>>from around the globe. It's >>the cube >>with coverage of >>Kublai khan and >>Cloud Native Con, Europe 2021 virtual brought >>to you by red hat, >>the cloud Native >>Computing foundation and ecosystem partners. Hello, welcome back to the cubes coverage of Kublai khan. Cloud Native Con 2021 part of the CNC. S continuing cube partnership virtual here because we're not in person soon, we'll be out of the pandemic and hopefully in person for the next event. I'm john for your host of the key. We're here with ricardo. Roach computing engineers sir. In CUBA. I'm not great to see you ricardo. Thanks for remote ng in all the way across the world. Thanks for coming in. >>Hello, Pleasure. Happy to be here. >>I saw your talk with Priyanka on linkedin and all around the web. Great stuff as always, you guys do great work over there at cern. Talk about what's going on with you and the two speaking sessions you have it coop gone pretty exciting news and exciting sessions happening here. So take us through the sessions. >>Yeah. So actually the two sessions are kind of uh showing the two types of things we do with kubernetes. We we are doing we have a lot of uh services moving to kubernetes, but the first one is more on the services we have in the house. So certain is known for having a lot of data and requests, requiring a lot of computing capacity to analyze all this data. But actually we have also very large community and we have a lot of users and people interested in the stuff we do. So the first question will actually show how we've been uh migrating our group of infrastructure into the into communities and in this case actually open shift. And uh the challenge there is to to run a very large amount of uh global websites on coordinators. Uh we run more than 1000 websites and there will be a demonstration on how we do all the management of the website um life cycle, including upgrading and deploying new new websites and an operator that was developed for this purpose. And then more on the other side will give with a colleague also talk about machine learning. Machine learning has been a big topic for us. A lot of our workloads are migrating to accelerators and can benefit a lot from machine learning. So we're giving a talk about a new service that we've deployed on top of Cuban areas where we try to manage to uh lifecycle of machine learning workloads from data preparation all the way to serving the bottles, also exploring the communities features and integrating accelerators and a lot of accelerators. >>So one part of the one session, it's a large scale deployment kubernetes key to there and now the machine learning essentially service for other people to use that. Right? Like take me through the first large scale deployment. What's the key innovation there in your opinion? >>Yeah, I think compared to the infrastructure we had before, is this notion that we can develop an operator that will uh, manage resource, in this case a website. And this is uh, something that is not always obvious when people start with kubernetes, it's not just an orchestra, it's really the ap and the capability of managing a huge amount of resources, including custom resources. So the possibility to develop this operator and then uh, manage the lifecycle of uh, something that was defined in the house and that fits our needs. Uh, There are challenges there because we have a large amount of websites and uh, they can be pretty active. Uh, we also have to some scaling issues on the storage that serves these these websites and we'll give some details uh during the talk as well, >>so kubernetes storage, this is all kind of under the covers, making this easier. Um and the machine learning, it plays nicely in that what if you take us for the machine learning use case, what's going on there, wow, what was the discovery, How did you guys put that together? What's the key elements there? >>Right, so the main challenge there has been um that machine learning is is quite popular but it's quite spread as well, so we have multiple groups focusing on this, but there's no obvious way to centralize not only the resource usage and make it more efficient, but also centralize the knowledge of how these procedures can be done. So what we are trying to do is just offer a service to all our users where we help them with infrastructure so that they don't have to focus on that and they could focus just on their workloads and we do everything from exposing the data systems that we have in the house so that they can do access to the data and data preparation and then doing um some iteration using notebooks and then doing distributed training with potentially large amount of gps and that storage and serving up the models and all of this is uh is managed with the coordinates cluster underneath. Uh We had a lot of knowledge of how to handle kubernetes and uh all the features that everyone likes scalability. The reliability out of scaling is very important for this type of workload. This is, this is key. >>Yeah, it's interesting to see how kubernetes is maturing, um congratulations on the projects. Um they're going to probably continue to scale. Remember this reminds me of when I was uh you know coming into the business in the 98 late eighties early nineties with TCP I. P. And the S. I. Model, you saw the standards evolve and get settled in and then boom innovation everywhere. And that took about a year to digest state and scale up. It's happening much faster now with kubernetes I have to ask you um what's your experience with the question that people are looking to get answered? Which is as kubernetes goes, the next generation of the next step? Um People want to integrate. So how is kubernetes exposing a. P. I. S. To say integration points for tools and other things? Can you share your experience and where this is going, what's happening now and where it goes? Because we know there's no debate. People like the kubernetes aspect of it, but now it's integration is the conversation. Can you share your thoughts on that? >>I can try. Uh So it's uh I would say it's a moving target, but I would say the fact that there's such a rich ecosystem around kubernetes with all the cloud, David projects, uh it's it's uh like a real proof that the popularity of the A. P. I. And this is also something that we after we had the first step of uh deploying and understanding kubernetes, we started seeing the potential that it's not reaching only the infrastructure itself, it's reaching all the layers, all the stack that we support in house and premises. And also it's opening up uh doors to easily scale into external resources as as well. So what we've been trying to tell our users is to rely on these integrations as much as possible. So this means like the application lifecycle being managed with things like Helmand getups, but also like the monitoring being managed with Prometheus and once you're happy with your deployment in house we have ways to scale out to external resources including public clouds. And this is really like see I don't know a proof that all these A. P. I. S are not only popular but incredibly useful because there's such a rich ecosystem around it. >>So talk about the role of data in this obviously machine learning pieces something that everyone is interested in as you get infrastructure as code and devops um and def sec ops as everything's shifting left. I love that, love that narrative day to our priests. All this is all proving mature, mature ization. Um data is critical. Right? So now you get real time information, real time data. The expectations for the apps is to integrate the data. What's your view on how this is progressing from your standpoint because machine learning and you mentioned you know acceleration or being part of another system. Cashing has always done that would say databases. Right. So you've got now is databases get slower, caches are getting faster now they're all the ones so it's all changing. So what's your thoughts on this next level data equation into kubernetes? Because you know stateless is cool but now you've got state issues. >>Yeah so uh yeah we we've always had huge needs for for data we store and I I think we are over half an exhibit of data available on the premises but we we kind of have our own storage systems which are external and that's for for like the physics data, the raw data and one particular charity that we had with our workloads until recently is that we we call them embarrassing parallel in the sense that they don't really need uh very tight connectivity between the different workloads. So if it's people always say tens of thousands of jobs to do some analysis, they're actually quite independent, they will produce a lot more data but we can store them independently. Machine learning is is posing a challenge in the sense that this is a training tends to be a lot more interconnected. Um so it can be a benefit from from um systems that we are not so familiar with. So for us it's it's maybe not so much the cashing layers themselves is really understanding how our infrastructure needs to evolve on premises to support this kind of workloads. We had some smallish uh more high performance computing clusters with things like infinite and for low latency. But this is not the bulk of our workloads. This is not what we are experts on these days. This is the transition we are doing towards uh supporting this machine learning workers >>um just as a reference for the folks watching you mentioned embarrassing parallel and that's a quote that you I read on your certain tech blog. So if you go to tech blog dot web dot search dot ch or just search cern tech blog, you'll see the post there um and good stuff there and in there you go, you lay out a bunch of other things too where you start to see the deployment services and customer resource definitions being part of this, is it going to get to the point where automation is a bigger part of the cluster management setting stuff up quicker. Um As you look at some of the innovations you're doing with machines and Coubertin databases and thousands of other point things that you're working on there, I mean I know you've got a lot going on there, it's in the post but um you know, we don't want to have the problem of it's so hard to stand up and manage and this is what people want to make simpler. How do you how do you answer that when people say say we want to make it easier? >>Yeah. So uh for us it's it's really automate everything and up to now it has been automate the deployment in the kubernetes clusters right now we are looking at automating the kubernetes clusters themselves. So there's some really interesting projects, uh So people are used to using things like terra form to manage the deployment of clusters, but there are some projects like cross playing, for example, that allows us to have the clusters themselves being resources within kubernetes. Uh and this is something we are exploring quite a bit. Uh This allows us to also abstract the kubernetes clusters themselves uh as uh as carbonated resources. So this this idea of having a central cluster that will manage a much larger infrastructure. So this is something that we're exploring the getups part is really key for us to, it's something that eases the transition from from from people that are used already to manage large scale systems but are not necessarily experts on core NATO's. Uh they see that there's an easier past there if they if they can be introduced slowly through through the centralized configuration. >>You know, you mentioned cross plane, I had some on earlier, he's awesome dude, great guy and I was smiling because you know I still have you know flashbacks and trigger episodes from the Hadoop world, you know when it was such so promising that technology but it was just so hard to stand up and managed to be like really an expert to do that. And I think you mentioned cross plane, this comes up to the whole operator notion of operating the clusters, right? So you know, this comes back down to provisioning and managing the infrastructure, which is, you know, we all know is key, right? But when you start getting into multi cloud and multiple environments, that's where it becomes challenging. And I think I like what they're doing is that something that's on your mind to around hybrid and multi cloud? Can you share your thoughts on that whole trajectory? >>Absolutely. So I actually gave an internal seminar just last week describing what we've been playing with in this area and I showed some demo of using cross plane to manage clusters on premises but also manage clusters running on public clouds. A. W. S. Uh google cloud in nature and it's really like the goal there. There are many reasons we we want to explore external resources. We are kind of used to this because we have a lot of sites around the world that collaborate with us, but specifically for public clouds. Uh there are some some motivations there. The first one is this idea that we have periodic load spikes. So we knew we have international conferences, the number of analysis and job requests goes up quite a bit, so we need to be able to like scale on demand for short periods instead of over provisioning this uh in house. The second one is again coming back to machine learning this idea of accelerators. We have a lot of Cpus, we have a lot less gPS uh so it would be nice to go on fish uh for those in the public clouds. And then there's also other accelerators that are quite interesting, like CPUs and I p u s that will definitely play a role and we probably, or maybe we will never have among premises, will only be able to to use them externally. So in that, in that respect, actually coming back to your previous question, this idea of storage then becomes quite important. So what we've been playing with is not only managing this external cluster centrally, but also managing the wall infrastructure from a central place. So this means uh, making all the clusters, whatever they are look very, very much the same, including like the monitoring and the aggregation of the monitoring centrally. And then as we talked about storage, this idea of having local storage that that will be allow us to do really quick software distribution but also access to the data, >>what you guys are doing as we say, cool. And relevant projects. I mean you got the large scale deployments and the machine learning to really kind of accelerate which will drive a lot of adoption in terms of automation. And as that kicks in when you got to get the foundational work done, I see that clearly the right trajectory, you know, reminds me ricardo, um you know, again not do a little history lesson here, but you know, back when network protocols were moving from proprietary S N A for IBM deck net for digital back in the history the old days the os I Open Systems Interconnect Standard stack was evolving and you know when TCP I P came around that really opened up this interoperability, right? And SAM and I were talking about this kind of cross cloud connections or inter clouding as lou lou tucker. And I talked that open stack in 2013 about inter networking or interconnections and it's about integration and interoperability. This is like the next gen conversation that kubernetes is having. So as you get to scale up which is happening very fast as you get machine learning which can handle data and enable modern applications really it's connecting networks and connecting systems together. This is a huge architectural innovation direction. Could you share your reaction to that? >>Yeah. So actually we are starting the easy way, I would say we are starting with the workloads that are loosely coupled that we don't necessarily have to have this uh tighten inter connectivity between the different deployments, I would say that this is this is already giving us a lot because our like the bulk of our workloads are this kind of batch, embarrassing parallel, uh and we are also doing like co location when we have large workloads that made this kind of uh close inter connectivity then we kind of co locate them in the same deployment, same clouds in region. Um I think like what you describe of having cross clouds interconnectivity, this will be like a huge topic. It is already, I would say so we started investigating a lot of service measure options to try to learn what we can gain from it. There is clearly a benefit for managing services but there will be definitely also potential to allow us to kind of more easily scale out across regions. There's we've seen this by using the public cloud. Some things that we found is for example, this idea of infinite, infinite capacity which is kind of sometimes uh it feels kind of like that even at the scale we have for Cpus But when you start using accelerators, Yeah, you start negotiating like maybe use multiple regions because there's not enough capacity in a single region and you start having to talk to the cloud providers to negotiate this. And this makes the deployments more complicated of course. So this, this interconnectivity between regions and clouds will be a big thing. >>And, and again, low hanging fruit is just a kind of existing market but has thrown the vision out there mainly to kind of talk about what what we're seeing which is the world's are distributed computer. And if you have the standards, good things happen. Open systems, open innovating in the open really could make a big difference is going to be the difference between real value for the society of global society or are we going to get into the silo world? So I think the choice is the industry and I think, you know, Cern and C and C. F and Lennox Foundation and all the companies that are investing in open really is a key inflection point for us right now. So congratulations. Thanks for coming on the cube. Yeah, appreciate it. Thank you. Okay, Ricardo, rocha computing engineer cern here in the cube coverage of the CN Cf cube con cloud, native con europe. I'm john for your host of the cube. Thanks for watching.

