>> Announcer: Live, from Vancouver, Canada it's the CUBE, covering OpenStack Summit North America 2018, brought to you by Red Hat, the Open Stack Foundation, and it's ecosystem partners. >> Welcome back, I'm Stu Miniman and my cohost John Troyer and you're watching the CUBE, the worldwide leader in tech coverage. Happy to welcome back to the program long time friend of the CUBE back from the earliest days, Randy Bias, Vice President with Juniper, Randy, great to see you. >> Absolutely, great to be back with you guys. >> All right, so Randy, we've been talking about, you know, community, and everything's going good and attendance might be down a little bit but how we fit in with containers and kubernetes, and everything, so we expect you to tear everything up for us and tell us the reality of what's happening in this community. >> I'll do my best (laughing). >> All right, so before we get to the kubernetic stuff, you're working on, we used to call it OpenContrail? Which you were involved in before Juniper acquired it, went through a rebranding recently, Tungsten, which I was looking up, came from the word heavy stone, give us the update from the networking side. >> Yeah, so the short history is that there was a company called Contrail, and they created a software defined networking controller, it was acquired by Juniper in 2012, 2013, and then that was open sourced, so Juniper for a long time was running with sort of two editions, Contrail which was the commercial offering, and OpenContrail which was the open source, and then shortly after I joined Juniper, identified that, you know, we really needed to go back to the drawing board on the way that we had organized the community, and transition it from being Juniper-led to community led, and so over the past year, I spearheaded that effort, and then that culminated in us announcing at the end of March at ONS that, you know, OpenContrail was now Tungsten Fabric. We renamed it, we moved it into the Linux foundation, under its governance, and now Juniper is one of many people of the community that have a seat at the table for the management, both from a business and technical perspective, and we're moving forward with a new reinvigorated community. >> Yeah, so networking sits at really the intersection of this multi-cloud world that we're living in. There's so many players trying to be there, you know Cisco, really moving to become more of a software company, when I interviewed their number two guy at their show, he's like, when you think of Cisco in the future, we're not even going to be a networking company, we'll be a software company. VMware, of course, pushed heavy through, then the Nicira acquisition, where does Tungsten fit, kind of compare and contrast for us, where it fits among some of these other offerings out there in the marketplace. >> Yeah, I mean, I think most enterprise vendors are in a similar transition from being a hardware to software companies. We're no different than any of the rest. I think we have a pretty significant advantage in that we have a lot of growth in the cloud sector, so a lot of the large public clouds are our customers and we're selling a tremendous amount of hardwaring to them, so I think we've got a lot longer runway. But, you know, we just recently hired CTO, Bikash Koley, out of Google, and we're starting to see some additional folks out of Google, like my new boss, Morgan, and what that's bringing with it is a very much a software first type perspective. So Bikash and Morgan really built everything for the Google network from the topper rack all the way out to the win and it's almost all software-based, disaggregated, hardware, software, opensource software running on top of white boxes, and so that kind of perspective is now really deep, start beginning to become embedded in Juniper. And at the head of that is Tungsten. So we see Tungsten Fabric as being sort of a tool that we use to create, you know, a global ubiquitous network fabric, that anybody can use anywhere, without talking to Juniper at all, without knowing that Juniper's part of Tungsten, and then as they grow up and they get to a point where they need multi-cloud, they need federation, or they need kind of day two enterprise operations, you know, we have a commercial version and a commercial distribution that they can use. >> Randy, we talked a little bit about OpenContrail and last year, at OpenStack Summit and moving it to a more of a community based governance model, and now that's happened with the Linux Foundation, can you talk a little bit about the role of opensource governance, and corporate governance, and then foundations, and just going forward, you know, what's an effective model for 2018 going forward, for a foundation-led project and maybe in the context of Tungsten Fabric, and how is that looking? >> Yeah, so again, OpenContrail's now Tungsten Fabrics, might be new for some of the viewers, lot of people still coming to terms with that. And so one of the things that we noticed is that, and when many people go and they say, hey, we want opensource first, the AT&T's of this world, part of what they're saying, one of the aspects of being opensource