>> Narrator: Live from Boston, Massachusetts, it's theCUBE covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, Red Hat, and additional ecosystem support. (techno music) >> And we're back. I'm Stu Miniman joined by my co-host, John Troyer. Happy to welcome to the program two of the keynote speakers this morning, worked on some of the container activity, Kendall Nelson, who's a Upstream Developer Advocate with the OpenStack Foundation. >> Yep. >> And John Griffith, who's a Principal Engineer from NetApp, excuse me, through the SolidFire acquisition. Thank you so much both for joining. >> Kendall Nelson: Yeah. Thank you. >> John Griffith: Thanks for havin' us. >> Stu Miniman: So you see-- >> Yeah. >> When we have any slip-ups when we're live, we just run through it. >> Run through it. >> Kendall, you ever heard of something like that happening? >> Kendall Nelson: Yeah. Yeah. That might've happened this morning a little bit. (laughs) >> So, you know, let's start with the keynote this morning. I tell ya, we're pretty impressed with the demos. Sometimes the demo gods don't always live up to expectations. >> Kendall Nelson: Yeah. >> But maybe share with our audience just a little bit about kind of the goals, what you were looking to accomplish. >> Yeah. Sure. So basically what we set out to do was once the ironic nodes were spun up, we wanted to set up a standalone cinder service and use Docker Compose to do that so that we could do an example of creating a volume and then attaching it to a local instance and kind of showing the multiple backend capabilities of Cinder, so... >> Yeah, so the idea was to show how easy it is to deploy Cinder. Right? So and then plug that into that Kubernetes deployment using a flex volume plugin and-- >> Stu Miniman: Yeah. >> Voila. >> It was funny. I saw some comments on Twitter that were like, "Well, maybe we're showing Management that it's not, you know, a wizard that you just click, click, click-- >> John Griffith: Right. >> Kendall Nelson: Yeah. >> "And everything's done." There is some complexity here. You do want to have some people that know what they're doing 'cause things can break. >> Kendall Nelson: Yeah. >> I love that the container stuff was called ironic. The bare metal was ironic because-- >> Kendall Nelson: Yeah. >> Right. When you think OpenStack at first, it was like, "Oh. This is virtualized infrastructure." And therefore when containers first came out, it was like, "Wait. It's shifting. It's going away from virtualization." John, you've been on Cinder. You helped start Cinder. >> Right. >> So maybe you could give us a little bit about historical view as to where that came from and where it's goin'. Yeah. >> Yeah. It's kind of interesting, 'cause it... You're absolutely right. There was a point where, in the beginning, where virtualization was everything. Right? Ironic actually, I think it really started more of a means to an end to figure out a better way to deploy OpenStack. And then what happened was, as people started to realize, "Oh, hey. Wait." You know, "This whole bare metal thing and running these cloud services on bare metal and bare metal clouds, this is a really cool thing. There's a lot of merit here." So then it kind of grew and took on its own thing after that. So it's pretty cool. There's a lot of options, a lot of choices, a lot of different ways to run a cloud now, so... >> Kendall Nelson: Yeah. >> You want to comment on that Kendall, or... >> Oh, no. Just there are definitely tons of ways you can run a cloud and open infrastructure is really interesting and growing. >> That has been one thing that we've noticed here at the show. So my first summit, so it was really interesting to me as an outsider, right, trying to perceive the shape of OpenStack. Right? Here the message has actually been very clear. We're no longer having to have a one winner... You know, one-size-fits-all kind of cloud world. Like we had that fight a couple of years ago. It's clear there's going to be multiple clouds, multiple places, multiple form factors, and it was very nice people... An acknowledgement of the ecosystem, that there's a whole open source ecosystem of containers and of other open source projects that have grown up all around OpenStack, so... But I want to talk a little bit about the... And the fact that containers and Kubernetes and that app layer is actually... Doesn't concern itself with the infrastructure so much so actually is a great fit for sitting on top of or... And adjacent to OpenStack. Can you all talk a little bit about the perception here that you see with the end users and cloud builders that are here at the show and how are they starting to use containers. Do they understand the way these two things fit together? >> Yeah. I think that we had a lot of talks submitted that were focused on containers, and I was just standing outside the room trying to get into a Women of OpenStack event, and the number of people that came pouring out that were interested in the container stack was amazing. And I definitely think people are getting more into that and using it with OpenStack is a growing direction in the community. There are couple new projects that are growing that are containers-focused, like... One just came into the projects, OpenStack Helm. And that's a AT&T effort to use... I think it's Kubernetes with OpenStack. So yeah, tons. >> So yeah, it's interesting. I think the last couple of years there's been a huge uptick in the interest of containers, and not just in containers of course, but actually bringing those together with OpenStack and actually running containers on OpenStack as the infrastructure. 'Cause to your point, what everybody wants to see, basically, is commoditized, automated and generic infrastructure. Right? And OpenStack does a really good job of that. And as people start to kind of realize that OpenStack isn't as hard and scary as it used to be... You know, 'cause for a few years there it was pretty difficult and scary. It's gotten a lot better. So deployment, maintaining, stuff like that, it's not so bad, so it's actually a really good solution to build containers on. >> Well, in fact, I mean, OpenStack has that history, right? So you've been solving a lot of problems. Right now the container world, both on the docker side and Kubernetes as well, you're dealing with storage drivers-- >> John Griffith: Yeah. >> Networking overlays-- >> Right. >> Multi-tenancy security, all those things that previous generations of technology have had to solve. And in fact, I mean, you know, right now, I'd say storage and storage interfaces actually are one of the interesting challenges that docker and Kubernetes and all that level of containers and container orchestration and spacing... I mean, it seems like... Has OpenStack already solved, in some way, it's already solved some of these problems with things like Cinder? >> Abso... Yeah. >> John Troyer: And possibly is there an application to containers directly? >> Absolutely. I mean, I think the thing about all of this... And there's a number of us from the OpenStack community on the Cinder side as well as the networking side, too-- >> Yeah. >> Because that's another one of those problem spaces. That are actually taking active roles and participating in the Kubernetes communities and the docker communities to try and kind of help with solving the problems over on that side, right? And moving forward. The fact is is storage is, it's kind of boring, but it's hard. Everybody thinks-- >> John Troyer: It's not boring. >> Yeah. >> It's really awesomely hard. Yeah. >> Everybody thinks it's, "Oh, I'll just do my own." It's actually a hard thing to get right, and you learn a lot over the last seven years of OpenStack. >> Yeah. >> We've learned a lot in production, and I think there's a lot to be learned from what we've done and how things could be going forward with other projects and new technologies to kind of learn from those lessons and make 'em better, so... >> Yeah. >> In terms of multicloud, hybrid cloud world that we're seeing, right? What do you see as the role of OpenStack in that kind of a multicloud deployments now? >> OpenStack can be used in a lot of different ways. It can be on top of containers or in containers. You can orchestrate containers with OpenStack. That's like the... Depending on the use case, you can plug and play a lot of different parts of it. On all the projects, we're trying to move to standalone sort of services, so that you can use them more easily with other technologies. >> Well, and part of your demo this morning, you were pulling out of a containerized repo somehow. So is that kind of a path forward for the mainline OpenStack core? >> So personally, I think it would be a pretty cool way to go forward, right? It would make things a lot easier, a lot simpler. And kind of to your point about hybrid cloud, the thing that's interesting is people have been talking about hybrid cloud for a long time. What's most interesting these days though is containers and things like Kubernetes and stuff, they're actually making hybrid cloud something that's really feasible and possible, right? Because now, if I'm running on a cloud provider, whether it's OpenStack, Amazon, Google, DigitalOcean, it doesn't matter anymore, right? Because all of that stuff in my app is encapsulated in the container. So hybrid cloud might actually become a reality, right? The one thing that's missing still (John Troyer laughs) is data, right? (Kendall Nelson laughs) Data gravity and that whole thing. So if we can figure that out, we've actually got somethin', I think. >> Interesting comment. You know, hybrid cloud a reality. I mean, we know the public cloud here, it's real. >> Yeah. >> With the Kubernetes piece, doesn't that kind of pull together some... Really enable some of that hybrid strategy for OpenStack, which I felt like two or three years ago it was like, "No, no, no. Don't do public cloud. >> John Griffith: Yeah. >> "It's expensive and (laughter) hard or something. "And yeah, infrastructure's easy and free, right?" (laughter) Wait, no. I think I missed that somewhere. (laughter) But yeah, it feels like you're right at the space that enables some of those hybrid and multicloud capabilities. >> Well, and the thing that's interesting is if you look at things like Swarm and Kubernetes and stuff like that, right? One of the first things that they all build are cloud providers, whether OpenStack, AWS, they're all in there, right? So for Swarm, it's pretty awesome. I did a demo about a year ago of using Amazon and using OpenStack, right? And running the exact same workloads the exact same way with the exact same tools, all from Docker machine and Swarm. It was fantastic, and now you can do that with Kubernetes. I mean, now that's just... There's nothing impressive. It's just normal, right? (Kendall Nelson laughs) That's what you do. (laughs) >> I love the demos this morning because they actually were, they were CLI. They were command-line driven, right? >> Kendall Nelson: Yeah. >> I felt at some conferences, you see kind of wizards and GUIs and things like that, but here they-- >> Yeah. >> They blew up the terminal and you were typing. It looked like you were actually typing. >> Kendall Nelson: Oh, yeah. (laughter) >> John Griffith: She was. >> And I actually like the other demo that went on this morning too, where they... The interop demo, right? >> Mm-hmm. >> John Troyer: They spun up 15 different OpenStack clouds-- >> Yeah. >> From different providers on the fly, right there, and then hooked up a CockroachDB, a huge cluster with all of them, right? >> Kendall