from around the globe it's thecube with coverage of kubecon and cloudnativecon north america 2020 virtual brought to you by red hat the cloud native computing foundation and ecosystem partners hey welcome back everybody jeff frick here with thecube coming to you from our palo alto studios for the continuing coverage of kubecon cloud native con 2020 north america there was the european version earlier in the summer it's all virtual uh so the good news is we don't have to get on planes and we can get guests from all over the world and we're excited to welcome back for his return to the cube ricardo rocha he is a staff member and computing engineer at cern ricardo great to see you hello thanks for having me absolutely and you're coming in from uh from geneva so you're you already had a good thursday i bet yeah we're just finishing right now yeah right so in in getting ready for this um interview i was looking at the interview that you did i think it was two cube cons ago uh in may of 2019 and it just strikes me a lot of people know what cern is but a lot of people don't know what's cern in so i wonder if you can just give you know kind of the 101 of what cern's mission is and what is some of the work that you guys do there yeah sure uh so cern is the european organization for uh nuclear research we are the largest particle physics laboratory in the world and our main mission is uh fundamental research so we try to answer big questions about why don't we see antimatter what is dark matter or dark energy other questions about the origin of the universe and to answer these questions we build very large machines particle accelerators where we try to recreate some of [Music] the moments just after the universe was created the big bang to try to understand better what was the state of the matter at that time the result of all of this is very often a lot of data that has to be analyzed and that's why we traditionally have had a huge requirements for computing resources during the the start of cern we always had this this large large requirements right and so you have this large particle accelerators as you said large machines the one that you've got now the the latest one how long has that one been operational yeah so it started uh like maybe around 10 years ago the first launch was a bit before that uh and it's uh it's a very large uh it's the largest one ever built so it's 27 kilometers in perimeter we inject protons into different uh directions and then we we make them collide where we build these huge detectors that can can see what's happening in these collisions uh the the main the main particle accelerator is this one we do have other experiments we have a nancy meta factory that is just uh down from my office and we have other types of experiments as well going right 27 kilometers that's a big that's a big number and then and then again just so people get some type of sense of scale so then you you you speed up the particles you smash them together you see what happens they collect all the data what types of data sets are generated off off just a one you know kind of event and i don't even know if that's a relative you know if that's a valid measure how do how do you measure kind of quantities of data around event just you know kind of for orders of magnitude right so uh the way it works is as you said we accelerate the particles to very close to the speed of light and we increase the energy by by having the beams well controlled and then at specific points we make them collide we have this gigantic detectors underground all of this is 100 meters in the ground and these detectors are pretty much a very large camera that would take something like 40 million pictures a second and the result of this is a huge amount of data each of these detectors can generate up to one petabyte of second this is not something we can record so what we do is we have hardware filters that will bring this down to something we can manage which is in the order of a few tens of gigabytes per second wow so you've been you've got a very serious computing challenge ahead of you because you're the one that's on the hook for for grabbing the data recording the data making the data available for for people to use um on their experiments um so we're here at kubecon cloud native con where did containers come into the story uh and and kubernetes specifically what was the real uh challenge that you're trying to overcome yeah so uh this is a a long story of uh using distributed computing at cern and other types of computing so as i mentioned we generate a lot of data we generate something like 7 but of 70 petabytes of data every year and we accumulated something over one half an exabyte of data by now so uh traditionally we've had to build this software ourselves um which was uh because there was not so many people around that would have this kind of needs but this revolution with containers and the clouds appearing kind of allowed us to to join other other communities and benefit also from their work and not have to do everything ourselves so this is the main probe for us to start doing this the other point is more containerization we traditionally are very we have a lot of needs to share information but also share resources between physicists and engineers so this idea of containerizing the work including all the code all the data and then sharing this with our colleagues is very appealing the fact that we can also take this unit of work and just deploy it in any infrastructure that has a standardized api like kubernetes and scale that monitoring the same way it's also very appealing so all of these things kind of connect with our way of working our natural way of working i would say right so you've talked about the this upgrade is coming um to the particle accelerator in a couple four or five years whatever that timeline is relatively soon um this as you've said before is a huge step function in the data that's that that's going to come off these experiments i mean how are you keeping up on the compute side with the fundamental shift in on kind of the physics side and the data that's going to be generated to make sure that you can keep up and i think you said it in a prior interview somewhere along the way that you know you don't want to be the bottleneck when there's all this great work being done but if it's not captured and made available for people to do stuff with the data then you know it's not uh it's not the greatest experiment so how are you keeping up and and what's the relative scale to have what you got to do on the compute side to keep up with the the guys on the physics side yeah so the the the idea well we what we will have to deal with is an increase of 10 times of more data than we have today we already have a lot and very soon we'll have a lot more but this is not i would say this is not the first time this kind of uh step happens uh in our computing we always kind of found a new technology or a new way to do things that would improve in in this case uh what we do is we do what we always do which is we try to look for all sorts of new technologies or all sorts of new resources that we could make use of in this case a lot is involving improving our own software to replace what we currently use with hardware triggers to replace that with software-based using accelerators gpus and other types of accelerators this will play a big role and also making our software more efficient in this way the second thing that we are doing is trying to make our infrastructure more agile and this is where cloud native kubernetes plays a huge role so that we can benefit from external resources uh we we can always think of like expanding our in on-premises resources but it's also very good to be able to just go and fish around if there's something available externally kubernetes plays a very big role in that respect as well yeah i'd love to dig into that a little deeper because the cloud native foundation is a super active foundation obviously a ton of activity around kubernetes so what does that mean to you as an infrastructure provider you know to your own company being on the hook to have now you know kind of an open source community that's supporting you indirectly via ongoing developments and ongoing projects and having as you said kind of this broader group of brain power to pull from to help you move your own infrastructure along yeah i think this this is great we've had really good experiences in the past we've been uh heavy users of uh linux from from from for a very long time we've used openstack for our private cloud and we've been heavily involved in that community as well we not only uh contribute as end users but we also uh offer some some manpower for development and helping with the community and we are doing the same with kubernetes uh and this is uh this is really we we end up getting a lot more than we we are putting in the community we are quite involved but uh it's so large and and and with such big players that have very similar needs to ours that uh we end up having a lot a lot more back than we are putting in we try to help as much as possible but uh yeah we have limited resources as well now open source is an amazing it's just an amazing innovation uh machine and and obviously it's proved as its value over a lot of things from linux to kubernetes being one of the most recent i want to shift gears a little bit right and ask you just your your take on public cloud right one of the huge benefits of public cloud is is the flexibility to add capacity shrink capacity as you need it and you talked again in a prior thing i was looking at you know that you definitely have spikes uh in demand spikes whether there's a high frequency of experiments i don't know how frequently you run those things versus maybe a conference or something where you said people you know want to get access to the data run experiments prior to your conference do you where does public cloud play in your thoughts and maybe you're there today maybe you're not how do you think about you know kind of public cloud generically but more specifically you know that ability to add a little bit more flex in your compute horsepower or are you just going up into the right up into the right and not really flexing down very much yeah so this is this is something we've been working on for a few years now uh we it's uh it's uh it's i would say it's an ongoing work it's a situation that will will not uh be very clear for the for the next few years but again what what we try to do is just to explore as much as possible all kinds of resources that can help us what we did in the kubecon last year was this demonstration that we can actually scale we can scale out and burst for for this uh spiky workloads we have we can burst to the to the public cloud quite easily using this kind of cloud native technologies that we have today and this is extremely important because it kind of changes our mindset instead of having to to think only on investing on premises we can think that maybe we can cover for the majority of use cases but then explore and burst to the public cloud this has to be easy in terms of infrastructure and that we are at that point right now with kubernetes we also have kind of workload that is maybe easier to do these things than than a traditional i.t where services are very interconnected in our case we are more thinking of batch workloads where we can just submit jobs uh and then fetch the data back right this also has a few challenges but but it's i would say it's it's easier than the traditional ite service deployments the other aspect where the public cloud is also very interesting is uh for resources that we don't have in large quantities so we have a very large farm for with cpus we have some gpus and it's very good to be able to explore this new accelerator technologies and maybe expand our available pool of accelerators by going to the public cloud maybe to use them but also to validate to see which ones are best for our use cases and explore that option as well it's not only general capacity it's really like dedicated um hardware that we might not even have ever like we think of tpus or ipu's it's something that is very interesting that we can scale and just go go use them in the public cloud yeah that's a really interesting point because because the cloud providers are big enough now right that they're building all kind of specialized specialized server specialized uh cpu specialized gpus dpus is a new one i've heard a data processing unit as you said there's fpgas and all kinds of accelerators so it is a really rich environment for as you said to do your experiments and find what the optimal solution is for whatever that particular workload is but ricardo i want to shift gears a little bit as we come to the end of 2020 thankfully for a whole bunch of reasons as you look forward to 2021 i mean clearly anticipating and starting to plan to get ready for your upgrade as a priority i'm just curious what are your other priorities and how does you know kind of the compute infrastructure in terms of an investment within cern you know kind of rank with the investment around the physical things that you're building the big machines because without the compute those other things really don't provide much data and i know those are we always talked about how expensive the particle accelerators is it's an interesting number and it's big but you guys are a big piece of that as well so what are your priorities looking forward to 2021 yeah from from the compute side i think we are keeping the the priorities in similar to what we've been doing the last few years which is to make sure that we improve all our automation to improve efficiency as well to prepare for these upgrades we have but also there's a lot of activity in this new uh area with machine learning popping up we have a ton of services appearing where people want to to start doing machine learning in many many use cases in some cases they want to do the filtering in the detectors in other cases they want to generate simulation data a lot faster using machine learning as well so i think this will be something that will be a huge topic for next year even for the next couple of years which is to see how we can offer our users and physicists the best service so that they don't have to care about the infrastructure they don't have to know about the details of how they scale their their model training their serving of their models all of this i think this will be a very big topic um it's something that it's becoming really a big part of of the world computing for high energy physics and for cern as well that's great we see that a lot you know just applied machine learning to very specific problems you talked about you still can't even record all that information that comes off those things you have to do some compression technology and other things so real opportunities barely scratched on the surface of machine learning and ai but i'm sure you're going to be using it a ton well ricardo give you give you the last word um we're in at cncf's uh kubecon cloud native con you know what do you get out of these types of shows and why is this such again kind of why is it such an important piece of your way you get your job done yeah honestly uh with all this uh situation right now i kind of really miss this kind of conferences in person uh it's really a huge opportunity to connect with uh with the other end users but also with with the community and to talk to the developers discuss things over uh coffee beer this is something that is really something that is really useful to to have this kind of meetings every year uh i think what what uh i always try to say is uh this this wall infrastructure is is truly making a big impact in the way we do things so we can only thank the community uh it's it allows us to to kind of shift to focusing on a higher level to focus more on our use cases instead of having to focus so much on the infrastructure we kind of start giving it as a given that the infrastructure scales and we can just use it and focus on optimizing our own software so this is a huge contribution we can only thank the cncf projects and everyone involved great well thank you for that uh that summary and that that's a terrific summary so ricardo thank you so much for all your hard work answering really big helping answer really big questions and uh and for joining us today and sharing your insight thank you very much all right he's ricardo i'm jeff you're watching the cube from our palo alto studios for continuing coverage of kubecon cloud nativecon 2020. thanks for watching see you next time [Music] you

>> Live from Barcelona, Spain, it's theCUBE, covering KubeCon + CloudNativeCon Europe 2019. Brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem Partners. >> Welcome back to theCUBE, here at KubeCon CloudNativeCon 2019 in Barcelona, Spain. I'm Stu Miniman. My co-host is Corey Quinn and we're thrilled to welcome to the program two gentlemen from CERN. Of course, CERN needs no introduction. We're going to talk some science, going to talk some tech. To my right here is Ricardo Rocha, who is the computer engineer, and Lukas Heinrich, who's a physicist. So Lukas, let's start with you, you know, if you were a traditional enterprise, we'd talk about your business, but talk about your projects, your applications. What piece of, you know, fantastic science is your team working on? >> All right, so I work on an experiment that is situated with the Large Hadron Collider, so it's a particle accelerator experiments where we accelerate protons, which are hydrogen nuclei, to a very high energy, so that they almost go with the speed of light. And so, we have a large tunnel underground, 100 meters underground in Geneva, so straddling the border of France and Switzerland. And there, we're accelerating two beams. One is going clockwise. The other one is going counterclockwise, and there, we collide them. And so, I work on an experiment that kind of looks at these collisions and then analyzes this data. >> Lukas, if I can, you know, when you talk to most companies, you talk about scale, you talk about latency, you talk about performance. Those have real-world implications for your world. Do you have anything you could share there? >> Yeah, so, one of the main things that we need to do, so we collide 40 million times a second these protons, and we need to analyze them in real time, because we cannot write out all the collision data to disk because we don't have enough disk space, and so we've essentially run 10,000 core real-time application to analyze this data in real-time and see what collisions are actually most interesting, and then only those get written out to disk, so this is a system that I work on called The Trigger, and yeah, that's pretty dependent on latency. >> All right, Ricardo, luckily you know, your job's easy. We say most people you need to respond, you know, to what the business needs for you and, you know, don't worry, you can't go against the laws of physics. Well, you're working on physics here, and boy those are some hefty requirements there. Talk a little bit about that dynamic and how your team has to deal with some pretty tough challenges. >> Right, so, as Lukas was saying, we have this large amount of data. The machines can generate something around the order of a petabyte a second, and then, thanks to their hardware- and software-level triggers, they will reduce this to something that is 10 gigabytes a second, and that's what my side has to handle. So, it's still a lot of data. We are collecting something like 70 petabytes a year, and we keep adding, so right now we have, the amount of storage available is on the order of 400 petabytes. We're starting to get at a pretty large scale. And then we have to analyze all of this. So we have one big data center at CERN, which is 300,000 cores, or something like this, around that, but that's not enough, so what we've done over the last 15, 20 years, we've created this large distributed computing environment around the world. We link to many different institutes and research labs together, and this doubles our capacity. So that's our challenge, is to make sure all the effort that the physicists put into building this large machine, that, in the end, it's not the computing that is breaking the world system. We have to keep up, yup. >> One thing that I always find fascinating is people who are dealing with real problems that push our conception of what scale starts to look like, and when you're talking about things like a petabyte a second, that's beyond the comprehension of what most of us can wind up talking about. One problem that I've seen historically with a number of different infrastructure approaches is it requires a fair level of complexity to go from this problem to this problem to this problem, and you have to wind up working through a bunch of layers of abstraction, and the end result is, and at the end of all of this we can run our blog that gets eight visits a day, and that just doesn't seem to make sense. Whereas what you're talking about, that level of complexity is more than justified. So my question for you is, as you start seeing these things evolve and looking at other best practices and guidance from folks who are doing far less data-intensive applications, are you seeing that a lot of the best practices start to fall down as you're pushing theoretical boundaries of scale? >> Right, that's actually a good point. Like, the physicists are very good at getting things done, and they don't worry that much about the process, as long as in the end it works. But there's always this kind of split between the physicists and the more computing engineer where the practices, we want to establish practices, but at the end of the day, we have a large machine that has to work, so sometimes we skip a couple of steps, but we still need, there's still quite a lot of control on like data quality and the software validation and all of this. But yeah, it's a non-traditional environment in terms of IT, I would say. It's much more fast pacing than most traditional companies. >> You mentioned you had how many cores working on these problems on site? >> So in-house, we have 300,000. >> If you were to do a full migration to the public cloud, you'd almost have to repurpose that many cores just to calculating out the bill at that point. Just, because all the different dimensions, everything winds working on at that scale becomes almost completely non-trivial. I don't often say that I'm not sure public cloud can scale to the level that someone would need to. In your case, that becomes a very real concern. >> Yeah, so that's one debate we are having now, and it's, it has a lot of advantages to have the computing in-house, and also because we pretty much use it 24/7, it's a very different type of workload. So we need a lot of resources 24/7, like even the pricing is kind of calculated differently. But the issue we have now is that the accelerator will go through a major upgrade just in five years' time, where we will increase the amount of data by 100 times. Now we are talking about 70 petabytes a year and we're very soon talking about like exabytes. So the amount of computing we'll need there is just going to explode, so we need all the options. We're looking into GPUs and machine learning to change how we do computing, and we are looking at any kind of additional resources we might get, and there the public cloud will probably play a role. >> Could you speak to kind of the dynamic of how something like an upgrade of that, you know, how do you work together? I can't imagine that you just say, "Well, we built it, "whatever we needed and everything, and, you know, "throw it over the wall and make sure it works." >> Right, I mean, so I work a lot on this boundary between computing and physics, and so internally, I think we also go through the same processes as a lot of companies, that we're trying to educate people on the physics side how to go through the best practices, because it's also important. So one thing I stressed also in the keynote is this idea of reproducibility and reusability of scientific software is pretty important, so we teach people to containerize their applications and then make them reusable and stuff like that, yup. >> Anything about that relationship you can expound on? >> Yeah, so like this keynote we had yesterday is a perfect example of how this is improving a lot at CERN. We were actually using data from CMS, which was one of the experiments. Lukas is a physicist in ATLAS, which is like a computing experiment, kind of. I'm in IT, and like all this containerized infrastructure kind of is getting us all together because computing is getting much easier in terms of how to share pieces of software and even infrastructure, and this helps us a lot internally also. >> So what particular about Kubernetes helps your environment? You talk for 15 years that you've been on this distributed systems build-out, so sounds like you were the hipsters when it came to some of these solutions we're