versus we want to be one of many around the table, we want to have a seat at the table, we want to have the option to contribute code back, and we want to feel like it's a group effort. And so that was a big factor, right? It was an opensource project, but it was largely the governance was carried by Juniper, all the testing infrastructure was Juniper, you know, all of the people who made architectural decisions were Juniper, all of the lead contributors were Juniper, and so, going to Linux Foundation was critical to us having a legal framework, for the trademarks, the code, the licenses, the contributor license agreements, are all owned and operated by the Linux Foundation and not by Juniper, so we basically have a trusted third party who can mediate all those things and create a structure, a governance small structure where Juniper has one seat at the table, and all the other community members do as well. So it was really key to getting, to moving to that model to increase people's interest in the project and to really go the next level. There just wasn't any way to do it without doing this. >> All right, so, Randy, let's talk about OpenStack. You were watching the keynote yesterday, you were, you know, in the Twitter stream, >> Randy: I don't usually watch keynotes, man. >> Stu: But you know this community, so-- >> I do know this community (laughing). >> Give us kind of the good, the bad, and the ugly from your standpoint as to, you know, where we've gone, you know, what's doing well, and what you're frustrated as heck that we still haven't fixed yet. >> Well, I mean, it's great that we have so much inroads amongst the carriers, it's great that, you know, that there's a segment that OpenStack has been able to land in. I mean, at some points when I was feeling particularly pessimistic on some days, I was like, oh man, this thing's never going to go anywhere, so that's great. On the other hand, you know, the promise that we had of sort of being the Linux operating center, operating system of the data center, and you know, really gaining inroads into private cloud and enterprise, that just hasn't materialized and I don't see a path to that. A lot of that has to do with history, I'm not sure how much of that I want to go into here, but I see those as being bright lights. I see the Ocata containers effort and sort of having this alternative structure that's more or less like the umbrella structure that I lobbied for while I was on the board. So for several years on the board, I said we need to really look more like the Apache Software Foundation, we need to look less like the Linux Operating System in terms of how we think about things. Not this big integrated monolithic release, you need more competition between projects and that just wasn't really embraced. And I think that that, in a way, that was one of several things that really kind of limited our ability to capture the market that we really wanted, which is the enterprise market. >> Yeah, well, I know, and one of those sticking points there that I've talked to you many times over the years about is how do I actually deploy this? You know, getting a base configuration and scaling this out, simplicity is tough, getting to those environments, you know, getting it up in two weeks, is good for some environments, but maybe not for others. >> Yeah, I mean I think there's sort of a spectrum, right? At one end of the spectrum, you say hey, I'm going to have a very opinionated approach like kubernetes does, and we're going to limit what we say we can do, you know, we're not all things to all people. And I think that opinionated approach, like the Linux operating system worked very, very well. And then other end of the spectrum is we've got no opinion like the Apache Software Foundation, and then it's up to vendors to go and cherry pick the pieces they want and turn that into some kind of commercial offering, whether it's Hortonworks, or Thi-dare or Du-per or whatever it is, the problem is that OpenStack wound up in the middle where it had the sort of integrated monolithic release cycle which it still does, which started to be all things to all people, and it was never as great as it could be, so it's like we got to support Hyper-V, we got to support VMware, and as the laundry list of all things we have to support grew longer, it became more and more difficult to have a compelling, easy to use, easy to scale offering that any enterprise could consume. >> Randy, a lot of talk this week about edge computing, with several different definitions, right? But it does strike me that, you know, there's a certain set of apps, that you write 'em and that they live fine in a big public cloud, and a big data center somewhere. But there's a lot of hardware that's going to be living out in the world, whether that's at the base of a radio tower, or in a wall, or in my shoe, that is going to be running hardware, and is going to be running something, and sometimes that something can be OpenStack, and we're seeing some examples of it, many examples of that already. Is that an area of growth for OpenStack? Is that an interesting part of how this fabric is going to expand? >> Well, I