Nelson: Yeah. >> Can you maybe talk... I just described it, but can you maybe talk a little bit about... That seemed actually super cool and surprising that that would happen that... You could script all that that it could real-time on stage. >> Yeah. I don't know if you, like, noticed, but after our little flub-up (laughs) some of the people during the interop challenge, they would raise their hand like, "Oh, yeah. I'm ready." And then there were some people that didn't raise their hands. Like, I'm sure things went wrong (John Troyer laughs) and with other people, too. So it was kind of interesting to see that it's really happening. There are people succeeding and not quite gettin' there and it definitely is all on the fly, for sure. >> Well, we talked yesterday to CTO Red Hat, and he was talking same thing. No, it's simpler, but you're still making a complicated distributed computing system. >> Kendall Nelson: Oh, definitely. >> Right? There are a lot of... This is not a... There are a lot of moving parts here. >> Kendall Nelson: Yeah. >> Yeah. >> Well, it's funny, 'cause I've been around for a while, right? So I remember what it was like to actually build these things on your own. (laughs) Right? And this is way better, (laughter) so-- >> So it gets your seal of approval? We have reached a point of-- >> Yeah. >> Of usability and maintainability? >> Yeah, and it's just going to keep gettin' better, right? You know, like the interop challenge, the thing that's awesome there is, so they use Ansible, and they talk to 20 different clouds and-- >> Kendall Nelson: Yeah. >> And it works. I mean, it's awesome. It's great. >> Kendall Nelson: Yeah. >> So I guess I'm hearing containers didn't kill OpenStack, as a matter of fact, it might enable the next generation-- >> Kendall Nelson: Yeah. >> Of what's going on, so-- >> John Griffith: Yeah. >> How about serverless? When do we get to see that in here? I actually was lookin' real quick. There's a Functions as a Service session that somebody's doing, but any commentary as to where that fits into OpenStack? >> Go ahead. (laughs) >> So I'm kind of mixed on the serverless stuff, especially in a... In a public cloud, I get it, 'cause then I just call it somebody else's server, right? >> Stu Miniman: Yeah. >> In a private context, it's something that I haven't really quite wrapped my head around yet. I think it's going to happen. I mean, there's no doubt about it. >> Kendall Nelson: Yeah. >> I just don't know exactly what that looks like for me. I'm more interested right now in figuring out how to do awesome storage in things like Kubernetes and stuff like that, and then once we get past that, then I'll start thinking about serverless. >> Yeah. >> Yeah. >> 'Cause where I guess I see is... At like an IoT edge use case where I'm leveraging a container architecture that's serverless driven, that's where-- >> Yeah. >> It kind of fits, and sometimes that seems to be an extension of the public cloud, rather than... To the edge of the public cloud rather than the data center driven-- >> John Griffith: Yeah. >> But yeah. >> Well, that's kind of interesting, actually, because in that context, I do have some experience with some folks that are deploying that model now, and what they're doing is they're doing a mini OpenStack deployment on the edge-- >> Stu Miniman: Yep. >> And using Cinder and Instance and everything else, and then pushing, and as soon as they push that out to the public, they destroy what they had, and they start over, right? And so it's really... It's actually really interesting. And the economics, depending on the scale and everything else, you start adding it up, it's phenomenal, so... >> Well, you two are both plugged into the user community, the hands-on community. What's the mood of the community this year? Like I said, my first year, everybody seems engaged. I've just run in randomly to people that are spinning up their first clouds right now in 2017. So it seems like there's a lot of people here for the first time excited to get started. What do you think the mood of the user community is like? >> I think it's pretty good. I actually... So at the beginning of the week, I helped to run the OpenStack Upstream Institute, which is teaching people how to contribute to the Upstream Community. And there were a fair amount of users there. There are normally a lot of operators and then just a set of devs, and it seemed like there were a lot more operators and users looking that weren't originally interested in contributing Upstream that are now looking into those things. And at our... We had a presence at DockerCon, actually. We had a booth there, and there were a ton of users that were coming and talking to us, and like, "How can I use OpenStack with containers?" So it's, like, getting more interest with every day and growing rapidly, so... >> That's great. >> Yeah. >> All right. Well, want to thank both of you for joining us. I think this went flawless on the interview. (laughter) And yeah, thanks so much. >> Yeah. >> All these things happen... Live is forgiving, as we say on theCUBE and absolutely going forward. So thanks so much for joining us. >> John Griffith: Thank you. John and I will be back with more coverage here from the OpenStack Summit in Boston. You're watching theCUBE. (funky techno music)

(inspirational music) >> The history of high performance computing is unique and storied. You know, it's generally accepted that the first true supercomputer was shipped in the mid 1960s by Controlled Data Corporations, CDC, designed by an engineering team led by Seymour Cray, the father of Supercomputing. He left CDC in the 70's to start his own company, of course, carrying his own name. Now that company Cray, became the market leader in the 70's and the 80's, and then the decade of the 80's saw attempts to bring new designs, such as massively parallel systems, to reach new heights of performance and efficiency. Supercomputing design was one of the most challenging fields, and a number of really brilliant engineers became kind of quasi-famous in their little industry. In addition to Cray himself, Steve Chen, who worked for Cray, then went out to start his own companies. Danny Hillis, of Thinking Machines. Steve Frank of Kendall Square Research. Steve Wallach tried to build a mini supercomputer at Convex. These new entrants, they all failed, for the most part because the market at the time just wasn't really large enough and the economics of these systems really weren't that attractive. Now, the late 80's and the 90's saw big Japanese companies like NEC and Fujitsu entering the fray and governments around the world began to invest heavily in these systems to solve societal problems and make their nations more competitive. And as we entered the 21st century, we saw the coming of petascale computing, with China actually cracking the top 100 list of high performance computing. And today, we're now entering the exascale era, with systems that can complete a billion, billion calculations per second, or 10 to the 18th power. Astounding. And today, the high performance computing market generates north of $30 billion annually and is growing in the high single digits. Supercomputers solve the world's hardest problems in things like simulation, life sciences, weather, energy exploration, aerospace, astronomy, automotive industries, and many other high value examples. And supercomputers are expensive. You know, the highest performing supercomputers used to cost tens of millions of dollars, maybe $30 million. And we've seen that steadily rise to over $200 million. And today we're even seeing systems that cost more than half a billion dollars, even into the low billions when you include all the surrounding data center infrastructure and cooling required. The US, China, Japan, and EU countries, as well as the UK, are all investing heavily to keep their countries competitive, and no price seems to be too high. Now, there are five mega trends going on in HPC today, in addition to this massive rising cost that we just talked about. One, systems are becoming more distributed and less monolithic. The second is the power of these systems is increasing dramatically, both in terms of processor performance and energy consumption. The x86 today dominates processor shipments, it's going to probably continue to do so. Power has some presence, but ARM is growing very rapidly. Nvidia with GPUs is becoming a major player with AI coming in, we'll talk about that in a minute. And both the EU and China are developing their own processors. We're seeing massive densities with hundreds of thousands of cores that are being liquid-cooled with novel phase change technology. The third big trend is AI, which of course is still in the early stages, but it's being combined with ever larger and massive, massive data sets to attack new problems and accelerate research in dozens of industries. Now, the fourth big trend, HPC in the cloud reached critical mass at the end of the last decade. And all of the major hyperscalers are providing HPE, HPC as a service capability. Now finally, quantum computing is often talked about and predicted to become more stable by the end of the decade and crack new dimensions in computing. The EU has even announced a hybrid QC, with the goal of having a stable system in the second half of this decade, most likely around 2027, 2028. Welcome to theCUBE's preview of SC22, the big supercomputing show which takes place the week of November 13th in Dallas. theCUBE is going to be there. Dave Nicholson will be one of the co-hosts and joins me now to talk about trends in HPC and what to look for at the show. Dave, welcome, good to see you. >> Hey, good to see you too, Dave. >> Oh, you heard my narrative up front Dave. You got a technical background, CTO chops, what did I miss? What are the major trends that you're seeing? >> I don't think you really- You didn't miss anything, I think it's just a question of double-clicking on some of the things that you brought up. You know, if you look back historically, supercomputing was sort of relegated to things like weather prediction and nuclear weapons modeling. And these systems would live in places like Lawrence Livermore Labs or Los Alamos. Today, that requirement for cutting edge, leading edge, highest performing supercompute technology is bleeding into the enterprise, driven by AI and ML, artificial intelligence and machine learning. So when we think about the conversations we're going to have and the coverage we're going to do of the SC22 event, a lot of it is going to be looking under the covers and seeing what kind of architectural things contribute to these capabilities moving forward, and asking a whole bunch of questions. >> Yeah, so there's this sort of theory that the world is moving toward this connectivity beyond compute-centricity to connectivity-centric. We've talked about that, you and I, in the past. Is that a factor in the HPC world? How is it impacting, you know, supercomputing design? >> Well, so if you're designing an island that is, you know, tip of this spear, doesn't have to offer any level of interoperability or compatibility with anything else in the compute world, then connectivity is important simply from a speeds and feeds perspective. You know, lowest latency connectivity between nodes and things like that. But as we sort of democratize supercomputing, to a degree, as it moves from solely the purview of academia into truly ubiquitous architecture leverage by enterprises, you start asking the question, "Hey, wouldn't it be kind of cool if we could have this hooked up into our