working on today. >> That has been like a major change. Lukas mentioned the container part for the software reproducibility, but I have been working on the infrastructure for, I joined CERN as a student and I've been working on the distributed infrastructure for many years, and we basically had to write our own tools, like storage systems, all the batch systems, over the years, and suddenly with this public cloud explosion and open source usage, we can just go and join communities that have requirements sometimes that are higher than ours and we can focus really on the application development. If we base, if we start writing software using Kubernetes, then not only we get this flexibility of choosing different public clouds or different infrastructures, but also we don't have to care so much about the core infrastructure, all the monitoring, log collection, restarting. Kubernetes is very important for us in this respect. We kind of remove a lot of the software we were depending on for many years. >> So these days, as you look at this build-out and what you're looking, not just what you're doing today but what you're looking to build in the upcoming years, are you viewing containers as the fundamental primitive of what empowers this? Are you looking at virtual machines as that primitive? Are you looking at functions? Where exactly do you draw the abstraction layer, as you start building this architecture? >> So, yeah, traditionally we've been using virtual machines for like the last maybe 10 years almost, or, I don't know, eight years at least, and we see containerization happening very quickly, and maybe Lukas can say a bit more about the physics, how this is important on the physics side? >> Yeah, what's been, so currently I think we are looking at containers for the main abstraction because it's also we go through things like functions as a service. What's kind of special about scientific applications is that we don't usually just have our entire code base on one software stack, right? It's not like we would deploy Node.js application or Python stack and that's it. And so, sometimes you have a complete mix between C++, Python, Fortran, and all that stuff. So this idea that we can build the entire software stack as we want it is pretty important. So even for functions as a service where, traditionally, you had just a limited choice of runtimes, this becomes important. >> Like, from our side, the virtual machines still had a very complex setup to be able to support all this diversity of software and the containerization, just all the people have to give us is like run this building block and it's kind of a standard interface, so we only have to build the infrastructure to be able to handle these pieces. >> Well, I don't think anyone can dispute that you folks are experts in taking larger things and breaking them down into constituent components thereof. I mean, you are, quite obviously, the leading world experts on that. But was there any challenge to you as you went through that process of, I don't necessarily even want to say modernizing, but in changing your viewpoint of those primitives as you've evolved, have you seen that there were challenges in gaining buy-in throughout the organization? Was there pushback? Was it culturally painful to wind up moving away from the virtual machine approach into a containerized world? >> Right, so yeah, a bit, of course. But traditionally we, like physicists really focus on their end goal. We often say that we don't count how many cores or whatever, we care about events per second, how many events we can process per second. So, it's a kind of more open-minded community maybe than traditional IT, so we don't care so much about which technology we use at some point, as long as the job gets done. So, yeah, there's a bit of traction sometimes, but there's also a push when you can demonstrate that we get a clear benefit, then it's kind of easier to push it. >> What's a little bit special maybe also for particle physics is that it's not only CERN that is the researcher. We are an international collaboration of many, many institutes all around the world that work on the same project, which is just hosted at CERN, and so it's a very flat hierarchy and people do have the freedom to try out things and so it's not like we have a top-down mandate what technology we use. And then somebody tries something out. If it works and people see a value in it then you get adoption from it. >> The collaboration with the data volumes you're talking about as well has got to be intense. I think you're a little bit beyond the, okay, we ran the experiment, we put the data in Dropbox, go ahead and download it, you'll get that in only 18 short years. It seems like there's absolutely a challenge in that. >> That was one of the key points actually in the keynote is that, so a lot of the experiments at CERN have an open data policy where we release our data, and so that's great because we think it's important for open science, but it was always a bit of an issue, like who can actually practically analyze this data for people who don't have a data center? And so one part of the keynote was that we could demonstrate that using Kubernetes and public cloud infrastructure actually becomes possible for people who don't work at CERN to analyze this large-scale scientific data sets. >> Yeah, I mean maybe just for our audience, the punchline is rediscovering the Higgs boson in the public cloud. Maybe just give our audience a little bit of taste of that. >> Right, yeah, so basically what we did is, so the Higgs boson was discovered in 2012 by both ATLAS and CMS, and a part of that data, we used open data from CMS and part of that data has now been released publicly, and basically this was a 70-terabyte data set which we, thanks to our Google Cloud partners, could put onto public cloud infrastructure and then we analyzed it on a large-scale Kubernetes cluster, and-- >> The main challenge there was that, like, we publish it and we say you probably need a month to process it, but we had like 20 minutes on the keynote, so we kind of needed a bit larger infrastructure than usual to run it down to five minutes or less. In the end, it all worked out, but that was a bit of a challenge. >> How are you approaching, I guess, making this more accessible to more people? By which I mean, not just other research institutions scattered around the world, but students, individual students, sometimes in emerging economies, where they don't have access to the kinds of resources that many of us take for granted, particularly work for a prestigious research institutions? What are you doing to make this more accessible to high school kids, for example, folks who are just dipping their toes into a world they find fascinating? >> We have entire programs, outreach programs that go to high schools. I've been doing this when I was a student in Germany. We would go to high schools and we would host workshops and people would analyze a lot of this data themselves on their computers. So we would come with a USB stick that have data on them, and they could analyze it. And so part of also the open data strategy from ATLAS is to use that open data for educational purposes. And then there are also programs in emerging countries. >> Lukas and Ricardo, really appreciate you sharing the open data, open science mission that you have with our audience. Thank you so much for joining us. >> Thank you. >> Thank you. >> All right, for Corey Quinn, I'm Stu Miniman. We're in day two of two days live coverage here at KubeCon + CloudNativeCon 2019. Thank you for watching theCUBE. (upbeat music)

>> Announcer: Live from Orlando, Florida, it's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to theCUBE's live coverage of PentahoWorld brought to you by Hitachi Vantara. I'm your host Rebecca Knight, along with my cohost Dave Vellante. We are joined by Derek Mathieson, he is the group leader at CERN. Welcome, Derek, glad to have you on the show. >> Well, glad to be here, thank you very much. >> So, CERN, which is of course the European Organization for Nuclear Research. And you know we think of it as this place of physicists and engineers working together to solve these problems. And probe the mysteries of the universe but in fact, CERN is a technology organization. >> Absolutely, I mean, I think that's the- CERN has this reputation of being exclusively physics. I mean, it is the world leading particle physics laboratory. But in fact, in the end, yeah, we're an infrastructure organization who provides all the technology, all the science. And all the scientists and engineers come to CERN to do their work. But CERN itself provides the facilities. So, our main focus, in fact, is technology. Computer science, civil engineering, construction. I mean, we built cathedral size concrete structures 400 and 50 feet underground, 17 mile long tunnels. I mean, this is civil engineering in the grand scale. And that's actually one of the major focuses. Is that CERN, although it's a physics organization, one of the difficulties we have as an organization is to explain to people, in fact, what we're looking for when we're recruiting. When we're contacting other universities. It's all about the fact that we're not looking for physicists, we're looking for engineers and technology specialists to come and work at CERN. >> So talk to us about some of the new, exciting projects that you're working on there. >> Oh, I mean, there's a lot going on. Obviously, the reason I'm here today is all about the work that we're doing with Pentaho. So we're, you know, building a new data warehouse. My group's actually responsible for the administrative computing of CERN. So basically running CERN as a business. I mean this is, there's a budget of around about one billion U.S. dollars. Going into CERN every year, in order to do all this physics research. So obviously we have a responsibility to treat, be faithfully to these tax dollars, carefully and you know spend them wisely. So a lot of my work is to make sure that we have the appropriate infrastructure, controls and proper technology there. To make sure that it's used effectively and wisely. >> So paint a picture of that infrastructure for us, if you would. What's it look like if we took a peak under the tent? Well, I mean, it's what quite nice about it is with the technology infrastructure that we have. So we have a huge computer center. There's a hundred thousand CPU's in our computer center. That's mainly used for doing physics but because we have all this infrastructure there, we can use part of it to also run the administration. Which gives us the ability to run a real world class technology stack to actually run the organization. So we have a huge data warehouse. Which gives a very rapid response to the physicists and engineers who actually want to go on and do their work. My job is to make sure that the administration of CERN doesn't get in their way. So we want to provide them the facilities so they just get on with their job and all the other things to do with actually running the organization are my problem and the team that works for me. And good examples is that CERN literally sits on the border between France and Switzerland. So we have, you know, we care about things like, there's 80 different customs forms that we have to worry about on a daily basis just as we move materials around the site. So we have such an usual organization but it's unique in the world. And that's what attracts people to work there is all these new challenges that we got. It's really a fantastic place. >> And the view is pleasant I bet. >> Oh yeah. (all giggling) >> Okay, so tell us more about the infrastructure. So you talked about this really fast data warehouse. 100,000 CPUs, is it all sort of on prem? Is it a mix sort of on prem and the Cloud? What's the data warehouse, you know, give us a sense of what that infrastructure is. 'Cause people hear data warehouse, they think you know, kind of old, clunky data warehouse. You're talking about this super high performance. >> Exactly, in fact, that's one of the challenges that we face is. We've got scientists who are used to dealing with high volumes of data with high fixation. Our particle detectors produce around 2 petabytes of data per second. So they're used to dealing with large amount of data. So immediately when they started looking at the administration of the organization of the same high expectations. They want it to be fast, they want it to process the data. Large quantities of data, very quickly indeed and give the answers (snaps) in a split second. So to do that we have to obviously put quite a lot of hardware behind it and also use good technical strength as well. We're quite big users of Oracle at CERN. We have a big Oracle database which is for the principle, where we keep most of our data. And then we use Pentaho on top of that in order to do all the deporting, the analytics, the building the Cube, so all this kind of thing. And their user base is very transient. So there's around fifteen thousand people who're actually working at CERN at any one time. Half of the world's particle physicists work at CERN. >> Rebecca: Wow. >> So, they're coming and going all the time. They don't want to worry about how to get the data. So it has to be there, has to be there right away. Has to be easy to use and easy to understand. These people live and work and breathe particle physics. They don't worry about the budget and the details about how to do all this stuff. This is something where the accountants have to get there. Get it in such a way that it's easy for them to do the right thing and make sure that we stay compliance with the various regulations. And make sure that the organization continues to function as a business while still getting on with our primary mission of particle physics research. >> And that infrastructure is primarily on premise, that correct? >> It's on premise, the vast majority of it. In fact, one of the, we have two main data centers. So there's one physically located at Cern in Geneva. And then there's another one over in the (mumbles) institute, in (snaps) >> The other place. >> The other place. (both laughing) >> Okay. >> Yep. >> And that, presume, because you've got such volumes of data. You can't just be moving that stuff around up into the Cloud. >> Right, in fact yeah, we have a lot of high speed data links between the different data centers in order to. We have a copy of quite a lot of the data in fact. The principle physics data is copied, not only at CERN, which is what's called a 2-0 site where we have all the data to start with. But we also copy it to I think it's around about seven different institutes around the world. So they have a first-line copy as well. Altogether we have a network of around a hundred computer centers working for CERN in some way or other. That's part of what we call the LHC computing grids which is (mumbles) a planetary data center in computer infrastructure to do all this processing of the LHC data. >> I'm going to ask you to go back to about the organizational structure. I mean, you described this office situated on the border of France and Switzerland. Where half the world's particle physicists work. What is the culture like? And how do you get- and as you said also the administrations job is to really get out of their way so they can do their thing. What is the culture like there? How do people work together? How do people collaborate? What do you do when there's disagreement? >> I mean this is one of the unique aspects of CERN. Is bringing people together. There's around about 90 different countries represented at CERN. Around about 100 different nationalities, all working on site. It's very much like a university environment. We have a canteen where people will come in. Their always saying that probably most of the physics and most of the science discoveries are happening within the canteen as people meet together from all over the world. We have countries, India, Pakistan, have just joined as associate members. We've got 22 member states. Mainly around Europe but now we have a policy enlargement. So we're actually trying to make the organization even larger. Touching more countries around the world. United States is an observer now within the organization. So they actually participate in the CERN council and they're also major players in some of the large LHC experiments as well. But yeah, on a day to day basis, I'll be sitting in the restaurant and there will be Nobel Prize winners. We have our director general, she will be there as well, having lunch with everyone else. So it's a very much a leveling organization where everyone feels free to speak to each other. And discuss the matters of the day and particle physics. >> So what do you guys talk about? >> (laughs) What's the canteen conversation? >> I think this is the utter geek speak usually. That's the main problem in CERN is that people are passionate about what they do. So they come to CERN, they love what they do, they talk about it all the time. So, I mean, people will be talking about the latest generation of the CPU architecture, GPU programming. How do we do simulations with petabytes of data? This is lunch time conversation. And evening and everything else. >> So you're not talking about the a football game, right? You're talking about this sort of, talking shop mostly right? >> There is a football team, there is a rugby team as well. There's real life as well at CERN but yeah, I mean, most people are there because they're passionate about what they do. >> Obviously you're listening to those conversations you must pick up a lot of it. >> Yeah, I know, I mean, I think it's if you work at Cern and you're at a dinner party, someone laughs, "Oh you work at Cern, tell me all about physics." So you pick up a bit about it of course. Everyone can speak a little bit about what we're doing at Cern and I think that's an imperative because we work there. Of course you hear about what's going on and understand a little bit about it. But I would never claim to be a physicist of course. >> Rebecca: You can fake it though. >> I have lunch with physicists, I'm not one myself. >> How 'about Pentaho? You painted the picture of the infrastructure before. Where does Pentaho fit? And how are they adding value? >> We've been using Pentaho now for the last few years. We started, I mean, what really attracted is actually this combination of open-source plus propriety software. We like the core and the open-source nature of it which it very much fits with the values of CERN as well as being an open lab. And sharing everything that we do. So we started, as I say, with Pentaho a few years ago. Now, it's a core component. It's a core strategic component of the administration and also used in other areas as well. So it's also used in some of the more technical infrastructure areas in terms of: how do we actually run the lab? Parts of the infrastructure in terms of monitoring the different parts of the accelerator complex. And even in terms of, you know, the maintenance of the buildings, all of that. So it's really, you know, core within the organization as a core component for us. >> So, CERN is an organization then as- I'll use the word insistent, if you will, on open-source as a component. So that puts pressure on companies like Pentaho to pay attention to the next project. Maybe contribute, maybe not. But it certainly integrate. Score card, how have they done on that? What would you like to see them do better in that regard? And what kind of open-source projects do you- and you may not be able to answer this. But, might your organizations see in the horizon that you want Pentaho to capture? I mean, obviously 8.0, you've heard about, Spark and bringing in Kafka and the like. But maybe you could comment. >> Absolutely, I think this is one of the eighters who's really attracted us was the open-source nature. And certainly Pentaho's movement in that direction particularly, I think, was the integration with Hitachi as well. They're seeing many other projects now being integrated within to that sort of pentacle world. This is something that was interesting to us. Of course because of our Cloud based infrastructure. The idea of scaling up and scaling out. And they're going with the open-source projects to particular and the patchy projects. Which was really interesting to us as well. Something that we've been working on a bit ourselves. And now to hear that Pentaho was doing that as well. That was great, a good piece of news for me because it was something that we have been struggling with is basically spreading out. We've got fifteen thousand users. We want to have a dynamic infrastructure where we can actually provision more service where necessary in order be able to take load when we need it. But at the same time we don't want to waste the resources when they're off doing something else. >> Over the course of last decade, let's say, has there ever been a tendency for- 'cause you've got so many alpha geeks running around. To say, "Hey, I can take these open-source components and kind of do it myself." >> Derek: Yeah. >> "I don't need the Pentaho load bouncer, I got yarn to negotiate my resources. Look what I built." And so, how do you manage that? >> No, I mean, you're absolutely right. It's a problem here there's always the risk of the naught of engineer syndrome where, "I could do it better." And we have to pressure against that. But, I mean, I think the important of the issue is take the bigger picture. If it's already done well, we don't need to do it again. Build on top of it, make something better on top of something that already exists. And that's the thing, that's the message that we can give to any of the engineers working at CERN. Is, "You can do so much more if you already use the infrastructure that's already solid." And that's part of this, you know, reuse, of course. Open-source software allows us to build on things which are already solid. We don't need to make another one of them. We'll make something on top of it. That's a primary message that we try to give. >> So here we are at Pentaho World and you're with a bunch of other practitioners. Sharing best practices, talking about how you use the product, learning from them too. What are some of the take aways? And how much are you actually talking to them versus talking to the Pentaho product people? >> We did a presentation yesterday. The focus of our presentation was managing Pentaho. So, one of the things that we've been using now for a number of years is you have to have an infrastructure to be able to actually take care of all the different artifacts, all the different reports. We have many, many different user who want to be able to use Pentaho at the same time creating their own artifacts. I mean we have to have some way of managing to actually manage all this landscape. Although Pentaho has got some tools necessary, that was one of the areas that we felt we could add some value in there. So we've been building on top of the existing Pentaho APIs. Building an infrastructure to make it easier to support for other people. And what was quite nice is we were speaking to some of the other attendees. And that's exactly the kind of thing they've been worrying about as well. And there was even some presentations of people doing a similar approach in their own organizations. On how they were actually trying to build some kind of architecture on top of Pentaho just to manage the whole thing. When you have hundred of reports and hundred of artifacts and very complicated data warehouse cubes, you need something on top of that to actually just manage the whole thing. And that's something that we've been focused on. And I see other people are doing the same kind of thing. So I can imagine that Pentaho will be taking note of this and probable incorporating some of the ideas. >> It's sending a loud and clear message to Pentaho, yes absolutely. >> How about the event? You've been to at least two or that I know of. I don't know if you were at the original. >> I've been to three altogether. >> Okay, so you've been to, I think all of them, right? >> I could have been all of them, yeah. >> I think the first one was 14, I think, I'm pretty sure. Things you've taken away? You know, interesting conversations? >> I think it's the main reason we come in. It's a long way for us to come all the way from Geneva to come here. It's really important for us to touch base with other people using the product. It is an open community, people do like to talk to each other about, you know the new things that are happening within the Pentaho community. And I think face to face contact, in the end, is very hard to beat. And we're coming to an event like this you actually get the opportunity to speak to people over lunch. Or in the evening events you can talk to them and actually find out what it's really like to use Pentaho. >> Great, well thank you so much Derek for coming on theCUBE. >> Thank you very much. >> I'm Rebecca Knight for Dave Vellante. We well have more from Pentaho World just after this.