probably have a contrarian view here. So, I spent a bunch of time at Juniper, one of the things I worked on for a while was edge computing and we're still trying to decide what we want to do there and you know, kind of to the first point you made is everybody's edge is different, right? Is it on the mobile phone, is it back in the data center, the difference is that the real estate gets more expensive as you move out, right? And it's in terms of latency, and it's in terms of bandwidth and it's also in terms of cost of storage and compute. There's a move closer to the mobile device that becomes progressively more expensive, and so that's why a lot of people sort of look and say hey, wouldn't it be nice if we can get you out the closer lower latency and bandwidth and so on but as we looked at it, a lot of the different use cases it became really interesting in that, it wasn't clear if there was that much value between 5 milliseconds and 20 milliseconds, right? I mean, that's pretty, either one's pretty close, sure there's a lot of difference between 20 and a 100, but maybe not so much between 5 and 20. And so we kind of came to the conclusion that at least for right now, probably, the bulk of use cases are fine with 20 milliseconds, and what that means is that regional systems like AWS's Lambda at the Edge, they're in metro, those are probably good for most cases. I don't know that you need to be on the tower, I don't know that you need to be in the central office, so I think edge computing is still nascent, we don't know exactly what all those use cases are, but I think you might be able to service most of them from regional data centers, and then the question really becomes what does that stack need to be and if you have a regional data center that's got plenty of power, plenty of space, then it might be that OpenStack is a good solution, but if you're trying to scale down onto the tower, I got to have some doubts about whether OpenStack can really scale down that far. >> Randy, analytics is something we've been seeing, the networking people used for many years, at this show, starting to hear a lot of discussion about AI and ML, would love your view point as to what you're seeing in that space. >> You know I have some friends who started off in AI in very early days and he had a very pessimistic view. He said, you know this stuff comes and goes, but I'm actually very positive and optimistic about it because the way I look at this is there's a renaissance happening which is that, you know, now ML is really available to masses and you're seeing people do really interesting things like, we have a product called AppFormix, and what they do is they take ML and they apply it to operations and I love this because as an operations guy, you know, I used to have these problems in production where something would go out and the first thing I'd do, is I'm trying to do correlation and then root cause analysis, like, what was the actual failure? Like I can see the symptom on this end and now I have to get all the way back to what caused it, and the reality is that machine learning, AI techniques and protocols can do all the heavy lifting for operators very, very quickly and basically surface a problem for somebody to do the final analysis on. And so I do think that ML and AI apply to very specific vertical problems, it is just a place where we're going to see a tremendous amount of revolution in the next couple years. >> All right, and that hits right at really that intersection between kind of the developers and the operators there-- >> Absolutely. >> What are you seeing from an organizational standpoint, companies you're talking to these days, how are they doing adopting that change, dealing with that, you know, often schism or are they bringing those groups together? >> Well, I think you remember that like in the early days, I used bring my deck along and I would talk about assembly line IT versus the robotics spectrum all of IT and I would sort of make that sort of analogy to sort of the car manufacturing process, and I think what machine learning is really going to do is take us to that next level past that right? So we had the assembly line where we have all the specialists, we had the robotics factory where we had people who know how to build a robots and software, and it's really sort of like, just churning out with a lot of people on the line, and I think the next level after that is, you know, completely fully automated applications driving themselves, you know, self-driving applications, and I think that's when things get really interesting, and maybe we start to remove the traditional operator out of the equation and it really becomes about empowering developers with tools that are comfortable and that leverage all the cloud era and stuff that we built. >> All right, so Randy, you're credited with the pets versus cattle analogy, what's the latest, you were talking about some of the previous slide decks, what's Randy Bias looking on down the road? >> I mean, the stuff just comes to me, man. I can't like predict, but the thing I've been talking about a lot lately is services of platform, I think we might've