ethernet networks?" And so, that's a whole interesting subject to explore because with things like RDMA over converged ethernet, you now have the ability to have these supercomputing capabilities directly accessible by enterprise computing. So that level of detail, opening up the box of looking at the Nix, or the storage cards that are in the box, is actually critically important. And as an old-school hardware knuckle-dragger myself, I am super excited to see what the cutting edge holds right now. >> Yeah, when you look at the SC22 website, I mean, they're covering all kinds of different areas. They got, you know, parallel clustered systems, AI, storage, you know, servers, system software, application software, security. I mean, wireless HPC is no longer this niche. It really touches virtually every industry, and most industries anyway, and is really driving new advancements in society and research, solving some of the world's hardest problems. So what are some of the topics that you want to cover at SC22? >> Well, I kind of, I touched on some of them. I really want to ask people questions about this idea of HPC moving from just academia into the enterprise. And the question of, does that mean that there are architectural concerns that people have that might not be the same as the concerns that someone in academia or in a lab environment would have? And by the way, just like, little historical context, I can't help it. I just went through the upgrade from iPhone 12 to iPhone 14. This has got one terabyte of storage in it. One terabyte of storage. In 1997, I helped build a one terabyte NAS system that a government defense contractor purchased for almost $2 million. $2 million! This was, I don't even know, it was $9.99 a month extra on my cell phone bill. We had a team of seven people who were going to manage that one terabyte of storage. So, similarly, when we talk about just where are we from a supercompute resource perspective, if you consider it historically, it's absolutely insane. I'm going to be asking people about, of course, what's going on today, but also the near future. You know, what can we expect? What is the sort of singularity that needs to occur where natural language processing across all of the world's languages exists in a perfect way? You know, do we have the compute power now? What's the interface between software and hardware? But really, this is going to be an opportunity that is a little bit unique in terms of the things that we typically cover, because this is a lot about cracking open the box, the server box, and looking at what's inside and carefully considering all of the components. >> You know, Dave, I'm looking at the exhibitor floor. It's like, everybody is here. NASA, Microsoft, IBM, Dell, Intel, HPE, AWS, all the hyperscale guys, Weka IO, Pure Storage, companies I've never heard of. It's just, hundreds and hundreds of exhibitors, Nvidia, Oracle, Penguin Solutions, I mean, just on and on and on. Google, of course, has a presence there, theCUBE has a major presence. We got a 20 x 20 booth. So, it's really, as I say, to your point, HPC is going mainstream. You know, I think a lot of times, we think of HPC supercomputing as this just sort of, off in the eclectic, far off corner, but it really, when you think about big data, when you think about AI, a lot of the advancements that occur in HPC will trickle through and go mainstream in commercial environments. And I suspect that's why there are so many companies here that are really relevant to the commercial market as well. >> Yeah, this is like the Formula 1 of computing. So if you're a Motorsports nerd, you know that F1 is the pinnacle of the sport. SC22, this is where everybody wants to be. Another little historical reference that comes to mind, there was a time in, I think, the early 2000's when Unisys partnered with Intel and Microsoft to come up with, I think it was the ES7000, which was supposed to be the mainframe, the sort of Intel mainframe. It was an early attempt to use... And I don't say this in a derogatory way, commodity resources to create something really, really powerful. Here we are 20 years later, and we are absolutely smack in the middle of that. You mentioned the focus on x86 architecture, but all of the other components that the silicon manufacturers bring to bear, companies like Broadcom, Nvidia, et al, they're all contributing components to this mix in addition to, of course, the microprocessor folks like AMD and Intel and others. So yeah, this is big-time nerd fest. Lots of academics will still be there. The supercomputing.org, this loose affiliation that's been running these SC events for years. They have a major focus, major hooks into academia. They're bringing in legit computer scientists to this event. This is all cutting edge stuff. >> Yeah. So like you said, it's going to be kind of, a lot of techies there, very technical computing, of course, audience. At the same time, we expect that there's going to be a fair amount, as they say, of crossover. And so, I'm excited to see what the coverage looks like. Yourself, John Furrier, Savannah, I think even Paul Gillin is going to attend the show, because I believe we're going to be there three days. So, you know, we're doing a lot of editorial. Dell is an anchor sponsor, so we really appreciate them providing funding so we can have this community event and bring people on. So, if you are interested- >> Dave, Dave, I just have- Just something on that point. I think that's indicative of where this world is moving when you have Dell so directly involved in something like this, it's an indication that this is moving out of just the realm of academia and moving in the direction of enterprise. Because as we know, they tend to ruthlessly drive down the cost of things. And so I think that's an interesting indication right there. >> Yeah, as do the cloud guys. So again, this is mainstream. So if you're interested, if you got something interesting to talk about, if you have market research, you're an analyst, you're an influencer in this community, you've got technical chops, maybe you've got an interesting startup, you can contact David, david.nicholson@siliconangle.com. John Furrier is john@siliconangle.com. david.vellante@siliconangle.com. I'd be happy to listen to your pitch and see if we can fit you onto the program. So, really excited. It's the week of November 13th. I think November 13th is a Sunday, so I believe David will be broadcasting Tuesday, Wednesday, Thursday. Really excited. Give you the last word here, Dave. >> No, I just, I'm not embarrassed to admit that I'm really, really excited about this. It's cutting edge stuff and I'm really going to be exploring this question of where does it fit in the world of AI and ML? I think that's really going to be the center of what I'm really seeking to understand when I'm there. >> All right, Dave Nicholson. Thanks for your time. theCUBE at SC22. Don't miss it. Go to thecube.net, go to siliconangle.com for all the news. This is Dave Vellante for theCUBE and for Dave Nicholson. Thanks for watching. And we'll see you in Dallas. (inquisitive music)

>>from around the globe. It's the Q with digital coverage of exa scale day made possible by Hewlett Packard Enterprise. Welcome, everyone to the Cube celebration of Exa Scale Day. Shaheen Khan is here. He's the founding partner, an analyst at Orion X And, among other things, he is the co host of Radio free HPC Shaheen. Welcome. Thanks for coming on. >>Thanks for being here, Dave. Great to be here. How are you >>doing? Well, thanks. Crazy with doing these things, Cove in remote interviews. I wish we were face to face at us at a supercomputer show, but, hey, this thing is working. We can still have great conversations. And And I love talking to analysts like you because you bring an independent perspective. You're very wide observation space. So So let me, Like many analysts, you probably have sort of a mental model or a market model that you look at. So maybe talk about your your work, how you look at the market, and we could get into some of the mega trends that you see >>very well. Very well. Let me just quickly set the scene. We fundamentally track the megatrends of the Information Age And, of course, because we're in the information age, digital transformation falls out of that. And the megatrends that drive that in our mind is Ayotte, because that's the fountain of data five G. Because that's how it's gonna get communicated ai and HBC because that's how we're gonna make sense of it Blockchain and Cryptocurrencies because that's how it's gonna get transacted on. That's how value is going to get transferred from the place took place and then finally, quantum computing, because that exemplifies how things are gonna get accelerated. >>So let me ask you So I spent a lot of time, but I D. C and I had the pleasure of of the High Performance computing group reported into me. I wasn't an HPC analyst, but over time you listen to those guys, you learning. And as I recall, it was HPC was everywhere, and it sounds like we're still seeing that trend where, whether it was, you know, the Internet itself were certainly big data, you know, coming into play. Uh, you know, defense, obviously. But is your background mawr HPC or so that these other technologies that you're talking about it sounds like it's your high performance computing expert market watcher. And then you see it permeating into all these trends. Is that a fair statement? >>That's a fair statement. I did grow up in HPC. My first job out of school was working for an IBM fellow doing payroll processing in the old days on and and And it went from there, I worked for Cray Research. I worked for floating point systems, so I grew up in HPC. But then, over time, uh, we had experiences outside of HPC. So for a number of years, I had to go do commercial enterprise computing and learn about transaction processing and business intelligence and, you know, data warehousing and things like that, and then e commerce and then Web technology. So over time it's sort of expanded. But HPC is a like a bug. You get it and you can't get rid of because it's just so inspiring. So supercomputing has always been my home, so to say >>well and so the reason I ask is I wanted to touch on a little history of the industry is there was kind of a renaissance in many, many years ago, and you had all these startups you had Kendall Square Research Danny Hillis thinking machines. You had convex trying to make many supercomputers. And it was just this This is, you know, tons of money flowing in and and then, you know, things kind of consolidate a little bit and, uh, things got very, very specialized. And then with the big data craze, you know, we've seen HPC really at the heart of all that. So what's your take on on the ebb and flow of the HPC business and how it's evolved? >>Well, HBC was always trying to make sense of the world, was trying to make sense of nature. And of course, as much as we do know about nature, there's a lot we don't know about nature and problems in nature are you can classify those problems into basically linear and nonlinear problems. The linear ones are easy. They've already been solved. The nonlinear wants. Some of them are easy. Many of them are hard, the nonlinear, hard, chaotic. All of those problems are the ones that you really need to solve. The closer you get. So HBC was basically marching along trying to solve these things. It had a whole process, you know, with the scientific method going way back to Galileo, the experimentation that was part of it. And then between theory, you got to