(upbeat music) >> Announcer: theCUBE presents KubeCon and CloudNativeCon Europe 2022, brought to you by Red Hat, the Cloud Native Computing Foundation, and its ecosystem partners. >> Welcome to Valencia, Spain, in KubeCon CloudNativeCon Europe 2022. I'm your host Keith Townsend, along with Paul Gillon. And we're going to talk to some amazing folks. But first Paul, do you remember your college days? >> Vaguely. (Keith laughing) A lot of them are lost. >> I think a lot of mine are lost as well. Well, not really, I got my degree as an adult, so they're not that far past. I can remember 'cause I have the student debt to prove it. (both laughing) Along with us today is Kenneth Hoste, systems administrator at Ghent University, and Marcel Hild, senior manager software engineering at Red Hat. You're working in office of the CTO? >> That's absolutely correct, yes >> So first off, I'm going to start off with you Kenneth. Tell us a little bit about the research that the university does. Like what's the end result? >> Oh, wow, that's a good question. So the research we do at university and again, is very broad. We have bioinformaticians, physicists, people looking at financial data, all kinds of stuff. And the end result can be very varied as well. Very often it's research papers, or spinoffs from the university. Yeah, depending on the domain I would say, it depends a lot on. >> So that sounds like the perfect environment for cloud native. Like the infrastructure that's completely flexible, that researchers can come and have a standard way of interacting, each team just use it's resources as they would, the Navana for cloud native. >> Yeah. >> But somehow, I'm going to guess HPC isn't quite there yet. >> Yeah, not really, no. So, HPC is a bit, let's say slow into adopting new technologies. And we're definitely seeing some impact from cloud, especially things like containers and Kubernetes, or we're starting to hear these things in HPC community as well. But I haven't seen a lot of HPC clusters who are really fully cloud native. Not yet at least. Maybe this is coming. And if I'm walking around here at KubeCon, I can definitely, I'm being convinced that it's coming. So whether we like it or not we're probably going to have to start worrying about stuff like this. But we're still, let's say, the most prominent technologies of things like NPI, which has been there for 20, 30 years. The Fortran programming language is still the main language, if you're looking at compute time being spent on supercomputers, over 1/2 of the time spent is in Fortran code essentially. >> Keith: Wow. >> So either the application itself where the simulations are being done is implemented in Fortran, or the libraries that we are talking to from Python for example, for doing heavy duty computations, that backend library is implemented in Fortran. So if you take all of that into account, easily over 1/2 of the time is spent in Fortran code. >> So is this because the libraries don't migrate easily to, distributed to that environment? >> Well, it's multiple things. So first of all, Fortran is very well suited for implementing these type of things. >> Paul: Right. >> We haven't really seen a better alternative maybe. And also it'll be a huge effort to re-implement that same functionality in a newer language. So, the use case has to be very convincing, there has to be a very good reason why you would move away from Fortran. And, at least the HPC community hasn't seen that reason yet. >> So in theory, and right now we're talking about the theory and then what it takes to get to the future. In theory, I can take that Fortran code put it in a compiler that runs in a container? >> Yeah, of course, yeah. >> Why isn't it that simple? >> I guess because traditionally HPC is very slow at adopting new stuff. So, I'm not saying there isn't a reason that we should start looking at these things. Flexibility is a very important one. For a lot of researchers, their compute needs are very picky. So they're doing research, they have an idea, they want you to run lots of simulations, get the results, but then they're silent for a long time writing the paper, or thinking about how to, what they can learn from the results. So there's lots of peaks, and that's a very good fit for a cloud environment. I guess at the scale of university you have enough diversity end users that all those peaks never fall at the same time. So if you have your big own infrastructure you can still fill it up quite easily and keep your users happy. But this busty thing, I guess we're seeing that more and more or so. >> So Marcel, talk to us about, Red Hat needing to service these types of end users. That it can be on both ends I'd imagine that you have some people still in writing in Fortran, you have some people that's asking you for objects based storage. Where's Fortran, I'm sorry, not Fortran, but where is Red Hat in providing the underlay and the capabilities for the HPC and AI community? >> Yeah. So, I think if you look at the user base that we're looking at, it's on this spectrum from development to production. So putting AI workloads into production, it's an interesting challenge but it's easier to solve, and it has been solved to some extent, than the development cycle. So what we're looking at in Kenneth's domain it's more like the end user, the data scientist, developing code, and doing these experiments. Putting them into production is that's where containers live and thrive. You can containerize your model, you containerize your workload, you deploy it into your OpenShift Kubernetes cluster, done, you monitor it, done. So the software developments and the SRE, the ops part, done, but how do I get the data scientist into this cloud native age where he's not developing on his laptop or on a machine, where he SSH into and then does some stuff there. And then some system admin comes and needs to tweak it because it's running out of memory or whatnot. But how do we take him and make him, well, and provide him an environment that is good enough to work in, in the browser, and then with IDE, where the workload of doing the computation and the experimentation is repeatable, so that the environment is always the same, it's reliable, so it's always up and running. It doesn't consume resources, although it's up and running. Where it's, where the supply chain and the configuration of... And the, well, the modules that are brought into the system are also reliable. So all these problems that we solved in the traditional software development world, now have to transition into the data science and HPC world, where the problems are similar, but yeah, it's different sets. It's more or less, also a huge educational problem and transitioning the tools over into that is something... >> Well, is this mostly a technical issue or is this a cultural issue? I mean, are HPC workloads that different from more conventional OLTP workloads that they would not adapt well to a distributed containerized environment? >> I think it's both. So, on one hand it's the cultural issue because you have two different communities, everybody is reinventing the wheel, everybody is some sort of siloed. So they think, okay, what we've done for 30 years now we, there's no need to change it. And they, so it's, that's what thrives and here at KubeCon where you have different communities coming together, okay, this is how you solved the problem, maybe this applies also to our problem. But it's also the, well, the tooling, which is bound to a machine, which is bound to an HPC computer, which is architecturally different than a distributed environment where you would treat your containers as kettle, and as something that you can replace, right? And the HPC community usually builds up huge machines, and these are like the gray machines. So it's also technical bit of moving it to this age. >> So the massively parallel nature of HPC workloads you're saying Kubernetes has not yet been adapted to that? >> Well, I think that parallelism works great. It's just a matter of moving that out from an HPC computer into the scale out factor of a Kubernetes cloud that elastically scales out. Whereas the traditional HPC computer, I think, and Kenneth can correct me here is, more like, I have this massive computer with 1 million cores or whatnot, and now use it. And I can use my time slice, and book my time slice there. Whereas this a Kubernetes example the concept is more like, I have 1000 cores and I declare something into it and scale it up and down based on the needs. >> So, Kenneth, this is where you talked about the culture part of the changes that need to be happening. And quite frankly, the computer is a tool, it's a tool to get to the answer. And if that tool is working, if I have a 1000 cores on a single HPC thing, and you're telling me, well, I can't get to a system with 2000 cores. And if you containerized your process and move it over then maybe I'll get to the answer 50% faster maybe I'm not that... Someone has to make that decision. How important is it to get people involved in these types of communities from a researcher? 'Cause research is very tight-knit community to have these conversations and help that see move happen. >> I think it's very important to that community should, let's say, the cloud community, HPC research community, they should be talking a lot more, there should be way more cross pollination than there is today. I'm actually, I'm happy that I've seen HPC mentioned at booths and talks quite often here at KubeCon, I wasn't really expecting that. And I'm not sure, it's my first KubeCon, so I don't know, but I think that's kind of new, it's pretty recent. If you're going to the HPC community conferences there containers have been there for a couple of years now, something like Kubernetes is still a bit new. But just this morning there was a keynote by a guy from CERN, who was explaining, they're basically slowly moving towards Kubernetes even for their HPC clusters as well. And he's seeing that as the future because all the flexibility it gives you and you can basically hide all that from the end user, from the researcher. They don't really have to know that they're running on top of Kubernetes. They shouldn't care. Like you said, to them it's just a tool, and they care about if the tool works, they can get their answers and that's what they want to do. How that's actually being done in the background they don't really care. >> So talk to me about the AI side of the equation, because when I talk to people doing AI, they're on the other end of the spectrum. What are some of the benefits they're seeing from containerization? >> I think it's the reproducibility of experiments. So, and data scientists are, they're data scientists and they do research. So they care about their experiment. And maybe they also care about putting the model into production. But, I think from a geeky perspective they are more interested in finding the next model, finding the next solution. So they do an experiment, and they're done with it, and then maybe it's going to production. So how do I repeat that experiment in a year from now, so that I can build on top of it? And a container I think is the best solution to wrap something with its dependency, like freeze it, maybe even with the data, store it away, and then come to it back later and redo the experiment or share the experiment with some of my fellow researchers, so that they don't have to go through the process of setting up an equivalent environment on their machines, be it their laptop, via their cloud environment. So you go to the internet, download something doesn't work, container works. >> Well, you said something that really intrigues me you know in concept, I can have a, let's say a one terabyte data set, have a experiment associated with that. Take a snapshot of that somehow, I don't know how, take a snapshot of that and then share it with the rest of the community and then continue my work. >> Marcel: Yeah. >> And then we can stop back and compare notes. Where are we at in a maturity scale? Like, what are some of the pitfalls or challenges customers should be looking out for? >> I think you actually said it right there, how do I snapshot a terabyte of data? It's, that's... >> It's a terabyte of data. (both conversing) >> It's a bit of a challenge. And if you snapshot it, you have two terabytes of data or you just snapshot the, like and get you to do a, okay, this is currently where we're at. So that's why the technology is evolving. How do we do source control management for data? How do we license data? How do we make sure that the data is unbiased, et cetera? So that's going more into the AI side of things. But at dealing with data in a declarative way in a containerized way, I think that's where currently a lot of innovation is happening. >> What do you mean by dealing with data in a declarative way? >> If I'm saying I run this experiment based on this data set and I'm running this other experiment based on this other data set, and I as the researcher don't care where the data is stored, I care that the data is accessible. And so I might declare, this is the process that I put on my data, like a data processing pipeline. These are the steps that it's going through. And eventually it will have gone through this process and I can work with my data. Pretty much like applying the concept of pipelines through data. Like you have these data pipelines and then now you have cube flow pipelines as one solution to apply the pipeline concept, to well, managing your data. >> Given the stateless nature of containers, is that an impediment to HPC adoption because of the very large data sets that are typically involved? >> I think it is if you have terabytes of data. Just, you have to get it to the place where the computation will happen, right? And just uploading that into the cloud is already a challenge. If you have the data sitting there on a supercomputer and maybe it was sitting there for two years, you probably don't care. And typically a lot of universities the researchers don't necessarily pay for the compute time they use. Like, this is also... At least in Ghent that's the case, it's centrally funded, which means, the researchers don't have to worry about the cost, they just get access to the supercomputer. If they need two terabytes of data, they get that space and they can park it on the system for years, no problem. If they need 200 terabytes of data, that's absolutely fine. >> But the university cares about the cost? >> The university cares about the cost, but they want to enable the researchers to do the research that they want to do. >> Right. >> And we always tell researchers don't feel constrained about things like compute power, storage space. If you're doing smaller research, because you're feeling constrained, you have to tell us, and we will just expand our storage system and buy a new cluster. >> Paul: Wonderful. >> So you, to enable your research. >> It's a nice environment to be in. I think this might be a Jevons paradox problem, you give researchers this capability you might, you're going to see some amazing things. Well, now the people are snapshoting, one, two, three, four, five, different versions of a one terabytes of data. It's a good problem to have, and I hope to have you back on theCUBE, talking about how Red Hat and Ghent have solved those problems. Thank you so much for joining theCUBE. From Valencia, Spain, I'm Keith Townsend along with Paul Gillon. And you're watching theCUBE, the leader in high tech coverage. (upbeat music)