talked about that last time, which is just this notion that if we look at where Amazon's invested and what's interesting, it's certainly not at the infrastructure layer and it's really not at the PAS layer, it's that thick layer in between with like database as a service and NoSQL as a service, and messaging service, and DNS and so on, where you can kind of cherry pick those things as you're assembling your own PAS for your application, and I still think that's the area that is under-discussed, and the reason is is the people back into basically doing that, building kind of the service as a platform system, but they're not like going into it, kind of like eyes wide open. >> Yeah, so just following up on that last piece, one of the criticisms I have this week is when you talk about multi-cloud, most of the people talk about, oh well people are clawing things back to their data centers. Juniper plays across the board, strong partnership with Amazon, yet you're here, what are you hearing from customers, you know, what do you see as kind of the balance there and, you know, the public cloud's role in the world? >> I mean, they're still winning, right? I don't think there's any doubt, I haven't seen a decline back here talking about, but we are starting to enter into the era of, okay, this stuff is out there, and it's running, but I need to find my governance model, I need to understand who's using what, I need to understand what it's costing me, and that's the sign of the maturation process. And so I think that, you know, we saw in the early days of cloud, people jumping the gun, creating compliance services, and you know, SAS products that would basically measure how much you're spending and think that it's time for that stuff to come back in vogue again, because the tool needs to be there for people to manage these extended supply chain of IT vendors which include the public cloud. And I think that the idea that would claw them back as opposed to like just see that as holistic part of what we're trying to accomplish doesn't make any sense. >> Well learned. Well, Randy Bias, always a pleasure to catch up with you. >> John. >> John Troyer, I'm Stu Miniman, getting towards the end of two days of three days of live coverage. Thanks for staying with the CUBE. (bubbly electronic music)

(upbeat music) >> Announcer: Live from Vancouver, Canada. It's The Cube covering Openstack Summit, North America, 2018. Brought to you by Red Hat, The Open Stack Foundation, and it's ecosystem partners. >> Welcome back to The Cube's coverage of Openstack Summit 2018 in Vancouver. I'm Stu Miniman with cohost of the week, John Troyer. Happy to welcome back to the program Stephan Fabel, who is the Director of Ubuntu product and development at Canonical. Great to see you. >> Yeah, great to be here, thank you for having me. Alright, so, boy, there's so much going on at this show. We've been talking about doing more things and in more places, is the theme that the Open Stack Foundation put into place, and we had a great conversation with Mark Shuttleworth, and going to dig in a little bit deeper in some of the areas with you. >> Stephan: Okay, absolutely. >> So we have the Cube, and we're go into all of the Kubernetes, Kubeflow, and all those other things that we'll mispronounce how they go. >> Stephan: Yes, yes, absolutely. >> What's your impression of the show first of all? >> Well I think that it's really, you know, there's a consolidation going on, right? I mean, we really have the people who are serious about open infrastructure here, serious about OpenStack. They're serious about Kubenetes. They want to implement, and they want to implement at a speed that fits the agility of their business. They want to really move quick with the obstrain release. I think the time for enterprise hardening delays an inertia there is over. I think people are really looking at the core of OpenStack, that's mature, it's stable, it's time for us to kind of move, get going, get success early, get it soon, then grow. I think most of the enterprise, most of the customers we talk to adopt that notion. >> One of the things that sometimes helps is help us lay out the stack a little bit here because we actually commented that some of the base infrastructure pieces we're not talking as much about because they're kind of mature, but OpenStack very much at the infrastructure level, your compute, storage, and network need to understand. But then we when we start doing things like Kubernetes as well, I can either do or, or on top of, and things like that, so give us your view as to what'd you put, what Canonical's seeing, and what customers-- how you lay out that stack? >> I think you're right, I think there's a little bit of path-finding here that needs to be done on the Kubernetes side, but ultimately, I think it's going to really converge around OpenStack being operative-centric, and operative-friendly, working and operating the infrastructure, scaling that out in a meaningful manner, providing multitenancy to all the different departments. Having Kubernetes be developer-centric and really help to on-board and accelerate the workload that option of the next gen