look at the experiment and the data. You kind of theorize things. And then you experimented to prove the theories and then simulation and using the computers to validate some things eventually became a third pillar of off science. On you had theory, experiment and simulation. So all of that was going on until the rest of the world, thanks to digitization, started needing some of those same techniques. Why? Because you've got too much data. Simply, there's too much data to ship to the cloud. There's too much data to, uh, make sense of without math and science. So now enterprise computing problems are starting to look like scientific problems. Enterprise data centers are starting to look like national lab data centers, and there is that sort of a convergence that has been taking place gradually, really over the past 34 decades. And it's starting to look really, really now >>interesting, I want I want to ask you about. I was like to talk to analysts about, you know, competition. The competitive landscape is the competition in HPC. Is it between vendors or countries? >>Well, this is a very interesting thing you're saying, because our other thesis is that we are moving a little bit beyond geopolitics to techno politics. And there are now, uh, imperatives at the political level that are driving some of these decisions. Obviously, five G is very visible as as as a piece of technology that is now in the middle of political discussions. Covert 19 as you mentioned itself, is a challenge that is a global challenge that needs to be solved at that level. Ai, who has access to how much data and what sort of algorithms. And it turns out as we all know that for a I, you need a lot more data than you thought. You do so suddenly. Data superiority is more important perhaps than even. It can lead to information superiority. So, yeah, that's really all happening. But the actors, of course, continue to be the vendors that are the embodiment of the algorithms and the data and the systems and infrastructure that feed the applications. So to say >>so let's get into some of these mega trends, and maybe I'll ask you some Colombo questions and weaken geek out a little bit. Let's start with a you know, again, it was one of this when I started the industry. It's all it was a i expert systems. It was all the rage. And then we should have had this long ai winter, even though, you know, the technology never went away. But But there were at least two things that happened. You had all this data on then the cost of computing. You know, declines came down so so rapidly over the years. So now a eyes back, we're seeing all kinds of applications getting infused into virtually every part of our lives. People trying to advertise to us, etcetera. Eso So talk about the intersection of AI and HPC. What are you seeing there? >>Yeah, definitely. Like you said, I has a long history. I mean, you know, it came out of MIT Media Lab and the AI Lab that they had back then and it was really, as you mentioned, all focused on expert systems. It was about logical processing. It was a lot of if then else. And then it morphed into search. How do I search for the right answer, you know, needle in the haystack. But then, at some point, it became computational. Neural nets are not a new idea. I remember you know, we had we had a We had a researcher in our lab who was doing neural networks, you know, years ago. And he was just saying how he was running out of computational power and we couldn't. We were wondering, you know what? What's taking all this difficult, You know, time. And it turns out that it is computational. So when deep neural nets showed up about a decade ago, arm or it finally started working and it was a confluence of a few things. Thalib rhythms were there, the data sets were there, and the technology was there in the form of GPS and accelerators that finally made distractible. So you really could say, as in I do say that a I was kind of languishing for decades before HPC Technologies reignited it. And when you look at deep learning, which is really the only part of a I that has been prominent and has made all this stuff work, it's all HPC. It's all matrix algebra. It's all signal processing algorithms. are computational. The infrastructure is similar to H B. C. The skill set that you need is the skill set of HPC. I see a lot of interest in HBC talent right now in part motivated by a I >>mhm awesome. Thank you on. Then I wanna talk about Blockchain and I can't talk about Blockchain without talking about crypto you've written. You've written about that? I think, you know, obviously supercomputers play a role. I think you had written that 50 of the top crypto supercomputers actually reside in in China A lot of times the vendor community doesn't like to talk about crypto because you know that you know the fraud and everything else. But it's one of the more interesting use cases is actually the primary use case for Blockchain even though Blockchain has so much other potential. But what do you see in Blockchain? The potential of that technology And maybe we can work in a little crypto talk as well. >>Yeah, I think 11 simple way to think of Blockchain is in terms off so called permission and permission less the permission block chains or when everybody kind of knows everybody and you don't really get to participate without people knowing who you are and as a result, have some basis to trust your behavior and your transactions. So things are a lot calmer. It's a lot easier. You don't really need all the supercomputing activity. Whereas for AI the assertion was that intelligence is computer herbal. And with some of these exa scale technologies, we're trying to, you know, we're getting to that point for permission. Less Blockchain. The assertion is that trust is computer ble and, it turns out for trust to be computer ble. It's really computational intensive because you want to provide an incentive based such that good actors are rewarded and back actors. Bad actors are punished, and it is worth their while to actually put all their effort towards good behavior. And that's really what you see, embodied in like a Bitcoin system where the chain has been safe over the many years. It's been no attacks, no breeches. Now people have lost money because they forgot the password or some other. You know, custody of the accounts have not been trustable, but the chain itself has managed to produce that, So that's an example of computational intensity yielding trust. So that suddenly becomes really interesting intelligence trust. What else is computer ble that we could do if we if we had enough power? >>Well, that's really interesting the way you described it, essentially the the confluence of crypto graphics software engineering and, uh, game theory, Really? Where the bad actors air Incentive Thio mined Bitcoin versus rip people off because it's because because there are lives better eso eso so that so So Okay, so make it make the connection. I mean, you sort of did. But But I want to better understand the connection between, you know, supercomputing and HPC and Blockchain. We know we get a crypto for sure, like in mind a Bitcoin which gets harder and harder and harder. Um and you mentioned there's other things that we can potentially compute on trust. Like what? What else? What do you thinking there? >>Well, I think that, you know, the next big thing that we are really seeing is in communication. And it turns out, as I was saying earlier, that these highly computational intensive algorithms and models show up in all sorts of places like, you know, in five g communication, there's something called the memo multi and multi out and to optimally manage that traffic such that you know exactly what beam it's going to and worth Antenna is coming from that turns out to be a non trivial, you know, partial differential equation. So next thing you know, you've got HPC in there as and he didn't expect it because there's so much data to be sent, you really have to do some data reduction and data processing almost at the point of inception, if not at the point of aggregation. So that has led to edge computing and edge data centers. And that, too, is now. People want some level of computational capability at that place like you're building a microcontroller, which traditionally would just be a, you know, small, low power, low cost thing. And people want victor instructions. There. People want matrix algebra there because it makes sense to process the data before you have to ship it. So HPCs cropping up really everywhere. And then finally, when you're trying to accelerate things that obviously GP use have been a great example of that mixed signal technologies air coming to do analog and digital at the same time, quantum technologies coming so you could do the you know, the usual analysts to buy to where you have analog, digital, classical quantum and then see which, you know, with what lies where all of that is coming. And all of that is essentially resting on HBC. >>That's interesting. I didn't realize that HBC had that position in five G with multi and multi out. That's great example and then I o t. I want to ask you about that because there's a lot of discussion about real time influencing AI influencing at the edge on you're seeing sort of new computing architectures, potentially emerging, uh, video. The acquisition of arm Perhaps, you know, amore efficient way, maybe a lower cost way of doing specialized computing at the edge it, But it sounds like you're envisioning, actually, supercomputing at the edge. Of course, we've talked to Dr Mark Fernandez about space born computers. That's like the ultimate edge you got. You have supercomputers hanging on the ceiling of the International space station, but But how far away are we from this sort of edge? Maybe not. Space is an extreme example, but you think factories and windmills and all kinds of edge examples where supercomputing is is playing a local role. >>Well, I think initially you're going to see it on base stations, Antenna towers, where you're aggregating data from a large number of endpoints and sensors that are gathering the data, maybe do some level of local processing and then ship it to the local antenna because it's no more than 100 m away sort of a thing. But there is enough there that that thing can now do the processing and do some level of learning and decide what data to ship back to the cloud and what data to get rid of and what data to just hold. Or now those edge data centers sitting on top of an antenna. They could have a half a dozen GPS in them. They're pretty powerful things. They could have, you know, one they could have to, but but it could be depending on what you do. A good a good case study. There is like surveillance cameras. You don't really need to ship every image back to the cloud. And if you ever need it, the guy who needs it is gonna be on the scene, not back at the cloud. So there is really no sense in sending it, Not certainly not every frame. So maybe you can do some processing and send an image every five seconds or every 10 seconds, and that way you can have a record of it. But you've reduced your bandwidth by orders of magnitude. So things like that are happening. And toe make sense of all of that is to recognize when things changed. Did somebody come into the scene or is it just you know that you know, they became night, So that's sort of a decision. Cannot be automated and fundamentally what is making it happen? It may not be supercomputing exa scale class, but it's definitely HPCs, definitely numerically oriented technologies. >>Shane, what do you see happening