>>Welcome to the cubes coverage of C CFS, co con EU cloud native con in Valencia Spain, I'm John furrier. This is a preview interview with the co-chairs versus we have Jasmine James senior engineering manager and of developer experience and Coon cloud native con EU co-chair and RI ricotta Rocher computing engineer at CERN and Coon co-chair as well at EU. Great to have you both on great to see you, both of you, >>Hey, to be here, >>Us >>Keep alumni. So, you know, Coon just continues to roll and get bigger and bigger, um, and watching all the end user action watching the corporations enterprises come in and just all the open source projects being green litted and just all the developer onboarding has been amazing. So it should be a great EU and Vale span, great venue. A lot of people I I'm talking to are very excited, so let's get into it as co-chairs take us through kind of the upcoming schedule at a very high level. Then I wanna dig into, uh, some of the new insights into selection and program programming that you guys had to go through. I know every year it's hard. So let's start with the overall upcoming schedule for COCOM. >>Yeah. So I'll dive into that. So the schedule is represents a, quite a diverse set of topics. I would say, um, I personally am a fan of those, you know, more personal talks from an end user perspective. There's also like a lot of the representation from a community perspective and how folks can get involved. Um, as most of, you know, like our tracks, the types of tracks has evolved over the year as well. So we now have a community track student track. So it's gonna be very exciting to hear content within those tracks, um, through in Valencia. So a very exciting schedule. Um, yeah. >>And just real quick for the folks watching it's virtual and physical it's hybrid event May 4th through seventh Ricardo, what's your take on the schedule? Uh, how do you see it breaking down from a high level standpoint? >>Yeah, so, um, I'm pretty excited. Um, I think the, the fact that this hybrid will help keep, um, build on the experiences we had, uh, during the pandemic times to, to give a better experience for people not making, uh, it to Valencia. I'm pretty excited also about the number of co-located events. So the two days before the conference will include, uh, um, a large number of co-located events, focusing on security S uh, and some new stuff for like batch and HPC workloads that I'm pretty close to as well. Uh, and then some, some really good consolidation in some tracks like this value, which I think will be quite, quite interesting as well. >>So you mentioned this is gonna be like watch parties, people gonna be creating kind of satellite events. Is that what you're referring to, uh, in terms of the physical space gonna be an event, obviously, um, what's going on around, outside the event, either online or as part of the program. >>So, yeah, uh, the, the, all the sessions, uh, from, from the collocated events will be available virtually as well. I don't know if people will actually be setting up parties everywhere. <laugh>, I'm sure some people will. Yeah, >>There'll definitely be >>Some. And then for, for, for the conference itself, there will be dedicated rooms where for the virtual talks, uh, people can just join in and sit for a while and watch the virtual talks and then go back to the in person, ones, uh, Monday feel >>Like, yeah, it's always a good event. Uh, Jasmine, we talked about this last time and Ricardo, we always get into the hood as well. What's the vibe on the, the, the, the programming. And honestly, people wanna get, give talks. There's a virtual component, which opens up more aperture, uh, for more community and more actions as, as Ricardo pointed out. What's what was the process this year? Because we're seeing a lot of big trends emerge, obviously securities front and center, um, end user projects are growing data engineering is a new persona. That's just really emerged out of kind of the growth of data and the role of data that it plays and containers. And, and with Kubernetes, just a lot of action. What's the, what was it like this year in, in the selection process for the program? >>Yeah, I mean, the selection process is always lots of fun for the co-chairs. Um, you shout out to program committee, track chairs, you put in a lot of great work and reviewing talks and, and it's just a very, very thorough process. So kudos to all of us who are getting through it for this year. I think that lots of things emerge, but I still feel like security is top of mind for a lot of folks, like security is really has provided. One of the biggest, um, submissions is from, from a quantity perspective, there are tons of talks submitted for security track, and that just kind of speaks for itself, right? This is something that the cloud native community cares about, and there's still a lot of innovation and people wanna voice what they're doing and share it. >>Ricardo, what's your take, we've had a lot of chats around not only some of the hardcore tech, but some of the new waves that are emerging out of the growth, the mature maturization of, of, of the segment. What are you seeing, uh, as terms of like the, the key things that came out during the, the process? >>Yeah, exactly. So I think I would highlight something that Jasmine said, which is the, the emergency emergence of some new tracks as well. Uh, she mentioned the student track, but also we added a research track, which is actually the first time we'll have it. So I'm pretty excited about that. Of course, uh, then for the trends, clearly security observability are, uh, massive tracks for app dev operations, uh, extending Kubernetes had also a lot of submissions. Um, I think the, the main things I saw that, uh, kind of, uh, gain a bit of more consistency is the part for the business value. And, uh, the, the, the fact that people are now looking more at the second step, like managing cloud costs, uh, how to optimize, uh, spot usage and, um, usage of GPUs for machine learning, things like this. So I'm pretty excited. And all these hybrid deployments also is something that keeps coming back. So those were, are the ones that, uh, I, I think came out from, from, from the submission at this time, >>You know, it's interesting as the growth comes in, you see these cool new things happen, but there are also signs of problems that need to be solved to create opportunities. Jasmine, you mentioned security. Um, there's a lot of big trends, scale Ricardo kind of hinting at the scale piece of it, but there's all this now new things, the security posture changes, uh, as you shift left, it's not, it's not, it's not over when you shift left in security in the pipeline in there, but it's, there's audits. There's the size of, uh, the security elements, uh, there's bill of materials. Now, people who got supply chains, these are huge conversations right now in the industry, supply chain security, um, scale data, uh, optimization management, um, notifications, all this is built in, built into a whole nother level. What do you guys see in the key trends in the cloud native ecosystem? >>I, I would say that a lot of the key trends, like you said, it, right, these things are not going anywhere. It's actually coming to a point of maturation. Um, I see more of a focus on how consuming, how, how companies go about consuming these different capabilities. What is that experience like? There's a talk that's gonna be offered, um, as a keynote, um, just about that security and leveraging developers to scale security within your environment. And not only is it a tool problem, it's a mindset thing that you have to be able to get over and partner bridge gaps between teams in order to make this, um, a reality within, within, um, people, within certain organizations. So I see the experience part of it, um, coming a big, a big thing. Um, there's multiple talks about that. >>Ricardo, what's your take on these trends? Cause I look at the, the, the paragraph of the projects now it's like this big used to be like a couple sentences. Now you got more projects coming on, you got the rookies in there and you got the, the veterans, the veteran projects in there. So this speaks volumes to kind of things like notaries new, right? So this is cool. Wait, what does that mean? Okay. Security auditing all this is happening. What are the, what are the big trends that you're excited about that you see that people are gonna be digging in, in, in the pro in, in the event? >>Yeah, I think we, we, we talked about supply chain just before. I think that's, that's a big one. We, we saw a, a keynote back in north America already introducing this, and we saw a lot of consolidation happening now in projects, but also companies supporting this project. Um, I, I'm also quite interested, interested in the evolution of Kubernetes in the sense that it's not just for, what was it, it was traditionally used for like traditional it services and scaling. We start seeing, there will be a very cool keynote from, from deploying, uh, Kubernetes at the edge, but really at the edge with the lower orbit satellites running ES in basically, uh, space. So those things I think are, are, are very cool. Like we start seeing really a lot of consolidation, but also people looking at Kubernetes for, for pretty crazy things, which is very exciting. >>Yeah. You mention, you mentioned space that really takes us to a whole edge, another level of edge thinking, um, you know, I've had many conversations around how do you do break fixing space with some folks in, in the space industry, in, in public sector, software is key in all this. And again, back to open source, open source has to be secured. It has to be, be able to managed effectively. It needs to be optimized into the new workflows space is one of them, you know, you see in, um, 5g edge is huge, uh, with new kind of apps that are being built there. So open source plays a big role in all this. So the, the question I wanna ask you guys is as open source continues to grow and it's growing, we're seeing startups emerge with the playbook of you. You play an open source or you actually create a project and then you get funding behind it because I know at least three or four VCs here in Silicon valley that look at the projects and say, they're looking for deals. And they're saying, keep it open a whole nother level. Can you guys share your insights on how the ecosystem's, uh, evolving with entrepreneurship and, and startups? >>Uh, oh, I guess I'll start. Um, I think that it's such a healthy thing, um, to have such innovation occurring, um, is it's really just, uh, Testament as to how the cloud native community right. Nurtures and cultivates these ideas and provides a great framework for them to develop over time, going from, you know, the sandbox and incubating and graduating and having the support of a solid framework, I think is a lot of the reason why a lot of these projects grow so quickly and reach certain these high levels of adoption. Um, so it's a really fantastic thing to see. I think that, you know, VCC an opportunity and, and, and there's a lot of great innovation that can be, you know, operationalized and scaled, right. Um, and applied to a lot of industries. So I feel it, I feel like it's a very healthy thing. Um, it also creates a lot of opportunities about something I'm passionate about, which is like, you know, people getting involved in open source as a step into the world of tech. Um, so all of these projects coming about provide an opportunity for folks to get involved in a particular component they're interested in and then grow their career in open source. So really great thing, in my opinion. >>And you mentioned the student track, by the way, I kept to point that out. I mean, that's huge. That's gonna be a lot of people who have, you know, in computer science programs or self learning. I mean, the, the, the ability to get up to speed, uh, from a development standpoint, as a coder, um, you can be a rural comp SI or, uh, just a practitioner just coding. I mean, data's everywhere. So data engineering, coding, I mean, Ricardo, this is huge student and then just every sector's opening up. I mean, the color codes on the calendar is, uh, larger than ever before. >>Yeah. I think, yeah, the, the diversity of the usage and the communities is, is something that is really important and it's been growing still. So I, I think this one not stop. Um, I'm pretty, pretty, pretty excited to see also how we'll handle this growth, because as you mentioned, like everything is increasing in numbers, number of projects, number of startups around this project. Uh, so one, one thing that I'm particularly interested on as an end user is to understand also how to help other end users that are jumping in not only the, the developers or, or the people wanting to support these projects, but also the end users. How, how do they choose their sta how, how it's, how, how should they look like for their use cases, much more than just going, uh, from, from the selection, individual projects to understand how they, they work together. So I think this is a challenge for, for the next couple of years. >>Yeah. I mean, roll your own and building blocks, whatever you wanna call it, you're starting to see people, uh, build their own stacks. And that's not a bad thing. It might be a feature, not a bug. >>Yeah. I, I would agree that I think it's something that we have to work on, uh, together to, to, to help, especially people starting in the ecosystem, but also for, for the experienced ones that start looking at other use cases as well. >>Okay. Jasmine, we talked about this last time, you gotta pick a favorite, uh, child in the, in the, in the agenda. Uh, what's your favorite session? Um, and you gotta pick one or three or maybe put handful, um, as you guys look through this year, what's the theme. I mean, people like you can kind of sense what's happening. Uh, when you look at the agenda, obviously observability is in there, all these great stuff's in there, but what's the, what's your favorite, um, uh, project or topic this year that, uh, you're jazzed about >>For me, I I'd say there's such diverse, um, topics that are being presented both on the keynote stage and throughout, um, the various tracks. I will just reference, um, the talk that I, I sort of alluded to earlier about, um, leveraging developers to scale Kubernetes. Um, it's a talk given by red hat on the keynote stage. Um, I just think it, you know, the abstracts will me because it's talks about bridging two different roles together, um, and scaling what we all know to be so important within the cloud native space, security and Kubernetes. So it's something that's very like real for me, um, in, in my current role and previous roles. So I think that that's the one that spoke to me. >>Awesome. Ricardo, what's your favorite, uh, this year? What do you, what do you, uh, if you had to put a little gold star on something that you're interested in, what it would it be? >>I think I hinted on, on it just before, which is, uh, I'm, I'm kind of a space enthusiast. So all, all this idea of running Kubernetes in space, um, makes me very excited. So really looking forward to that one, but as an end user, I'm also very interested in talks. Uh, like the one Mercedes will be doing, which is the transition from a kind of a more traditional company to this, uh, uh, more modern world of, uh, cloud native. And I'm quite interested to hear how, how, what their experience has been has been like in the last few years. >>Well, you guys do a great job. I love chatting with you and I love, uh, CNCF and following from the beginning, we were there when it was, when it was created and watched it grow from an insider perspective, the hyperscalers people who are really kind of eating glass and building scale, you know, SREs. Now you have, you have the SRE concept going kind of global mainstream, seeing enterprises and end users contributing and participating enterprises, getting, connecting those two worlds. Jasmine, as you said, as you look at that, you're starting to see the scale piece become huge. You mentioned it a little bit earlier, Jasmine, the SRE role was specific to servers and cloud. You're kind of seeing that kind of role needed for this kind of cloud native layer. We're seeing it with data engineering. It's not for the faint of heart. It may not be a persona. That's got zillions of people, but it scales. It's like an SRE role. You're seeing that with this kind of monitoring and, and with containers and Kubernetes where it's gotta get easier and scale, how do you guys see that? Do you see that emerging in the community, this, this kind of new scale role and, um, what is it, what is this trend? Or maybe I'm misrepresenting it or maybe I'm sensing it wrong, but what do you guys think about the scale piece? How is that F falling into place? >>Yeah, I, I think that is, um, adoption, like, or there's more saturation of, of cloud native technologies within any environment. Um, most in most companies realize that you have to have that represented right within the role that is managing it. Um, if you wanna have it be reliable. Um, so I think that a lot of roles are adopting those behaviors, right. In order to be able to sustain this within their environment and learning as they start to implement these things. Um, so I see that to be something that just happens. Um, we saw it was like DevOps, right? You know, engineers were starting to adopt, you know, working on the systems versus just, you know, working on software. Um, so it's sort of like encompassing all the things, right. We're, we're seeing a shift in the role and, and the behaviors that are within it in order to maintain these cloud native services. So >>Ricardo, what's your take, we've been seeing engineers get to the front lines more and more. Uh, you guys mentioned business value as one of the tracks and, uh, focus topics this year, it's happening, engineers and developers. They're getting in the front lines cuz as you move up that stack, whether it's a headless system for retail or deploying something in another sector, they gotta be in the front lines. If you're gonna be in doing machine learning and have data, you gotta have domain scales about what the business is. Right? >>Yeah. I, I, I agree very much with what Jasmine said and, and uh, if we add this for, for kind of the business value and the, this opportu opportunistic usage of, uh, all types of resources that can come from basically anywhere these days, I think this is, this is really becoming, um, a real role to, to understand how, how to best, uh, use all of this and uh, to, to make the best of all this available resources. When we start talking about, uh, CPUs, it's already important. If we start talking about GPU's, which are more scar or some sort of specialized accelerators, then, then it becomes really like something that, uh, you, you need people that know where, where to go and fish for those. Cause they, they, you can just build your own data center and, and scale that anymore. So you really need to understand what's out there. >>Applications gotta have the security posture nailed down. They gotta have it. Automation built in. You gotta have the observability, you gotta have the business value. I mean, it sounds like a mature industry developing here finally. It's happening. Good job guys. Thanks for coming on the queue. Really appreciate it. >>Thank you. Thank you for having >>Us. And we'll see the cube here at Koon cloud native con May 16th through the 20th in Vale Spain, the cube will be there. We'll have some online coverage as well. Look for the virtual from CNCF. The cube will bring all the, all the action. I'm John fur, your host, see you in Spain and see you on the 16th.