initiatives, right? So, what we see is absolutely a use case for Kubernetes and OpenStack to work perfectly well together, be an extension of each other, possibly also sit next to each other without being too incumbenent there. But I think that ultimately having something like Kubernetes contain a based developer APIs that are providing that orchestration layer are the next thing, and they run just perfectly fine on Canonical OpenStack. >> Yeah, there certainly has been a lot of talk about that here at the show. Let's see, let's go a level above that, things we run on Kubernetes, I wanted to talk a little bit about ML and AI and Kubeflow. It seems like we're, I'd almost say that we're, this is like, if we were a movie, we're in a sequel like AI-5; this time, it's real. I really do see real enterprise applications incorporating these technologies into the workflow for what otherwise might be kind of boring, you know, line of business, can you talk a little bit about where we are in this evolution? >> You mean, John, only since we've been talking about it since the mid-1800s, so yeah. >> I was just about to point that out, I mean, AI's not new, right? We've seen it since about 60 years. It's been around for quite some time. I think that there is an unprecedented amount of sponsorship of new startups in this area, in this space, and there's a reason why this is heating up. I think the reason why ultimately it's there is because we're talking about a scale that's unprecedented, right? We thought the biggest problem we had with devices was going to be the IP addresses running out, and it turns out, that's not true at all, right? At a certain scale, and at a certain distributed nature of your rollout, you're going to have to deal with just such complexity and interaction between the underlying, the under-cloud, the over-cloud, the infrastructure, the developers. How do I roll this out? If I spin up 1000 BMs over here, why am I experiencing dropped calls over there? It's those types of things that need to be self-correlated. They need to be identified, they need to be worked out, so there's a whole operator angle just to be able to cope with that whole scenario. I think there's projects that are out there that are trying to ultimately address that, for example, Acumos (mumbles) Then, there is, of course, the new applications, right? Smart cities to connect to cars, all those car manufacturers who are, right now, faced with the problem: how do I deal with mobile, distributed inference rollout on the edge while still capturing the data continually, train my model, update, then again, distribute out to the edge to get a better experience. How do I catch up to some of the market leaders here that are out there? As the established car manufacturers are going to come and catch up, put more and more miles autonomously on the asphalt, we're going to basically have to deal with a whole lot more of proctization of machine-learning applications that just have to be managed at scale. And so we believe for all certain good company in that belief that having to manage large applications at scale, that containers and Kubernetes is a great way to do that, right? They did that for web apps. They did that for the next generation applications. This is one example where with the right operators in mind, the right CRDs, the right frameworks on top of Kubernetes managed correctly, you are actually in a great position to just go to market with that. >> I wonder if you might have a customer example that might go to walk us through kind of where they are in this discussion, talk to many companies, you know, the whole IOT even pieces were early in this. So what's actually real today, how much is planning, is this years we're talking before some of these really come to fruition? >> So yeah, I can't name a customer, but I can say that every single car manufacturer we're talking to is absolutely interested in solving the operational problem of running machine-learning frameworks as a service, making sure those are up running and up to speed at any given point in time, spin them up in a multitenant fashion, make sure that the GPU enablement is actually done properly at all layers of the virtualization. These are real operational challenges that they're facing today, and they're looking to solve with us. Pick a large car manufacturer you want. >> John: Nice. We're going down to something that I can type on my own keyboard then, and go to GitHub, right? That's one of the places to go where it is run, TensorFlow of machine-learning framework on Kubernetes is Kubeflow, and that little bit yesterday on stage, you want to talk about that maybe? >> Oh, absolutely, yes. That's the core of our current strategy right now. We're looking at Kubeflow as one of the key enablers of machine-learning frameworks as a service on top of Kubernetes, and I think they're a great example because they can really show how that as a service can be implemented on top of a virtualization platform, whether that be KVM, pure KVM, on bare metal, on OpenStack, and actually provide machine-learning frameworks such as TensorFlow, Pipe Torch, Seldon Core. You have all those