in chip architectures? Because, you see, you know the classical intel they're trying to put as much function on the real estate as possible. We've seen the emergence of alternative processors, particularly, uh, GP use. But even if f b g A s, I mentioned the arm acquisition, so you're seeing these alternative processors really gain momentum and you're seeing data processing units emerge and kind of interesting trends going on there. What do you see? And what's the relationship to HPC? >>Well, I think a few things are going on there. Of course, one is, uh, essentially the end of Moore's law, where you cannot make the cycle time be any faster, so you have to do architectural adjustments. And then if you have a killer app that lends itself to large volume, you can build silicon. That is especially good for that now. Graphics and gaming was an example of that, and people said, Oh my God, I've got all these cores in there. Why can't I use it for computation? So everybody got busy making it 64 bit capable and some grass capability, And then people say, Oh, I know I can use that for a I And you know, now you move it to a I say, Well, I don't really need 64 but maybe I can do it in 32 or 16. So now you do it for that, and then tens, of course, come about. And so there's that sort of a progression of architecture, er trumping, basically cycle time. That's one thing. The second thing is scale out and decentralization and distributed computing. And that means that the inter communication and intra communication among all these notes now becomes an issue big enough issue that maybe it makes sense to go to a DPU. Maybe it makes sense to go do some level of, you know, edge data centers like we were talking about on then. The third thing, really is that in many of these cases you have data streaming. What is really coming from I o t, especially an edge, is that data is streaming and when data streaming suddenly new architectures like F B G. A s become really interesting and and and hold promise. So I do see, I do see FPG's becoming more prominent just for that reason, but then finally got a program all of these things on. That's really a difficulty, because what happens now is that you need to get three different ecosystems together mobile programming, embedded programming and cloud programming. And those are really three different developer types. You can't hire somebody who's good at all three. I mean, maybe you can, but not many. So all of that is challenges that are driving this this this this industry, >>you kind of referred to this distributed network and a lot of people you know, they refer to this. The next generation cloud is this hyper distributed system. When you include the edge and multiple clouds that etcetera space, maybe that's too extreme. But to your point, at least I inferred there's a There's an issue of Leighton. See, there's the speed of light s So what? What? What is the implication then for HBC? Does that mean I have tow Have all the data in one place? Can I move the compute to the data architecturally, What are you seeing there? >>Well, you fundamentally want to optimize when to move data and when to move, Compute. Right. So is it better to move data to compute? Or is it better to bring compute to data and under what conditions? And the dancer is gonna be different for different use cases. It's like, really, is it worth my while to make the trip, get my processing done and then come back? Or should I just developed processing capability right here? Moving data is really expensive and relatively speaking. It has become even more expensive, while the price of everything has dropped down its price has dropped less than than than like processing. So it is now starting to make sense to do a lot of local processing because processing is cheap and moving data is expensive Deep Use an example of that, Uh, you know, we call this in C two processing like, you know, let's not move data. If you don't have to accept that we live in the age of big data, so data is huge and wants to be moved. And that optimization, I think, is part of what you're what you're referring to. >>Yeah, So a couple examples might be autonomous vehicles. You gotta have to make decisions in real time. You can't send data back to the cloud flip side of that is we talk about space borne computers. You're collecting all this data You can at some point. You know, maybe it's a year or two after the lived out its purpose. You ship that data back and a bunch of disk drives or flash drives, and then load it up into some kind of HPC system and then have at it and then you doom or modeling and learn from that data corpus, right? I mean those air, >>right? Exactly. Exactly. Yeah. I mean, you know, driverless vehicles is a great example, because it is obviously coming fast and furious, no pun intended. And also, it dovetails nicely with the smart city, which dovetails nicely with I o. T. Because it is in an urban area. Mostly, you can afford to have a lot of antenna, so you can give it the five g density that you want. And it requires the Layton sees. There's a notion of how about if my fleet could communicate with each other. What if the car in front of me could let me know what it sees, That sort of a thing. So, you know, vehicle fleets is going to be in a non opportunity. All of that can bring all of what we talked about. 21 place. >>Well, that's interesting. Okay, so yeah, the fleets talking to each other. So kind of a Byzantine fault. Tolerance. That problem that you talk about that z kind of cool. I wanna I wanna sort of clothes on quantum. It's hard to get your head around. Sometimes You see the demonstrations of quantum. It's not a one or zero. It could be both. And you go, What? How did come that being so? And And of course, there it's not stable. Uh, looks like it's quite a ways off, but the potential is enormous. It's of course, it's scary because we think all of our, you know, passwords are already, you know, not secure. And every password we know it's gonna get broken. But give us the give us the quantum 101 And let's talk about what the implications. >>All right, very well. So first off, we don't need to worry about our passwords quite yet. That that that's that's still ways off. It is true that analgesic DM came up that showed how quantum computers can fact arise numbers relatively fast and prime factory ization is at the core of a lot of cryptology algorithms. So if you can fact arise, you know, if you get you know, number 21 you say, Well, that's three times seven, and those three, you know, three and seven or prime numbers. Uh, that's an example of a problem that has been solved with quantum computing, but if you have an actual number, would like, you know, 2000 digits in it. That's really harder to do. It's impossible to do for existing computers and even for quantum computers. Ways off, however. So as you mentioned, cubits can be somewhere between zero and one, and you're trying to create cubits Now there are many different ways of building cubits. You can do trapped ions, trapped ion trapped atoms, photons, uh, sometimes with super cool, sometimes not super cool. But fundamentally, you're trying to get these quantum level elements or particles into a superimposed entanglement state. And there are different ways of doing that, which is why quantum computers out there are pursuing a lot of different ways. The whole somebody said it's really nice that quantum computing is simultaneously overhyped and underestimated on. And that is that is true because there's a lot of effort that is like ways off. On the other hand, it is so exciting that you don't want to miss out if it's going to get somewhere. So it is rapidly progressing, and it has now morphed into three different segments. Quantum computing, quantum communication and quantum sensing. Quantum sensing is when you can measure really precise my new things because when you perturb them the quantum effects can allow you to measure them. Quantum communication is working its way, especially in financial services, initially with quantum key distribution, where the key to your cryptography is sent in a quantum way. And the data sent a traditional way that our efforts to do quantum Internet, where you actually have a quantum photon going down the fiber optic lines and Brookhaven National Labs just now demonstrated a couple of weeks ago going pretty much across the, you know, Long Island and, like 87 miles or something. So it's really coming, and and fundamentally, it's going to be brand new algorithms. >>So these examples that you're giving these air all in the lab right there lab projects are actually >>some of them are in the lab projects. Some of them are out there. Of course, even traditional WiFi has benefited from quantum computing or quantum analysis and, you know, algorithms. But some of them are really like quantum key distribution. If you're a bank in New York City, you very well could go to a company and by quantum key distribution services and ship it across the you know, the waters to New Jersey on that is happening right now. Some researchers in China and Austria showed a quantum connection from, like somewhere in China, to Vienna, even as far away as that. When you then put the satellite and the nano satellites and you know, the bent pipe networks that are being talked about out there, that brings another flavor to it. So, yes, some of it is like real. Some of it is still kind of in the last. >>How about I said I would end the quantum? I just e wanna ask you mentioned earlier that sort of the geopolitical battles that are going on, who's who are the ones to watch in the Who? The horses on the track, obviously United States, China, Japan. Still pretty prominent. How is that shaping up in your >>view? Well, without a doubt, it's the US is to lose because it's got the density and the breadth and depth of all the technologies across the board. On the other hand, information age is a new eyes. Their revolution information revolution is is not trivial. And when revolutions happen, unpredictable things happen, so you gotta get it right and and one of the things that these technologies enforce one of these. These revolutions enforce is not just kind of technological and social and governance, but also culture, right? The example I give is that if you're a farmer, it takes you maybe a couple of seasons before you realize that you better get up at the crack of dawn and you better do it in this particular season. You're gonna starve six months later. So you do that to three years in a row. A culture has now been enforced on you because that's how it needs. And then when you go to industrialization, you realize that Gosh, I need these factories. And then, you know I need workers. And then next thing you know, you got 9 to 5 jobs and you didn't have that before. You don't have a command and control system. You had it in military, but not in business. And and some of those cultural shifts take place on and change. So I think the winner is going to be whoever shows the most agility in terms off cultural norms and governance and and and pursuit of actual knowledge and not being distracted by what you think. But what actually happens and Gosh, I think these exa scale technologies can make the difference. >>Shaheen Khan. Great cast. Thank you so much for joining us to celebrate the extra scale day, which is, uh, on 10. 18 on dso. Really? Appreciate your insights. >>Likewise. Thank you so much. >>All right. Thank you for watching. Keep it right there. We'll be back with our next guest right here in the Cube. We're celebrating Exa scale day right back.