>>welcome. My name is Ben Bennett. I am the director of HPC Strategic programs here at Hewlett Packard Enterprise. It is my great pleasure and honor to be talking to Professor Mark Parsons from the Edinburgh Parallel Computing Center. And we're gonna talk a little about exa scale. What? It means we're gonna talk less about the technology on Maura about the science, the requirements on the need for exa scale. Uh, rather than a deep dive into the enabling technologies. Mark. Welcome. >>I then thanks very much for inviting me to tell me >>complete pleasure. Um, so I'd like to kick off with, I suppose. Quite an interesting look back. You and I are both of a certain age 25 plus, Onda. We've seen these milestones. Uh, I suppose that the S I milestones of high performance computing's come and go, you know, from a gig a flop back in 1987 teraflop in 97 a petaflop in 2000 and eight. But we seem to be taking longer in getting to an ex a flop. Um, so I'd like your thoughts. Why is why is an extra flop taking so long? >>So I think that's a very interesting question because I started my career in parallel computing in 1989. I'm gonna join in. IPCC was set up then. You know, we're 30 years old this year in 1990 on Do you know the fastest computer we have them is 800 mega flops just under a getting flogged. So in my career, we've gone already. When we reached the better scale, we'd already gone pretty much a million times faster on, you know, the step from a tariff block to a block scale system really didn't feel particularly difficult. Um, on yet the step from A from a petaflop PETA scale system. To an extent, block is a really, really big challenge. And I think it's really actually related to what's happened with computer processes over the last decade, where, individually, you know, approached the core, Like on your laptop. Whoever hasn't got much faster, we've just got more often So the perception of more speed, but actually just being delivered by more course. And as you go down that approach, you know what happens in the supercomputing world as well. We've gone, uh, in 2010 I think we had systems that were, you know, a few 1000 cores. Our main national service in the UK for the last eight years has had 118,000 cores. But looking at the X scale we're looking at, you know, four or five million cores on taming that level of parallelism is the real challenge. And that's why it's taking an enormous and time to, uh, deliver these systems. That is not just on the hardware front. You know, vendors like HP have to deliver world beating technology and it's hard, hard. But then there's also the challenge to the users. How do they get the codes to work in the face of that much parallelism? >>If you look at what the the complexity is delivering an annex a flop. Andi, you could have bought an extra flop three or four years ago. You couldn't have housed it. You couldn't have powered it. You couldn't have afforded it on, do you? Couldn't program it. But you still you could have You could have bought one. We should have been so lucky to be unable to supply it. Um, the software, um I think from our standpoint, is is looking like where we're doing mawr enabling with our customers. You sell them a machine on, then the the need then to do collaboration specifically seems mawr and Maura around the software. Um, so it's It's gonna be relatively easy to get one x a flop using limb pack, but but that's not extra scale. So what do you think? On exa scale machine versus an X? A flop machine means to the people like yourself to your users, the scientists and industry. What is an ex? A flop versus >>an exa scale? So I think, you know, supercomputing moves forward by setting itself challenges. And when you when you look at all of the excess scale programs worldwide that are trying to deliver systems that can do an X a lot form or it's actually very arbitrary challenge. You know, we set ourselves a PETA scale challenge delivering a petaflop somebody manage that, Andi. But you know, the world moves forward by setting itself challenges e think you know, we use quite arbitrary definition of what we mean is well by an exit block. So, you know, in your in my world, um, we either way, first of all, see ah flop is a computation, so multiply or it's an ad or whatever on we tend. Thio, look at that is using very high precision numbers or 64 bit numbers on Do you know, we then say, Well, you've got to do the next block. You've got to do a billion billion of those calculations every second. No, a some of the last arbitrary target Now you know today from HPD Aiken by my assistant and will do a billion billion calculations per second. And they will either do that as a theoretical peak, which would be almost unattainable, or using benchmarks that stressed the system on demonstrate a relaxing law. But again, those benchmarks themselves attuned Thio. Just do those calculations and deliver and explore been a steady I'll way if you like. So, you know, way kind of set ourselves this this this big challenge You know, the big fence on the race course, which were clambering over. But the challenge in itself actually should be. I'm much more interesting. The water we're going to use these devices for having built um, eso. Getting into the extra scale era is not so much about doing an extra block. It's a new generation off capability that allows us to do better scientific and industrial research. And that's the interesting bit in this whole story. >>I would tend to agree with you. I think the the focus around exa scale is to look at, you know, new technologies, new ways of doing things, new ways of looking at data and to get new results. So eventually you will get yourself a nexus scale machine. Um, one hopes, sooner rather >>than later. Well, I'm sure you don't tell me one, Ben. >>It's got nothing to do with may. I can't sell you anything, Mark. But there are people outside the door over there who would love to sell you one. Yes. However, if we if you look at your you know your your exa scale machine, Um, how do you believe the workloads are going to be different on an extra scale machine versus your current PETA scale machine? >>So I think there's always a slight conceit when you buy a new national supercomputer. On that conceit is that you're buying a capability that you know on. But many people will run on the whole system. Known truth. We do have people that run on the whole of our archer system. Today's A 118,000 cores, but I would say, and I'm looking at the system. People that run over say, half of that can be counted on Europe on a single hand in a year, and they're doing very specific things. It's very costly simulation they're running on. So, you know, if you look at these systems today, two things show no one is. It's very difficult to get time on them. The Baroque application procedures All of the requirements have to be assessed by your peers and your given quite limited amount of time that you have to eke out to do science. Andi people tend to run their applications in the sweet spot where their application delivers the best performance on You know, we try to push our users over time. Thio use reasonably sized jobs. I think our average job says about 20,000 course, she's not bad, but that does mean that as we move to the exits, kill two things have to happen. One is actually I think we've got to be more relaxed about giving people access to the system, So let's give more people access, let people play, let people try out ideas they've never tried out before. And I think that will lead to a lot more innovation and computational science. But at the same time, I think we also need to be less precious. You know, we to accept these systems will have a variety of sizes of job on them. You know, we're still gonna have people that want to run four million cores or two million cores. That's absolutely fine. Absolutely. Salute those people for trying really, really difficult. But then we're gonna have a huge spectrum of views all the way down to people that want to run on 500 cores or whatever. So I think we need Thio broaden the user base in Alexa Skill system. And I know this is what's happening, for example, in Japan with the new Japanese system. >>So, Mark, if you cast your mind back to almost exactly a year ago after the HPC user forum, you were interviewed for Premier Magazine on Do you alluded in that article to the needs off scientific industrial users requiring, you know, uh on X a flop or an exa scale machine it's clear in your in your previous answer regarding, you know, the workloads. Some would say that the majority of people would be happier with, say, 10 100 petaflop machines. You know, democratization. More people access. But can you provide us examples at the type of science? The needs of industrial users that actually do require those resources to be put >>together as an exa scale machine? So I think you know, it's a very interesting area. At the end of the day, these systems air bought because they are capability systems on. I absolutely take the argument. Why shouldn't we buy 10 100 pattern block systems? But there are a number of scientific areas even today that would benefit from a nexus school system and on these the sort of scientific areas that will use as much access onto a system as much time and as much scale of the system as they can, as you can give them eso on immediate example. People doing chroma dynamics calculations in particle physics, theoretical calculations, they would just use whatever you give them. But you know, I think one of the areas that is very interesting is actually the engineering space where, you know, many people worry the engineering applications over the last decade haven't really kept up with this sort of supercomputers that we have. I'm leading a project called Asimov, funded by M. P S O. C in the UK, which is jointly with Rolls Royce, jointly funded by Rolls Royce and also working with the University of Cambridge, Oxford, Bristol, Warrick. We're trying to do the whole engine gas turbine simulation for the first time. So that's looking at the structure of the gas turbine, the airplane engine, the structure of it, how it's all built it together, looking at the fluid dynamics off the air and the hot gasses, the flu threat, looking at the combustion of the engine looking how fuel is spread into the combustion chamber. Looking at the electrics around, looking at the way the engine two forms is, it heats up and cools down all of that. Now Rolls Royce wants to do that for 20 years. Andi, Uh, whenever they certify, a new engine has to go through a number of physical tests, and every time they do on those tests, it could cost them as much as 25 to $30 million. These are very expensive tests, particularly when they do what's called a blade off test, which would be, you know, blade failure. They could prove that the engine contains the fragments of the blade. Sort of think, continue face really important test and all engines and pass it. What we want to do is do is use an exa scale computer to properly model a blade off test for the first time, so that in future, some simulations can become virtual rather than having thio expend all of the money that Rolls Royce would normally spend on. You know, it's a fascinating project is a really hard project to do. One of the things that I do is I am deaf to share this year. Gordon Bell Price on bond I've really enjoyed to do. That's one of the major prizes in our area, you know, gets announced supercomputing every year. So I have the pleasure of reading all the submissions each year. I what's been really interesting thing? This is my third year doing being on the committee on what's really interesting is the way that big systems like Summit, for example, in the US have pushed the user communities to try and do simulations Nowhere. Nobody's done before, you know. And we've seen this as well, with papers coming after the first use of the for Goku system in Japan, for example, people you know, these are very, very broad. So, you know, earthquake simulation, a large Eddie simulations of boats. You know, a number of things around Genome Wide Association studies, for example. So the use of these computers spans of last area off computational science. I think the really really important thing about these systems is their challenging people that do calculations they've never done before. That's what's important. >>Okay, Thank you. You talked about challenges when I nearly said when you and I had lots of hair, but that's probably much more true of May. Um, we used to talk about grand challenges we talked about, especially around the teraflop era, the ski red program driving, you know, the grand challenges of science, possibly to hide the fact that it was a bomb designing computer eso they talked about the grand challenges. Um, we don't seem to talk about that much. We talk about excess girl. We talk about data. Um Where are the grand challenges that you see that an exa scale computer can you know it can help us. Okay, >>so I think grand challenges didn't go away. Just the phrase went out of fashion. Um, that's like my hair. I think it's interesting. The I do feel the science moves forward by setting itself grand challenges and always had has done, you know, my original backgrounds in particle physics. I was very lucky to spend four years at CERN working in the early stage of the left accelerator when it first came online on. Do you know the scientists there? I think they worked on left 15 years before I came in and did my little ph d on it. Andi, I think that way of organizing science hasn't changed. We just talked less about grand challenges. I think you know what I've seen over the last few years is a renaissance in computational science, looking at things that have previously, you know, people have said have been impossible. So a couple of years ago, for example, one of the key Gordon Bell price papers was on Genome Wide Association studies on some of it. If I may be one of the winner of its, if I remember right on. But that was really, really interesting because first of all, you know, the sort of the Genome Wide Association Studies had gone out of favor in the bioinformatics by a scientist community because people thought they weren't possible to compute. But that particular paper should Yes, you could do these really, really big Continental little problems in a reasonable amount of time if you had a big enough computer. And one thing I felt all the way through my career actually is we've probably discarded Mawr simulations because they were impossible at the time that we've actually decided to do. And I sometimes think we to challenge ourselves by looking at the things we've discovered in the past and say, Oh, look, you know, we could actually do that now, Andi, I think part of the the challenge of bringing an extra service toe life is to get people to think about what they would use it for. That's a key thing. Otherwise, I always say, a computer that is unused to just be turned off. There's no point in having underutilized supercomputer. Everybody loses from that. >>So Let's let's bring ourselves slightly more up to date. We're in the middle of a global pandemic. Uh, on board one of the things in our industry has bean that I've been particularly proud about is I've seen the vendors, all the vendors, you know, offering up machine's onboard, uh, making resources available for people to fight things current disease. Um, how do you see supercomputers now and in the future? Speeding up things like vaccine discovery on help when helping doctors generally. >>So I think you're quite right that, you know, the supercomputer community around the world actually did a really good job of responding to over 19. Inasmuch as you know, speaking for the UK, we put in place a rapid access program. So anybody wanted to do covert research on the various national services we have done to the to two services Could get really quick access. Um, on that, that has worked really well in the UK You know, we didn't have an archer is an old system, Aziz. You know, we didn't have the world's largest supercomputer, but it is happily bean running lots off covert 19 simulations largely for the biomedical community. Looking at Druk modeling and molecular modeling. Largely that's just been going the US They've been doing really large uh, combinatorial parameter search problems on on Summit, for example, looking to see whether or not old drugs could be reused to solve a new problem on DSO, I think, I think actually, in some respects Kobe, 19 is being the sounds wrong. But it's actually been good for supercomputing. Inasmuch is pointed out to governments that supercomputers are important parts off any scientific, the active countries research infrastructure. >>So, um, I'll finish up and tap into your inner geek. Um, there's a lot of technologies that are being banded around to currently enable, you know, the first exa scale machine, wherever that's going to be from whomever, what are the current technologies or emerging technologies that you are interested in excited about looking forward to getting your hands on. >>So in the business case I've written for the U. K's exa scale computer, I actually characterized this is a choice between the American model in the Japanese model. Okay, both of frozen, both of condoms. Eso in America, they're very much gone down the chorus plus GPU or GPU fruit. Um, so you might have, you know, an Intel Xeon or an M D process er center or unarmed process or, for that matter on you might have, you know, 24 g. P. U s. I think the most interesting thing that I've seen is definitely this move to a single address space. So the data that you have will be accessible, but the G p u on the CPU, I think you know, that's really bean. One of the key things that stopped the uptake of GPS today and that that that one single change is going Thio, I think, uh, make things very, very interesting. But I'm not entirely convinced that the CPU GPU model because I think that it's very difficult to get all the all the performance set of the GPU. You know, it will do well in H p l, for example, high performance impact benchmark we're discussing at the beginning of this interview. But in riel scientific workloads, you know, you still find it difficult to find all the performance that has promised. So, you know, the Japanese approach, which is the core, is only approach. E think it's very attractive, inasmuch as you know They're using very high bandwidth memory, very interesting process of which they are going to have to, you know, which they could develop together over 10 year period. And this is one thing that people don't realize the Japanese program and the American Mexico program has been working for 10 years on these systems. I think the Japanese process really interesting because, um, it when you look at the performance, it really does work for their scientific work clothes, and that's that does interest me a lot. This this combination of a A process are designed to do good science, high bandwidth memory and a real understanding of how data flows around the supercomputer. I think those are the things are exciting me at the moment. Obviously, you know, there's new networking technologies, I think, in the fullness of time, not necessarily for the first systems. You know, over the next decade we're going to see much, much more activity on silicon photonics. I think that's really, really fascinating all of these things. I think in some respects the last decade has just bean quite incremental improvements. But I think we're supercomputing is going in the moment. We're a very very disruptive moment again. That goes back to start this discussion. Why is extra skill been difficult to get? Thio? Actually, because the disruptive moment in technology. >>Professor Parsons, thank you very much for your time and your insights. Thank you. Pleasure and folks. Thank you for watching. I hope you've learned something, or at least enjoyed it. With that, I would ask you to stay safe and goodbye.

>> Hi everyone, this is Dave Vellante and I want to welcome you to our celebration of Exascale Day. A community event with support from Hewlett Packard Enterprise. Now, Exascale Day is October 18th, that's 10, 18 as in 10 to the power of 18. And on that day we celebrate the scientists, and researchers, who make breakthrough discoveries, with the assistance, of some of the most sophisticated supercomputers in the world. Ones that can run and Exascale. Now in this program, we're going to kick off the weekend and discuss the significance of Exascale computing, how we got here, why it's so challenging to get to the point where we're at now where we can perform almost, 10 to the 18th floating point operations per second. Or an exaFLOP. We should be there by 2021. And importantly, what innovations and possibilities Exascale computing will unlock. So today, we got a great program for you. We're not only going to dig into a bit of the history of supercomputing, we're going to talk with experts, folks like Dr. Ben Bennett, who's doing and some work with the UK government. And he's going to talk about some of the breakthroughs that we can expect with Exascale. You'll also hear from experts like, Professor Mark Parsons of the University of Edinburgh, who cut his teeth at CERN, in Geneva. And Dr. Brian Pigeon Nuskey of Purdue University, who's studying buyer diversity. We're going to also hear about supercomputers in space as we get as a great action going on with supercomputers up at the International Space Station. Let me think about that, powerful high performance water-cooled supercomputers, running on solar, and mounted overhead, that's right. Even though at the altitude at the International Space Station, there's 90% of the Earth's gravity. Objects, including humans they're essentially in a state of free fall. At 400 kilometers above earth, there no air. You're in a vacuum. Like have you ever been on the Tower of Terror at Disney? In that free fall ride, or a nosedive in an airplane, I have. And if you have binoculars around your neck, they would float. So the supercomputers can actually go into the ceiling, crazy right? And that's not all. We're going to hear from experts on what the exascale era. will usher in for not only space exploration, but things like weather forecasting, life sciences, complex modeling, and all types of scientific endeavors. So stay right there for all the great content. You can use the #ExascaleDay on Twitter, and, enjoy the program. Thanks everybody for watching.

