. So one of the other things that we found in that study, again, is that there's new threats. So cyber recovery has become, and ransomware, and cyber recovery has become such a foundational consideration for customers. Being able to also spin up VMs regardless instantly. We just announced the X400 PowerProtect, which is very exciting and was part of today's announcement. It's all flash, and the reason it's all flash is because the use cases such as data reuse, app test and development, being able to test disaster recovery scenarios or cyber recovery scenarios real time, these are all critical use cases that you couldn't imagine doing years ago on your protection data. So we're really excited about both the PowerProtect announcement, as well as the Integrated Data Protection Appliance announcement. So you and I, Dave, have talked a lot about the Integrated Data Protection Appliance and simplicity and efficiency and breadth of coverage and cloud capabilities. Under Armour actually is a big proponent. They use cloud very prolifically, in terms of their IT environment. And IDPA really fit that need for them, in terms of being able to really drive costs out of their environment through efficiency, have that protection performance, just the foundational capabilities, yet still be able to offer some of those new innovation and the cloud capabilities, as well as automation. >> Alright, so we've heard from the marketing pro. Nick, now we got to hear from the customer. I heard simple, efficient, so how simple, how efficient, how do you measure these things? How does it compare with other products that you've looked at? >> Well, the product that we had before, we used Avamar Data Domain, and the problem that we had with it, it was decentralized. So we were managing a regional hub separately. So by refreshing, as we did, it got very simple. Now we have a centralized management. We were able to reduce 40 to 1 ratio. We're getting reductions, before we were getting 92 to 93%. Now we're getting 98, 99%. More importantly, for me, reporting. So able to produce those reports, we didn't have that before, so it's been really great. >> And how do those internal benefits that you talked about manifest out through the organization and really drive, like we talked about earlier, brand reputation or Under Armour being able to use that valuable data to identify new insights and act on the new product streams to delight, say, Tom Brady, for example. >> Well not only does it make-- >> You know he cares. (laughing) >> We certainly care about Tom Brady. >> I know! >> It makes my life a lot easier, right? So I'm able to take this data, it allows me to think, it allows the teams to be agile. Can you use that data to promote other projects, other ideas, things that we really want to do in the future to kind of push the brand even farther. >> When you guys meet privately, what kind of things, Nick, do you ask Rüya and her team at Dell EMC to do that will make your life easier? >> Quite honestly, the Dell team that we work with is wonderful. Really, we ask for a partner, someone that works with us, someone that understands us, understands our pain and is in there with us, so that we can really work on solutions together. >> Okay, obvious question, is that why you work with these guys? 'Cause of the strong partnership? Two part question, and what about the product? Is the product in your opinion, based on what you've evaluated, best of breed relative to other competitive products that are out there. >> Yeah, we did look at some other competitor products. We believe that it is best of breed. And that's why we chose to partner with Dell Technologies. >> So a lot of news yesterday and today, everything around multi-cloud. Customers are in this multi-cloud world for a variety of reasons. With the partnership that you've established with Dell Technologies and Rüya's group, what are some of the things that you've heard from Michael, from Pat, from John, Jeff, that really resonated with you that, ah, Dell Technologies is listening to customers like Under Armour and others as they're developing, helping you to really tackle this multi-cloud world with a lot of success. >> Yeah, so one of the things that was really exciting was part of the keynote yesterday with the SDDC. You can spin up a data center at the click of a button nowadays, and that resonates with us because it's going to make our lives really easy. We're going to be more agile. We can speed up and really take the brand farther. >> So you mentioned cloud before. I think Rüya said you've got multiple clouds. You have multiple clouds, is that right? >> We have a hybrid cloud infrastructure. >> So you've got multiple public clouds, is that correct? Obviously. >> Yes. >> You've got SAS, you've got on-prem stuff, and you try to make them all look the same, substantially similar from a control plan standpoint? >> We try. (laughs) >> It's a journey. >> Yes. >> I get that. But there's also the operating model. And I want to follow up with, are you enabling, whether it's DBAs or application owners, to do their own back-ups, do their own recoveries, do their own analytics, et cetera. Is that where you're headed, are you there today? Is it something that you don't want to do? Can you elaborate? >> That's the idea is to try and make everyone's life a lot easier. And being part of the Compute and Storage team, we're really stuck in the middle of all teams. Applications teams come to us. Sequel teams come to us, networking teams. So we really have a lot of responsibility on our plate. In order to make our lives simpler, we have to enable all these teams to do it themselves, and that's really where we're headed. >> Well, great stuff guys. Nick, Rüya, thank you so much for joining Dave and me on the program this afternoon. And go Warriors. >> Ahh. >> I said it. (laughs) >> For Dave Vellante, who again is wearing a Warriors colored tie. I'm Lisa Martin, you're watching theCUBE live from Las Vegas. Okay. >> I do. >> Alright. >> I like the Warriors. >> Alright, good, see and I mentioned Tom Brady-- >> I like them a lot better than the Lakers, sorry Nick. I can't get over that. >> I'm not sorry. I was saying, we're at VM (laughs). No, we're not at VM World, we're at Dell Technologies World. Oh my goodness, Lisa Martin for Dave Vellante, thanks for watching. (electronic music)