>> Announcer: Live from Boston, Massachusetts, it's the CUBE covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, Red Hat, and additional ecosystem support. >> Welcome back, I'm Stu Miniman. And if I'm sitting on this side of the table with the long hallways behind me, it means we're here for the wrap of the second day. John Troyer's here, day two of three days, theCUBE here at OpenStack Summit. John, I feel like you're building energy as the show goes on, kind of like the show itself. >> Yeah, yeah, getting my footing here. Again, my first summit. It was a good second day, Stu, I think we made it through. We had some fascinating stuff. >> Yeah, fascinating stuff. Before we jump into some of the analysis here, I do want to say you know, first and foremost, big thanks to the foundation. Foundations themselves tend to get, they get beat up some, they get loved some, without the OpenStack Foundation, we would not be here. Their support for a number of years, our fifth year here at the show, as well as the ecosystem here, really interesting and diverse and ever-changing ecosystem, and that fits into our sponsors too. So Red Hat's our headline sponsor here. We had Red Hat Summit last week and two weeks, lots of Red Haters, and now lots of Stackers here. Additional support brought to us by Cisco, by Netronome, and by Canonical. By the way, no secret, we try to be transparent as to how we make our money. If it's a sponsored segment, it lists "sponsored by" that guest here, and otherwise it is editorial. Day three actually has a lot of editorial, it means we have a lot of endusers on the program. We do have vendors, cool startups, interesting people, people like Brian Stevens from Google. When I can get access to them, love to have it here. So big shout out as always. Content, we put it out there, the community, try to have it. Back to the wrap. John, you know we've kind of looked at some of the pieces here, the maturity, you know where it fits in the hybrid and multi cloud world. What jumped out at you as you've been chewing on day two? >> Well, my favorite thing from today, and we talked about it a couple times just in passing it keep coming up, is OpenStack on the edge. So the concept of, that the economics works today, that you can have a device, a box, maybe it's in your closet somewhere, maybe it's bolted to a lamppost or something, but in the old days it would have run on some sort of proprietary chip, maybe an embedded Linux. You can put a whole OpenStack distribution on there, and when you do that, it becomes controllable, it becomes a service layer, you can upgrade it, you can launch more services from there, all from a central location. That kind of blew my mind. So that's my favorite thing from today. I finally got my arms around that I think. >> Okay, great, and we saw Beth Cohen from Verizon was in the day one keynote. We're actually going to have her on our program for the third day. And right, teasing out that edge, most of it, telecommunications is a big discussion point here. I understand why. Telcos spend a lot of money, they are at large scale, and that NFV use case has driven a lot of adoption. So Deutsche Telekom is a headline sponsor of the OpenStack Foundation, did a big keynote this morning. AT&T's up on the main stage, Verizon's up on the main stage, you know Red Hat and Canonical all talk about their customers that are using it. You know, we just talked to Netronome about telecommunications. Everybody here, if you're doing OpenStack, you probably have a telco place because that's where the early money is and it tends to be, there's the network edge, then there's the IoT edge, and some of the devices there. So it was was one of the buzzy things going in and definitely is one of the big takeaways from the show so far. >> Well, Stu, I also think it's a major prove point for OpenStack, right. Bandwidth needs are not going down, that's pretty clear, with all the things you mentioned. Throughput is going to have to go up, services are going to have to be more powerful, and so all these different connected devices and qualities of service and streaming video to your car. So if OpenStack can build a back plan, a data plan for OpenStack that can do that, which it looks like they are doing, right, that's a huge prove point downstream from the needs of a telco, so I think that's super important for OpenStack that it's usable enough and robust enough to do that and that's one of the reasons I think it gets talked about so much. The nice thing is this year compared to my comparisons of previous years of OpenStack Summit, telco is not the only game in town, right. Enterprise also got a lot of play and there's a lot of use cases there too. >> And just to close out on that edge piece, really enjoyed the conversation we had with John and Kendall who had worked on the container space. Talking about the maturation of where Cinder had gone, how we went from virtualized environments to containerized environments. And even we teased out a little bit that edge use case. I can have a really small OpenStack deployment to put it at that edge. Maybe that's where some of the serverless stuff fits in. I know I've been, I tell my team, every time I get a good quote on serverless, let's make a gem out of that, put it out there, 'cause it's early days, but that is one of those deployments where I need at the edge environments, I need something lightweight, I need something that's going to be less expensive, can do some task processing, and both containers and potentially serverless can be interesting there. >> Yeah, I mean, even in our Canonical discussion with the product manager for their OpenStack distribution, right, containers are all over that, right, containers are just a way of packaging, there are some really interesting development pipelines that are now very popular and being talked about and built on in the container space. But containerization actually can come into play multiple points in the stack. Like you said, the Canonical distribution gets containerized and pushed out, it's a great way of compartmentalizing and upgrading, that's what the demo on stage today was about. Also, just with a couple of very short scripts, containerizing and pulling down components. So I think again, my second favorite thing after the edge today was just showing that actually containers and OpenStack mix pretty well. They're really not two separate things. >> Right, and I think containerization is one of those things that enables that multi cloud world. We talked in a number of segments today, everything from Kubernetes with Brian Stevens as to how that enables that. Reminds me at Red Hat Summit last week we talked a lot about OpenShift. OpenShift's that layer on top of OpenStack and sits at that application level layer to allow be to be able to span between public or private clouds and we need that kind of you know that to be able to enable some real multi or hybrid cloud environments. >> Yeah I mean, containers and in fact that Kubernetes layer may end up being the thing that drives more OpenStack adoption. >> Yeah, and the other thing that's been interesting, just hallway conversations, bumping into people we know, you know trying to walk around the show a little bit, as to people that are finally getting their arms around, okay, OpenStack from a technology standpoint has matured and you know they either need it to clean up what was their internal cloud or building something out, so real deployments. We talked about it yesterday in the close though. They're real customers doing real deployments. It's heartening to hear. >> Yeah I mean, one of those conversations, I ran into somebody at a hyperscale company, a friend of mine, and you know they are building out, internal OpenStack clouds to use for real stuff, right. >> But wait, hyperscale, come on, John, we can give away. Is this something we have on our phone or something we, I'll buy and use? >> One of those big folks. >> There's a large Chinese company that anybody in tech knows that's supposed to be doing a lot with OpenStack. We heard definitely Asia, very broad use of OpenStack. Been a theme of the whole show, right, is that outside the US where we tend to talk a lot about the public cloud, OpenStack's being used. An undertone I've heard is certain companies that start here in the United States, it's sometimes challenging for a foreign company to say I'm going to buy and use that, absolutely that is a headwind against a company like Amazon. Ties back to we had a keynote this morning with Edward Snowden and some of those things. What is the relationship between government and global companies that have a headquarters in the US and beyond. >> Yeah I think it's too soon to say where the pendulum, how the far the pendulum is going to swing. I'll be very interested in the commentary for next year to see have we moved away from more of the centralized services dominating the entire marketplace and workload into more distributed, more private, more customizable. For all those reasons, there's a lot of dynamics that might be pushing the pendulum in that direction. >> And one of the things I've liked hearing is infrastructure needs to be more agile, it needs to be more distributed, more modularized, especially as the applications are changing. So I feel like more than previous summits I've been at, we're at least talking about how those things fit together. With everything that's happening with the OpenStack Days, the Kubernetes, Cloud Foundry, Ceph, other open source projects, how those all fit together. It feels like a more robust, full position as opposed to , we were just building a software version of what we were doing in the data center before. >> My impression was the conversation at times had been a little more internally focused, right, it's a world unto its own. Here at this summit, they're definitely acknowledging there's an ecosystem, there's a landscape, it all has to interoperate. Usability's a part of that, and then interoperability and componentization is a part of that as well. >> The changing world of applications. We understand the whole reason we have infrastructure is to run those applications, so if we're not getting ready for that, what are we doing? >> I don't want to put words in their mouth, but I think the OpenStack community as a whole, one of their goals, you know, OpenStack needs to be as easy to run as a public cloud. The infrastructure needs to be boring. We heard the word boring a lot actually today. >> Yeah and what we say is, first of all, the public cloud is the bar that you were measured against. Whether it is easier or cheaper, your mileage may vary, because public cloud was supposed to be simple. They're adding like a thousand new features every year, and it seems to get more complicated over time. It's wonderful if we could architect everything and make it simple. Unfortunately, you know, that's why we have technology. I know every time I go home and have some interaction with a financial institution or a healthcare institution, boy, you wish we could make everything simpler, but the world's a complicated place and that's why we need really smart people like we've gotten to interview here at the show. So any final comments, John? >> No, I think that sums it up. Those are my favorite things for today. I'm looking forward to talking to a lot of customers tomorrow. >> Yeah, I'm really excited about that. John, appreciate your help here. So there's a big party here at the show. They're taking everyone to Fenway Park for the Stacker party. Last year it was an epic party in Austin. Boston's fun, Fenway's a great venue. Looks like the rain's going to hold off, which is good, but it'll be a little chillier than normal, but we will be back here with a third day of programming as John and I talked about. Got a lot of users on the program. Really great lineup, two days in the bag. Check out all the videos, go to SiliconANGLE.tv to check it all out. Big shout out to the rest of the team that's at the Dell EMC World and ServiceNOW shows, be able to check those out and all our upcoming shows. And thank you, everyone, for watching theCUBE. (technical beat)