>>from around the globe. It's the Cube with digital coverage of Red Hat. Summit 2020 Brought to you by Red Hat. >>Hi, I'm stupid, man. And this is the Cube's coverage of the Red Hat Summit 2020 course. The event this year is digital. We're talking to Red Hat executives, partners and customers where they are around the globe, pulling them in remotely happy to welcome back to the program. One of our Cube alumni on a very important topic, of course, that red hat open shift and joining me is Clayton Coleman. Who's the open shift chief architect with Red Hat. Clayton, thanks so much for joining us. Thank you >>for having me today. >>All right, So before we get into the product, it's probably worthwhile that we talked about you know what's happening in the community and talking specifically, you know, kubernetes the whole cloud, native space. Normally we would have gotten together. I would have seen you at Cube Con Ah, you know, at the end of March. But instead, here we are at the end of April. Looking out, you know, more CN cf events later this year, but first Red Hat Summit is a great open source event and broad community. So would really love your viewpoint as to what's happening in that ecosystem. >>It's been a really interesting year, obviously. Ah, with an open source community, you know, we react to this. Um, like we always react to all the things that go on in open source. People come to the community and sometimes they have more time, and sometimes they have less time. I think just from a community perspective, there's been a lot of people you know. It's reaching out to their colleagues outside of their companies, to their friends and coworkers and all of the different participants in the community. And there's been a lot of people getting together for a little bit of extra time trying todo, you know, connect virtually where they can't connect physically. And it's been it's been great to at least see where we've come this year. We haven't had Cube con and that'll be coming up later this year. But Kubernetes just had the 1 18 release, and I think Kubernetes is moving into that phase where it's a mature, open source project. We've got a lot of the processes down. I'm really happy with the work that the steering committee, um, has gone through. We handed off the last of the bootstrap Steering Committee members hand it off to the new, fully elected steering committee last year, and it's gone absolutely smoothly, which has been phenomenal on the The core project is trying to be a little bit more stable and to focus on closing out those loose ends being a little bit more conservative to change. And at the same time, the ecosystem has really exploded in a number of directions, as as Kubernetes becomes more of a bedrock technology for, um, enterprises and individuals and startups and everything in between. We've really seen a huge amount of of innovation in the space, and every year it just gets bigger and bigger. There's a lot of exciting projects that >>I >>have never even talk to somebody on the Kubernetes project. But they have made and build and, uh, and solve problems for their environments without us ever having to be involved, which I think it's success. >>Yeah, Clayton, you know, one of the challenges when you talk to practitioners out there is just keeping up with the pace of change. Can really be challenging. Something we really saw acutely was Docker was rolling out updates every six weeks. Most customers aren't going to be able to change fast enough to keep up with things you love your view point both is toe really what the CN CF says, as well as how Red Hat thinks of products. So you talked about you know, kubernetes 1.18. My understanding, even Google isn't yet packaging and offering that version there. So there's a lag between things. And as we start talking about managing across lots of clusters, how does Red Hat think of this? How should customers think about this? How do we make sure that we're, you know, staying secure and keeping updated on things without getting run over by the constant treadmill of >>change? That the interesting part about kubernetes Is it so much more than just that core project? You know, no matter what any of us in the in the core kubernetes project or in the products that red hat that build around open shift and layers on top, there's a There's a whole ecosystem of components that most people think of this fundamental to accomplishing building applications deploying them, running them, Whether it's their continuous integration pipelines or it's their monitoring stacks, we really as communities has become a little bit more conservative. >>Um, I >>think we really nail down our processes for taking that change from the community, testing it. You know, we run tens of thousands of automation tests a week on the latest and greatest kubernetes code, given time to soak, and we did it together with all those pieces of the ecosystem and then make sure that they work well together. And I've noticed over the last two years that the rate of oops we missed that in KUBERNETES 1 17 that by the time someone saw it, people are already using that that started to go down for us, it really hasn't been about the pace of keeping up with the upstream. But it's about making sure that we can responsibly pull together all the other ecosystem components that are still have much newer and a little bit. How do we say, Ah, they are then the exciting phase of their development while still giving ah predictable, reliable update stream. I would say that the challenges that most people are going to see is how they bring together all those pieces. And that's something that, on open shift, we think of as our goal is to help pull together all the pieces of this ecosystem, Um, and to make some choices for customers that makes sense and to give them flexibility where it's not clear yet what the right choice might be or where different people could reasonably disagree. And I'm really excited. I feel like we've got our We have a release cadence down and we're shipping the latest Cube after it's had time to quickly review, and I think we've gotten better and better at that. So I'm really proud of the team on Red Hat and how they've worked within the community so that everybody benefits from that in that testing of that stability. >>Great. I'd like to teach here, you dig in a little bit on the application side what's happening from the work loads that customers are using? Ah, what other innovations happening around that space? And how is Red Hat really helping? Really, The the infrastructure team and the developer team work even closer together, like Red Hat has done for a long time. >>This is This is a great question. I say There's two key, um, two key groups coming together. People are bringing substantial important critical production workloads, and they expect things both to just work, but also to be able to understand it. And they're making the transition. Ah, lot of folks I talked to were making the transition from previous systems they've got. They've been running open shift for a while, or they've been running kubernetes for a while, and they're getting ready to move, um, a significant portion of their applications over. And so, you know, in the early days of any project, you get the exciting Greenfield development and you get to go play with new technologies. But as you start moving your 1st 1 and then 10 and then 100 of your core business applications from the EMS or from bare metal into containers, you're taking advantage of that technology in a responsible way. And so the the expectations on us as engineers and community members is to really make sure that we're closing out the little stuff. You know, no bug is too small, but it can't trip up someone's production applications. So seeing a lot of that whether it's something new and exciting like, Um uh, model is a service or ai workloads or whether it's traditional big enterprise transaction processing. APS on the other side on that development, um, model I think we're starting to see phase to our community is 2.0, in the community, which is people are really leveraging the flexibility and the power of containers, things that aren't necessarily new to people who had. We got into containers early and had a chance to go through a couple of iterations. But now people are starting to find patterns that up level development teams, so being able to run applications the same way on a local machine as in a production environment. Well, most production environments are there now, and so people are really having toe. They're having to go through all of their tools and saying, Well, does this process that works for an individual developer also work when I want to move it there, my production or staging environments to production, and so on. New projects like K native and tectonic, which are kubernetes native, that's just one part of the ecosystem around development. On top of kubernetes, there's tons of exciting projects out there from companies that have adopted the full stack of kubernetes. They built it into their mindset, this idea of flexible infrastructure, and we're seeing this explosion of new ways where kubernetes is really just a detail, and containers are just the detail and the fact that it's running this little thing called Docker down at the heart of it. Nobody talks about anymore, and so that that transition has been really exciting. I think there's a lot that we're trying to do to help developers and administrators see eye to eye. And a lot of it's learning from the customers and users out there who really paved the way the which is the open source way. It's learning from others and helping others benefit from that. >>Yeah, I think you bring up a really important point we've been saying for a couple of years. Now that you know KUBERNETES should get to the point where it's boring and boring in a way also cause it's gonna be baked in everywhere we saw from basically customers just taking the code, really spending a lot of their own things by building the stack to, of course, lots of customers have used open shift over the year to If I'm adopting Public Cloud more and more, they're using those services from that standpoint. Can you talk a bit about how Red Hat is really integrating with public clouds? And you know your architectural technical philosophy on that? And how might that be? Differ from some other companies that you might call a little bit more, you know, Cloud of Jason, as opposed to being deeply integrated with the public cloud. >>The interesting thing about Kubernetes is that while it was developed on top of the clouds, it wasn't really built from Day one assuming a cloud underneath it. And I think that was an opportunity that we really missed. And to be fair, we had to make the thing work first before we depended on these unreliable clouds. You know, when we started, the clouds were really hitting their stride on stability and reliability, and people were it was the hot was becoming the obvious choice to some of what we've tried to do is take flexible infrastructure is a given, um, assume that the things that the cloud provides should be programmed for the for the benefit of the developer and the application, and I think that's a that's a key trend is we're not using the cloud because our administration teams want us. We're using the cloud because it makes us more powerful developers. That enables new scenarios. It shortens the the time between idea reality. What we have done in open shift is we've really built around The idea of open shift running on a cloud should take advantage of that cloud to an extreme degree, which is infrastructure could be flexible. The machines in that cluster need to come and go according to the demands of the applications on top of it. So giving a little bit more power to the cluster and taking a little bit of way from the cloud I'm. But that benefits. That also needs to benefit that those who are running on premise because I think, as you noted, our goal is you want this ubiquitous kubernetes environment everywhere, and the operations teams and the development teams and the Dev Ops teams in between need to have a consistent environment and so you can do this on the cloud. But you don't have that flexibility on premise. You've lost something. And so what we've tried to do as well is to think about those ideas that are what we think of as quote unquote cloud native that starts with a mutable operating systems. It starts with everything being declarative and working backwards from, you know, I wanna have 15 machines and then the cluster or controllers on the cluster say, Oh, well, you know, one of the machines has gone bad. Let's replace it on the cloud. You ask for a new I'm cloud infrastructure provider or you ask the the cloud a p i for a new machine, and then you replace it automatically, and no one knows any better on premise. We'd love to do the same thing with both bare metal virtualization on top of kubernetes. So we have that flexibility to say you may not have all of the options, but we should certainly be able to say, Oh, well, this hardware is bad or the machine stopped, so let's reboot it, and there's a lot of that same mindset that could be applied. We think that'll, um if you need virtualization, you can always use it. But virtualization is a layer on top benefits from some of the same things that all the other extensions and applications on top of kubernetes competitive trump. So trying to pay that layer and make sure that you have flexible, reliable storage on premise through our SEF and red hat storage products, which are built on top of the cluster exactly like virtualization, is both on top of the cluster. So you get cloud native storage mixed in working with those teams toe. Take those operational best practices. You know there's well, I think one of the things that interests me is no. 1 20 years ago, who was running an early version of SEF wouldn't have some approach to run these very large things that scales organizations like CERN have been using SEF for over a decade at extremely large scales. Some of what our mindset is we think it's time to bake some of that knowledge actually into our software for a very long time. We've kind of been building out and adding more and more software, but we always left the automation and the the knowledge about how that software supposed to be run to the side. And so by taking that and we talked about operators. Kubernetes really enshrines. This principle is taking that idea, taking some of that operational knowledge into the software we ship. Um, though that software can rely on kubernetes open shift tries to hide the details of the infrastructure underneath and our goal. I think in the long run it will just make everybody's lives easier. I shouldn't have to ship you a SEF admin for you to be successful. And we think we think there's a lot more room here that's really gonna improve how operations teams work, that the software that they use day to day. >>So Clinton you mentioned virtualization is one of the topics in there. Of course, virtualization is very prevalent in a customer's data center environment today. Red Hat open shift, oftentimes in data centers, is sitting on BM ware environments. Of course. Recently, VM Ware announced that they have kubernetes baked into the solution, and red hat has open shift with red hat virtualization. Maybe, you know, without going into too much depth, and you probably have breakouts and white papers on this. But you know what kind of decision point should customers be thinking about when they're deciding? Do I do this in bare metal. Do I do it in virtualization? What are some of the, you know, just high level trade offs there when they need to make those decisions, >>I think it's, um I think the 1st 1 is Virtualization is a mature technology. It's a known quantity for many organizations, and so those who are comfortable with virtualization, I'd say, like any responsible, uh, architecture engineering team. You don't want to stop using something that's working well just because you can. And a lot of what I would see as the transition that companies on is for some organizations without a big investment in virtualization. They don't see the need for it anymore, except as maybe a technical detail of how they isolate insecure workloads. One of the great things about virtualization technology that we're all aware of over the last couple years is it creates a boundary between work loads and the underlying environment. That doesn't mean that the underlying environment and containers can't be as secure or benefit from those same techniques. And so we're starting to see that in the community, this kind of spectrum of virtualization all the way from the big traditional virtualization to very streamlined, stripped down virtualization wrappers around containers. Um, like some of the cloud providers use for their application environments. So I'm really excited about the open source. Community is touching each of these points on the spectrum. Some of our goals are if you're happy with your infrastructure provider, we want to work well with, and that's kind of the pragmatic of everyone's on a different step in that journey. The benefit of containers is no matter how fast you make of VM, it's never gonna be quite as fast, is it containers. And it's never gonna be quite as easy for a developer to run on their laptop. And I think working through this is there's still a lot of work that we as a community to do around, making it easier for developers to build containers and test them locally in smaller environments. But all of that flexibility can still benefit from virtualization under later or virtualization used as an isolation technology. So projects like Kata and some of the work that's being done in the open source community around projects like firecracker taking the same, um, open source ideas and remixing them a different points gives us a lot of flexibility. So I would say, um, I'm actually less interested in virtualization then all of the other technologies that are application centric and at the heart of it, a VM isn't really a developer centric idea. It's specifically an administrative concept that benefits the administrator, and developers can take advantage of it. But I think all of the capabilities that you think of when you think about building an application like scaling out and making sure patches are applied, being able to roll back separating your configuration on then all of the hundreds of other levels of complexity that will add around that like service MASH and the ability to gracefully tolerate failures in your database. These were where I think, um, virtualization needs to work with the platform rather than being something that dominates how we think about the platform. It's application first, not being first. >>Yeah, no, you're absolutely right that the critique I've always given, you know for a number of years now is if you look at virtualization, the promise was, let's take that old application that probably should have been updated and just shove it in a VM and never think about it again. That's not doing good things for the user. So if I look at that at one end of the spectrum away at the other end of the spectrum, trying not to think about infrastructure, you mentioned K native s 01 of the things that you know I've been digging in tryingto learn more about at Red Hat Summit has really been the open shift server lists. So give us the update on that piece. Um, you know, that's obviously very different discussion than what we were just having from a virtualization standpoint. Eso How does open shift look at server lists? How does that tie into what? You know, if I'm doing server, listen, Amazon versus you know some of the other open source options for serverless. How should I be thinking about that? >>There's a lot of great choices on the spectrum out there. I think one of the interesting things and I love the word spectrum here because cane native kind of sits in a spot where it tries to be, as the name says, it tries to be as kubernetes native as possible, which lets you tap into some of those additional capabilities when you need it. And one of the things I've always appreciate it is the more restrictive framework is usually the better. It is doing that one thing and doing it really well. We learned this with rails. We learned this with no Js. And as people have built over the years, the idea of simple development platforms. The core function idea is a great simple idea, but sometimes you need to break out of that. You need extra flexibility or your application needs to run longer or slow Start is actually an issue. One of the things I think is most interesting about K native and I see comers and user. I think this way it's a good point. Um, that gives you some of the flexibility of kubernetes and a lot of the simplicity of, um, the functions is a service, but I think that there's going to be an inevitable set of use cases that tie into that which are simpler where open organization has a very opinionated way of running applications, and I think that flexibility will really benefit K native. Whereas some of the more opinionated remarks around server lists lose a little bit of that. So that's one dimension that I still think a native is well positioned to kind of capture the broadest possible audience, which for kubernetes and Containers was kind of our mindset. We wanted to solve enough of the problems that you can solve. You can run all your software. We don't have to solve all those problems to such a level that there's endless complexity, although we've been accused of having endless complexity and Cooper days before, but just trying to think through what are the problems that everyone's going to have to give them a way out? I'm at the same time for us, when we think about prioritization functions is service about integration. It's about taking applications and connecting them, connecting them through kubernetes. And so it really depends on identity and access to data and tying that into your cloud environment. If you're running on top of a cloud or tying it into your back end databases, if your on premise, >>I >>think that is where the ecosystem is still working to bring together and standardize some of those pieces in kubernetes or on top of Kubernetes. What I'm really excited about is the team as much. You know, there's been this core community effort to get a native to a G, a quality. Alongside that, the open shift serverless team has been trying to make it a dramatically simpler action. If you have kubernetes and open shift, it's a one click action to get started with, Um Kay native and just like any other technology. How accessible it is determines how easy users find it to get started and to build the applications they need. So for us, it's not just about the core technology. It's about someone who's not familiar with Serverless or not familiar with kubernetes. Bring up an editor and build a function and then deploy it on top of open shift. See it scale out like a normal kubernetes application, not having to know about pods or persistent volumes or notes. And so these air, these are some of the steps. I've been really proud that the team's done. I think there's a huge amount of innovation that will happen this year and next year, as the maturity of the kubernetes ecosystem really grows up, we'll start to see standardized technologies, for I'm sharing identity across multiple clouds across multiple environments. It's no good if you've got these applications on the cloud that need to tie into your corporate L dap. But you can't connect your corporate held up to the cloud. And so your applications need 1/3 identity system. Nobody wants 1/3 identity system. And so, working through some of this thing where the challenges I think that hybrid organizations are already facing and our job is just to work with them in the open source communities and with the cloud providers partner with them and open source so that the technologies in kubernetes fit very well into whatever environment they run it. Alright, >>well, Clayton, really appreciate all the updates there. I know the community is definitely looking forward to digging through some of the breakout sessions reading all the new announcements. And, of course, we look forward to seeing you on the team participating in many of the kubernetes related events happening later this >>year. That's right. It's ah, gonna be a good year. >>All right. Thanks so much for joining us. I'm still Minuteman and as always thank you for watching you. >>Yeah, yeah, yeah, yeah