frameworks being supported, and then basically start mix and matching. I think ultimately it's so interesting to us because the data scientists are really not the ones that are expected to manage all this, right? Yet they are the core of having to interact with it. In the next generation of the workloads, we're talking to PHDs and data scientists that have no interest whatsoever in understanding how all of this works on the back end, right? They just want to know this is where I'm going to submit my artifact that I'm creating, this is how it works in general. Companies pay them a lot of money to do just that, and to just do the model because that's where, until the right model is found, that is exactly where the value is. >> So Stephan, does Canonical go talk to the data scientists, or is there a class of operators who are facilitating the data scientists? >> Yes, we talk to the data scientists who understand their problems, we talk to the operators to understand their problems, and then we work with partners such as Google to try and find solutions to that. >> Great, what kind of conversations are you having here at the show? I can't imagine there's too many of those, great to hear if there are, but where are they? I think everybody here knows containers, very few know Kubernetes, and how far up the stack of building new stuff are they? >> You'd be surprised, I mean, we put this out there, and so far, I want to say the majority of the customer conversations we've had took an AI turn and said, this is what we're trying to do next year, this is what we're trying to do later in the year, this is what we're currently struggling with. So glad you have an approach because otherwise, we would spend a ton of time thinking about this, a ton of time trying to solve this in our own way that then gets us stuck in some deep end that we don't want to be. So, help us understand this, help us pave the way. >> John: Nice, nice. I don't want to leave without talking also about Microcades, that's a Kubernetes snap, you code some clojure download, Can we talk a little bit about that? >> Yeah, glad to. This was an idea that we conceived that came out of this notion of alright, well if I do have, talking to a data scientist, if I do have a data scientist, where does he start? >> Stu: Does Kubernetes have a learning curve to date? >> It does, yeah, it does. So here's the thing, as a developer, you have, what options do you have right when you get started? You can either go out and get a community stood up on one of the public clouds, but what if you're in the plane, right? You don't have a connection, you want to work on your local laptop. Possibly, that laptop also has a GPU, and you're a data scientist and you want to try this out because you know you're going to submit this training job now to a (mumbles) that runs un-prem behind the firewall with a limited training set, right? This is the situation we're talking about. So ultimately, the motivation for creating Microcades was we want to make this very, very equivalent. Now you can deploy Kubeflow on top of Microcades today, and it'll run just fine. You get your TensorBoard, you have Jupyter notebook, and you can do your work, and you can do it in a fashion that will then be compatible to your on-prem and public machine-learning framework. So that was your original motivation for why we went down this road, but then we noticed you know what, this is actually a wider need. People are thinking about local Kubernetes in many different ways. There are a couple of solutions out there. They tend to be cumbersome, or more cumbersome than developers would like it. So we actually said, you know, maybe we should turn this into a more general purpose solution. So hence, Microcades. It works like a snap on your machine, you kick that off, you have Kubernetes API, and under 30 seconds or little longer if your download speed plays a factor here, you enable DNS and you're good to go. >> Stephan, I just want to give you the opportunity, is there anything in the Queens Release that your customers have been specifically waiting for or any other product announcements before we wrap? >> Sure, we're very excited about the Queens Release. We think Queens Release is one of the great examples of the maturity of the code base and really the knot towards the operator, and that, I think was the big challenge beyond the olden days of OpenStack where the operators took a long time for the operators to be heard, and to establish that conversation. We'd like to say and to see that OpenStack Queens has matured in that respect, and we like things like Octavia. We're very exciting about (mumbles) as a service, taking its own life and being treated as a first-class citizen. I think that it was a great decision of the community to get on that road. We're supporting as a part of our distribution. >> Alright, well, appreciate the update. Really fascinating to hear about all, you know, everybody's thinking about it and really starting to move on all the ML and AI stuff. Alright, for John Troyer, I'm Tru Miniman. Lots more coverage here from OpenStack Summit 2018 in Vancouver. Thanks for watching The Cube. (upbeat music)