lie from las vegas it's the cube covering discover 2016 las vegas brought to you by Hewlett Packard Enterprise now here's your host Jeff Frick hey Jeff Rick here with the cube we're in Las Vegas at the hpe discovered 2016 the first year that HP Enterprises has discovered in Vegas they flipped the switch before they went to a London last year so we're excited to be back a lot of changes a lot more green squares all over the place green frames so it's pretty exciting but you know obviously what's at the forefront of all this is data in big data what's happening with data so we're excited to get somebody from the trenches who's out working with customers first off crystal and obviously VP biz dev cube alumni been on all the time we'll see him in Boston how long Krista that show the end of August a little further and then ken Chris the director of data analytics for pwc welcome thank you nice to be here absolutely so welcome so data a lot of talk about data in kind of this this this change in data as it's kind of a liability back in the day like what am I going to do with all this stuff i'm going to sample to now I've got the data but that's not really enough you need to get the data to information you got to get the information to incite then you got to get the insight into actionable information so what are you seeing out in the real world with some of the customers that you work with so I think that a lot of what we're seeing with customers out there I mean I was walking through the floor earlier today and to see all the things that HP is doing with various technologies the people are partnering with very impressive but fundamentally at the end of the day a lot of those technologies are producing data and like you said clients and customers are trying to figure out how do i generate value from this how do I get it in the right hands of the people that can make decisions what am I seeing out in the industry today a lot of stuff particularly around customers personalization better service client experience we have the whole concept of CX which is that customer experience end-to-end don't just worry about you know how am I going to retain customers and prevent churn but also go up the the lifecycle and figure out how to attract more customers using data personalizing my service offerings improving my digital products things of that nature I'd love to get your perspective there's a lot of talk of you know there's never enough data scientists right how we're going to get enough data scientist but it takes me back to the day when there's never going to be enough chauffeur's this car thing is never going to take off I mean are you seeing the you know this kind of this vision of getting the data into the decision-makers hands getting it out of the hallowed halls of just the data science are you seeing that happening in the real world and what are some of the ways that that happened definitely I mean we've talked a long time about the concept of the data scientists being that individual that is like the unicorn it doesn't exist right so what we talk more about now is like pulling together those SWAT teams where you have someone that understands the data someone that understands the business problem someone that understands deep analytics spin teams like that up go out and find the answers yeah that's funny that you said that because we hear that a lot that data science is not an individual's it's a team sport you know you really have to bring a lot of people to bear and it's it's not just this this hallowed thing down a mahogany row at the very end it's actually getting that in you know and getting dirty with a lot of folks yeah that and I would also say another thing that's going to help with regards to the whole data scientist crunch is machine learning robotics things of that nature artificial intelligence I definitely think that that's something that people kid about as something that's far down the future but I think it's coming very quickly and something that customer sorry excuse me company should pay attention today so Chris you've been playing in the space forever you've seen a lot of transformation wonder if you could speak specifically to how the cloud has really impacted this whole kind of big data meme in this big data discussion because now suddenly it's a lot of people that have a lot of access to a lot of stuff that aren't necessarily connected to the VPN you know back at corporate headquarters that enable that to go out well it's allowed a lot of customers to iterate faster to try new things more quickly set them up take them down it's gotten business people involved one of the things can and I talked about in the session we just gave together was about how this is becoming more of a business discussion so our partnership with solution partners like PwC become more and more important because it's not always just IT people these days driving the data lakes it's now you're starting to see other sea level execs you know CFO the CMO starting to drive some of these initiatives and cloud-based solutions make those things more accessible so we're definitely seeing both quicker iteration and more business involvement the other thing we hear Kendall a lot about was back in the day right you had to sample you know you couldn't store all the data you couldn't process all the data yeah there was a lot of sampling going on right now that's that's changing you know you can store the data you can grab a lot more than you even think that you might need today but what you might need tomorrow and you can run big processes against big data sets that you couldn't do before you seen that kind of manifest itself in the market oh yeah all over the place i mean my specialty is within the entertainment media and communications business so when you talk about the cable companies and phone companies out there digesting set-top box data data coming off of phones if you go into the world where you know people Internet of Things sensor data just that you know we call it data to lose where where where it's just coming in Fast and Furious and the folks that are responsible for maintaining protecting and serving that data up are challenged more and more today and there's a lot of business pressure because people that use you know apps on their phone don't understand why can't I do the same thing with data that I know that we have to makes it make it insightful and actionable and allow me to do my job right but then kind of the dark side of that is if you have too much data you know our argues are you swimming in data that's not necessarily an indication of the change that you're trying to impact or you know it's not an indicator of something that you can take action so how are people kind of filtering through to get the right data to the right people at the right time yeah I mean Chris mentioned this and one of his previous answers but the attack that we take and that we stress with our clients is to take a business capabilities driven approach so when you think about the guy in the field that's responsible for sales or the person in the call center that's responsible for customer service taking the viewpoint of how what data do they need how do they need it served up how do they need it parsed and when do they need it that is the key to the approach to figuring out how do i find the signals through the noise what data is really worthwhile and do i really need to protect and make sure it gets served up versus this stuff i can keep versus this stuff i really don't need right and of course the other big trend is is an actual word spark summit we had another crew up there is this whole move to real time right and streaming data and not not you know grabbing capturing reviewing and looking back but watching it in real time and taking action while it's dreaming totally changing the business yeah fascinating and big data are used you know you use that car analogy before and if you heard Meg in the keynote say I think every driverless car is going to create three library of congress's worth of in fourth of data so and obviously it's very important right so you want to aggregate the data about what's going on with if you're running a fleet of cars but obviously you also have to know what's going on in the car and that's that's about as real time as it gets so and so these things are complementary big data and fascinator highly complementary and we're seeing a lot more activity out at the edge and obviously we made some announcements here both in terms of partners and some of our initiatives at HB around that here so Ken last question video we hear over and over and over the videos and increasing proportion of the total traffic on the Internet nobody ever thought that people would hang out on their phones and watch Game of Thrones or an NFL game or go warriors and you're in the media comes or the cube that's right well we knew they would watch a cute Chris um they're only 18 minutes but that's a huge huge stressor on resources a huge stress Iran on capacity storage networking and yet the customers want it right the expectation is going to be there it's going to look good so how is that impacting the guys on the back end that are responsible for delivering a good experience but they also have pricing pressure and they've got a ton of demands on their resources yeah yeah it's funny that you bring that up I walked into my house last week and hell-bent on having some good family time with my wife and kids and the TV was on and all of them had multiple devices actually iPads and iPhones that they were and everything was sucking off the internet which was kind of amusing to me but that's exactly your point and a lot of the companies that we're working with in the communications industry specifically their main goal and focuses to make sure that the pipes are big enough that they're utilized properly to make sure people have the best experience possible so utilizing the technology not only capture the data but really deep analytics to pinpoint where are my peaks and troughs and utilization and usage going to be how do i divert and make sure the right resources are available again also that can provide the best customer experience just can't over provision it like bananas oh yeah but it's expensive so you don't want those pipes of the empty either that's the thing you want to have enough capacity but you don't want / build that so it's it's an analytics challenge this analytics challenge and it's I always think of the old AT&T ma belle you know problem on Mother's Day everybody calls mom on mother's day back in the day you had to build the pipe to support mother's day even though most people aren't calling or not on Mother's Day well can Chris thanks for stopping by can give you last word we're looking forward to in the next six months as you know see some of the exciting things your customers are working on yeah i mean the technological advances are really great i will say that customers especially business consumers of the data getting very much more smarter much more savvy er so the demands on the folks serving up that data storing that data and protecting that data are going to be you know more and more crucial but it's it's just great business to be a part of it's great to see it's great to see the technology and some of the stuff that you guys are doing so we're proud to be part of it and happy to be here thanks for stopping by Ken Chris crystal and I'm Jeff Rick you're watching the cube we'll see you next time

live from las vegas it's the cube covering knowledge 60 brought to you by service now here your host dave vellante and Jeff Frick welcome back to knowledge 16 everybody this is the cube this is day two for the cube at knowledge our fourth year here cube goes out to the events we extract the signal from the noise we find the people that really know what they're talking about April Carter is a practitioner she's a senior IT operations manager at cars.com you want when a car go check out cars.com April thanks for coming on the cube thank you so take us inside well first of all talking about cars com I mean very competitive industry you're in right very competitive lots of merging market you're transforming thing you're disrupting and you're banging heads with everybody else is trying to do that but what's happening in the business what are the real pressures that are they're putting on i.t coming up with those new services really and really delivering quickly so we're very much anjel shops and we're doing continuous integration and continuous delivery is the big things for us right now the need for speed but so take us inside the world of IT Service Management your world you know what's it like so basically our transformation was you think of us of us accom so you think that were you know ahead of them a game but when I got two cars they were very paper and spreadsheet driven email is still even still very key to what we do you're rolling your eyes when you say that we can relate it annoys me so you know what service now has really helped us to really start that process and really rethink the services we deliver to our employees so everybody thinks of that external face to cars.com and that's what we focus on so much and we forget that internal phase so making things easier for our employees okay so um maybe start with the the journey of service now you you brought service now into the organization three years ago you had had experience in prior lives with not with service now but with other itsm vendors and they have always been very painful so when we did our bake off on what product that we were going to use you know when they came in they weren't we weren't really considering them a contender how long ago was this sorry two three years okay um but when they came in and they did their demo that you you know we were in the system and we're like this is a little too good to be true and then they say they we could be implemented in three months were like yeah right right that never happens but it all came to fruition and we were implemented with you know incident problem changed the basics you know knowledge an employee self-service portal with probably 30 or so orderable IIT items and it was a big deal for us and a huge success and how long did it take uh three months three months then you got a cake we did get a game everybody gets in here they don't miss daddy must have that in service now so they don't miss that reverse process okay so what was so the cars before was really paper-based spreadsheet based email base what was the business impact the business impact is really trying to drive our business partners in HR and in even in the development space to really try to rethink the way they interact internally so HR we implemented an onboarding automation so we went from multiple forms that we had to fill out as hiring managers to down to one so that was a big deal for us plus we were manually creating user accounts we were manually provisioning and how hardware and access we went through the entire process of about six months after we implemented service now to really try to grab ahold of that process and make it easier because we were delivering our new employees they're all of their things on time that first day because that's our goal but it was extremely painful for the service desk and those folks that Purdue that provisioning so we wanted to make it easier for them and we were able to okay so you you you brought in HR is Ellis recruiting but yeah okay HR pieces a little bit more difficult so we have let we left that piece out so we said onboarding yep you met onboarding so for my recruiting so as a hiring manager you basically submit the form to hire somebody and then all the way through to provisioning all their heirs and that inner integrates or interfaces in some way shape or form with your HR system or um it doesn't today it integrates with the recruiting system right okay which is separate from the HR system am okay and how does that integration occur so basically what what we did was we stood up a form within our catalog so as a hiring manager I can fill out all the information I need from the position that I'm filling through you know their salary requirements and all that kind of stuff plus all of their access they need once that person is hired all that's in there that in that form I can also save that form so as I need it in the future because I'm never going to remember what each person needs so i can say that form as well but then what service now does it sends that all that data over to Silk Road and actually implements all that data for the recruiters so they don't have to manually enter it because they were manually entering it before how do you find stuff ready listen giant content repository all right search it's just we have great search capabilities yeah yeah so this is that simple yeah cuz I could never find anything in my laptop uh-huh I'm very organized so it's one of those things that the the CMS that we had a portal that we have implemented now the design when we were implementing cuz it was three months we didn't really were thinking about everything it was a very broad scope when we were implementing so we didn't really think too heavily on a design of the portal and i think that the organization of the portals what probably annoys me the most at this point because people have to navigate through so much so with the news i'm very excited about the new CMS that they're pulling in helsinki which will actually help us to actual redesign that portal and get it so it's not so deep so as you say it's very hierarchical before yes and so now you're you're able to develop up with hell sinking a flatter structure exactly and it's much more easy to manage because right now it's kind of hard to manage especially if you don't have the technical skill set to do so because it's it's not easy it's more like nested folders versus labels exactly love labels so jizz so talk some more about the kinds of things that that you want to do with with the platform so there's a couple things we really want to push HR so HR is very very paper-based they love their paper actually so we implemented a take my paper ok what's your week HR status change form that you know it's a very very large process so any any time you want to change an employee status whether it's giving them a raise or changing their their location that they're based we fill out this foot paper form so we automated that and put it into service now it goes through approval processes so it's even auditable now or at least much easier to audit it and at the end of the design process was the HR folks are like well as long as I can print it out at the end I'll be fine yeah not really the point uploaded to ever know ok the other really thing that we're really excited about is actually so with the continuous delivery continuous integration that we're doing on the development side is we're opening up a lot of API is that our developers can use to automate a lot of their processes so we want to automate our release cycles right now everything's somewhat manual when we're doing release there's still people at the keyboard it's not wholly up manual but we want to get to that point where they just click on something in JIRA and it initiates the Jenkins Jenkins crates you know changes and it automates it all for them but it's still completely auditable from our perspective if you had to take a creative benefits pie and and you how to allocate a portion of the value let's say that's received by sort of IT versus outside of IT what would that pie look like I would say the biggest benefit is you know that an employee's so my goal is is to make the employees life easier I mean and that's the way I evangelize the product it's really what can I do to make your life easier what can I do to take some process it's very heavy and make it lighter for you that's the biggest biggest benefit the other thing is the ease of development on the tool so we don't want to go out and buy something every time a developer decides it wants to do something else so the ease of development so we can build small ABS we have a library app so they can check out kindle books I can check out Kendall even logins and within the tool that they're just little apps we're not going to go somewhere and buy that but we need to be able to do that so we can do that easily within the tool and it's funny in making the employees job easier is this nice second order effect where your phone doesn't ring exactly that's my goal are you don't tell him that little secret we were just doing it for you April could you talk about building these you know lightweight apps well describe the skill set of the people who are building these apps so they hard core developers or they locoed developers both I think its a mix of both so we some hard core developers that JavaScript pretty much 24 7 and then we have you know the admins who I can I code it within the tool myself but I'm definitely not a developer but it makes it easy enough for me to be able to do those little snippets of code that i need to make form easier for somebody to make it prettier to make it behave lightweight as so you're not you've never been a developer you've never written no code no never i still do it never works in pewter science major no ok but so you know you said no like okay so uh so you're smart this is ok all right ok but and so i want to dig it to level but so you are able to build apps or at least improve apps absolutely and I think there's there's multiple ways to do it obviously research the internet can tell me how to do a lot of stuff the community has been very helpful there's a also share the where you can find you know little little apps that will help you along your way as well so they make it very easy to actually kind of build out your core product did you have to go through training to get to that point or was it just sort of autodidactic or Ashley knowledge has been most of my training we didn't training at the beginning when we implemented but I haven't taken a look at training sense and you mentioned JIRA and just every tonight these stories make me think of JIRA it sounds like you know using kind of best practice in the hardcore software development part of the house and now bringing that over into the less hardcore software development side of the house but still very similar types of techniques and processes absolutely yeah that's great so bumper sticker on knowledge 16 for you what's the way they wouldn't when the trucks are pulling away from the Mandalay Bay was from April Carter standpoint what's it gonna say so the one thing there's a couple things I guess you know I did I always find vendors it at the show so I found we're implementing move soft right now it's a it's an event management tool and we're literally going through the process as we're here at the conference but it's it's an event management tool it I can't I in service now i can create manage my critical instant through being the OC critical incidents are my my bread and butter I have to make sure that those go off well and they that we reduce that time and i always find products here that I'm like oh I want to look into that we found one downstairs just yesterday that help is gonna help us and hopefully manage our mobile communications so all the cell phones and tablets and everything that we have in our orders and then dealing with the external vendors like Verizon and AT&T have been fun maybe not quite fun yeah I'm surprised something good here and and I learn a lot a new thing so it's it's always been very helpful how many years have you been coming um this will be my fourth your fourth all right same as ours too yeah April an awesome having you thanks so much for coming on the cube you know cube newbie did a great job awesome yeah you're a cube alone I here alone all right thank you thank you okay keep right here everybody will be back with our next guest is the cube we're live from knowledge 16 in Las Vegas bright back every once in a while a true break