live from San Francisco it's the cube covering micron insight 2019 brought to you by micron welcome back to San Francisco everybody we're here at Pier 27 the Sun is setting behind the the buildings in San Francisco you're watching the cube the leader in live tech cover jump date Volante with my co-host David Flair we've been here all day covering micron insight 2019 Tommy Vee is here is the senior vice president and general manager of the compute and networking business unit at micron Tama great to see you again great to see you so you got compute and networking two of the big three you're in your business unit there you go but we're gonna talk about 3d crosspoint today but so anyway you know absolutely we're kind of bringing you outside the swimlane or maybe not but tell us about your bu and what's the update yes we you know we sell primarily memory today DRAM although in the future we see 3d crosspoint it's a great opportunity into the the data center you know both traditional servers and the cloud players pcs graphics and networking yes so you get some hard news today why don't we dig into that a little bit we surely haven't covered much of it but okay yeah so I guess you know a couple couple things of interest probably most directly as we we announced our our first 3d crosspoint storage device it's a it's a it's the highest performance SSD in the world and offers compared to other 3d crosspoint based solutions on the market you know anywhere from three and a half to five times the performance on a range of both sequential and random reads and writes two and a half million I ops bandwidth readin right north of nine gigabytes a second and I'm super fast super fast fast and you know similar similar you know a very positive comparisons up against up against me and SSDs ok and so we're excited about that so where's the fit what are the use cases who you're targeting with sure yeah I mean I think you know that one way to think about it is that anytime you introduce a new layer into the memory and storage hierarchy you know historically it was SRAM caches and then it was SSDs going in between dear and rotating media now this is 3d crosspoint sitting in between DRAM and and NAND and and the reason it is a benefit in terms of another layer is it's you know higher density and and greater persistence than DRAM it's greater performance and and you know you can it can cycle greater endurance than the man and and when you do that you do nibble away at either side of that layer so in this case that nibbles away a little bit from DRAM and a little bit from NAND but it grows the overall pie and it's the only player in the industry that provides DRAM 3d crosspoint in and we think that's a great opportunity at some code to the economics cuz it's more expensive than and less expensive than the DRAM higher performance than the traditional flash short lower performance well under the performance of DRAM so yeah I mean so again I think you know the the the you know the benefits like I said is it's it offers greater density and it offers greater persistence than DRAM and so that's the advantage there and it offers much greater performance on things like bandwidth and I ops and much greater endurance than the NAND and certainly our preliminary results are in in applications like databases in certain AI and machine learning workloads and in workloads that that benefit from low latency I think financial service markets is one specific example you know we think there's a good value bro so so a Colombo question if I may yeah so si P would say no throw it throw everything in memory in Hana and of course sell the DRAM and say ok that's ok with us so you mentioned databases how should we think about this relative to in-memory databases sure I mean I think that if if you can afford it and of course it will be more expensive we would love to provide you know our highest density DRAM modules on on the highest end server platforms and you know put put you know you mentioned you know Hana database in the terabytes and terabytes of the RAM that would be great that is is not free if we refer you to do it right exactly and and so if you have the need for that performance that's will do but we we see there's a you know a an attractive range of workloads that cannot afford you know there's a costume that very high-end solution and so this affords something that that gives you know good benefits a database performance but at a slightly more I know you want to jump in go oh yeah sure I compare yourself with Intel which is obviously got the same raw technology they have gone for consumer type obtain [Music] SSDs but they put all their effort into combining it with a DVD or envied him and have combined that with the processor itself and made a combination which is very good for storage controllers yeah so the quest you can very well in in in the SSD much much much more than they have are you looking to go into that and the dim because he obviously you don't have the processes themselves to to to man yeah I mean you know to be clear the you know what we're offering today you know is a product that runs on standard and yeah and via me and while there may in the future be opportunities to further enhance performance with software optimization it runs you know out of the box absolutely without any software optimization and but I do think that you know there are opportunities both to use this technology in you know more of a storage type of configuration and and looking forward there are also opportunities to use it in a memory configuration you know what what what we're announcing today is our is our first storage and with regard to additional products you know stay tuned so if I think about the storage hierarchy you know the the classical pyramid and forget about let's let's focus on the persistent end of that spectrum yeah this is at the tip right is that how we should think about this or not necessarily I mean it is at the storage tip yes but I think we 10 to think a little bit more holistically that you know that that triangle extends from you know from DRAM traditionally to SSDs to rotating and we're now inserting a 3d crosspoint based layer in between and and so from that perspective it is it is the tip of the storage triangle right but it does sit below it does sit below DRAM so in the overall and the reason for my question was sort of a loaded question because if you eliminate the the DRAM piece now you've got that tip sewn and benefits from the volume of consumer thoughts on how you get volume with 3d crosspoint sure you know again I think there are you know at a at a lower performance point you know you can get higher density you know more cost effective storage solutions with that um and we certainly don't see you know NAND going away or we're quite bullish on that you're like man you know it's both a both a SATA and a nvme 96 layer TLC nan based products today so that's that continues to be a major area of investment but you know from a you know from a from a value and opportunity point of view we see a better opportunity you know applying this technology again into this layer in the you know in the in the server or datacenter hierarchy um you know as opposed to what one might be able to do in the consumer space and your OEM say bring it on right I mean they they want this we're talking about the server manufacturers data center yeah I mean I think we're in you know we're in we're in limited sampling with select customers so you know more to say about our go-to-market you know at a at a future date but certainly we we see that there is you know we're we're bullish about the opportunity the marketplace so just asking a question about volume making sure you if you look at the marketplace it's arm has been incredibly successful and it's driven a huge amount of memory and and Nan for yourself then that seems to be where the volume is growing much faster than most other platforms are you looking to use this technology 3d crosspoint as in in in that environment as even memory as in DRAM itself as memory itself at a much lower level I'm just thinking of ways that you could increase volume sure I mean so to be just just to be clear you're talking about what's driven overwhelmingly by by the cell phone market right obviously it's it's proliferating into IOT you know I guess again our our our view of the of the first and best opportunity is in the data center which is still today an x86 dominated world I would say you know in terms of opportunities like I said for you know memory based solutions in the data center um and for how we apply this in other areas you know stay tuned let's talk about this forward next acquisition so it's really interesting to see micron making moves in an AI why the acquisition tell us more about it sure yeah so it's a it's a it's a small small start-up you know handful of players although you know fairly experienced as as I believe sanjay mentioned they're on their their fifth generation of their architecture and so what we've acquired it's both it's both the hardware architecture that currently runs on FPGAs along with the supporting software that supports all the common frameworks the tensorflow is the the PI torches as well as the range of the network architectures you know that that are necessary to support again primarily on the inference side you know are we see the best opportunities in edge in fencing but in terms of what's behind the acquisition first of all there is there's an explosion of opportunity in machine learning we see that in particular on you know on edge inferencing and we feel that in order for us to continue to optimize and develop the best solutions both over all of a deep learning platform that includes memories but also just memories that are best optimized we need to understand you know when you noticed in the workloads we understand the best solutions and and and so that's why we made this acquisition we integrated it with our team that has for some time developed FPGA based adding cards and it's actually the basis of the technology for some of the dialog that used to offer example with OHSU when you talk about edge inferencing we're envisioning this sort of massively scalable distributed system that of course comprises edge you want to bring the compute to the data wherever the data lives obviously don't want to start moving data around now you're bringing a eye to that data which is the data data ai cloud all these superpowers coming together uh-huh so our premise is that the inferencing is going to be done at the edge much much of the data if not most of the data is going to stay at the edge yeah so this is what you're enabling through that integration provision heterogeneous combination of technologies correct I mean you know to use the extreme example that we talked about you know on stage earlier you know CERN has this massive amount of information that comes from the I think it's 40 million collisions a second or I may have my figures wrong and you cannot possibly store nor do you want to transmit that data and and so you you have to be applying AI to figure out what the good stuff is and there's no stream it's exactly and that solution exists in a myriad of applications you know the very you know simplistic one you're not going to send you know the picture of who's at your front door you know to a core data center to figure out if it's somebody in your family yeah you don't want to be doing that maybe not in the camera but certainly a lot closer because you just you know the network simply will not can't handle the capacity all right we got to go but but last word you know what are the takeaways from today what do you want our audience to remember from this event well I think you know I think it's just we continue to build on our memory and storage base to to move up the stack and add values in way that maybe storage subsystems like our our NAND SSD and 3d crosspoint that you know go a little further up the stack in terms of our gaining greater expertise in you know machine learning solutions or or the example with authentic of providing you know a broader solution including key management for how we secure the billions of devices they're gonna be at the edge touching all the bases Tom all right congratulations on all the hard work and it was great to see you again thanks guys Dave and Dave thank you and you keep right there but it will be back to wrap micron insight 2019 right after this short break from San Francisco you watching the cube

>> Live from Barcelona, Spain, it's theCUBE! Covering KubeCon and CloudNativeCon Europe 2019. Brought to you by Red Hat, the Cloud Native Computing Foundation, and ecosystem partners. >> Welcome back. This is theCUBE's live coverage of KubeCon, CloudNativeCon 2019 here in Barcelona, Spain. 7,700 here in attendance, here about all the Cloud Native technologies. I'm Stu Miniman; my cohost to the two days of coverage is Corey Quinn. And to help us break down what's happening in this ecosystem, we've brought in Jason Bloomberg, who's the president at Intellyx. Jason, thanks so much for joining us. >> It's great to be here. >> All right. There's probably some things in the keynote I want to talk about, but I also want to get your general impression of the show and beyond the show, just the ecosystem here. Brian Liles came out this morning. He did not sing or rap for us this morning like he did yesterday. He did remind us that the dinners in Barcelona meant that people were a little late coming in here because, even once you've got through all of your rounds of tapas and everything like that, getting that final check might take a little while. They did eventually filter in, though. Always a fun city here in Barcelona. I found some interesting pieces. Always love some customer studies. Conde Nast talking about what they've done with their digital imprint. CERN, who we're going to have on this program. As a science lover, you want to geek out as to how they're finding the Higgs boson and how things like Kubernetes are helping them there. And digging into things like storage, which I worked at a storage company for 10 years. So, understanding that storage is hard. Well, yeah. When containers came out, I was like, "Oh, god, we just fixed it for virtualization, "and it took us a decade. "How are we going to do it this time?" And they actually quoted a crowd chat that we had in our community. Tim Hawken, of course one of the first Kubernetes guys, was in on that. And we're going to have Tim on this afternoon, too. So, just to set a little context there. Jason, what's your impressions of the show? Anything that has changed in your mind from when you came in here to today? Let's get into it from there. >> Well, this is my second KubeCon. The first one I went to was in Seattle in December. What's interesting from a big picture is really how quickly and broadly KubeCon has been adopted in the enterprise. It's still, in the broader scheme of things, relatively new, but it's really taking its place as the only container orchestrator anybody cares about. It sort of squashed the 20-or-so alternative container orchestrators that had a brief day in the sun. And furthermore, large enterprises are rapidly adopting it. It's remarkable how many of them have adopted it and how broadly, how large the deployment. The Conde Nast example was one. But there are quite a number. So we turned the corner, even though it's relatively immature technology. That's the interesting story as well, that there's still pieces missing. It's sort of like flying an airplane while you're still assembling it, which makes it that much more exciting. >> Yeah, one of the things that has excited me over the last 10 years in tech is how fast it takes me to go from ideation to production, has been shrinking. Big data was: "Let's take the thing that used to take five years "and get it down to 18 months." We all remember ERP deployments and how much money and people you need to throw at that. >> It still takes a lot of money and people. >> Right, because it's ERP. I was talking to one of the booths here, and they were doing an informal poll of, "How many of you are going to have Kubernetes "in production in the next six months?" Not testing it, but in production in the next six months, and it was more than half of the people were going to be ramping it up in that kind of environment. Anything architecturally? What's intriguing you? What's the area that you're digging down to? We know that we are not fully mature, and even though we're in production and huge growth, there's still plenty of work to do. >> An interesting thing about the audience here is it's primarily infrastructure engineers. And the show is aimed at the infrastructure engineers, so it's technical. It's focused on people who code for a living at the infrastructure level, not at the application level. So you have that overall context, and what you end up having, then, is a lot of discussions about the various components. "Here's how we do storage." "Here's how we do this, here's how we do that." And it's all these pieces that people now have to assemble, as opposed to thinking of it overall, from the broader context, which is where I like writing about, in terms of the bigger picture. So the bigger picture is really that Cloud Native, broadly speaking, is a new architectural paradigm. It's more than just an architectural trend. It's set of trends that really change the way we think about architecture. >> One interesting piece about Kubernetes, as well. One of the things we're seeing as we see Kubernetes start to expand out is, unlike serverless, it doesn't necessarily require the same level of, oh, just take everything you've done and spend 18 months rewriting it from scratch, and then it works in this new paradigm in a better way. It's much less of a painful conversion process. We saw in the keynote today that they took WebLogic, of all things, and dropped that into Kubernetes. If you can do it with something as challenging, in some respects, and as monolithic as WebLogic, then almost any other stack you're going to see winds up making some sense. >> Right, you mentioned serverless in contrast with Kubernetes, but actually, serverless is part of this Cloud Native paradigm as well. So it's broader than Kubernetes, although Kubernetes has established itself as the container orchestration platform of choice. But it's really an overall story about how we can leverage the best practices we've learned from cloud computing across the entire enterprise IT landscape, both in the cloud and on premises. And Kubernetes is driving this in large part, but it's bigger picture than the technology itself. That's what's so interesting, because it's so transformative, but people here are thinking about trees, not the forest. >> It's an interesting thing you say there, and I'm curious if you can help our community, Because they look at this, and they're like, "Kubernetes, Kubernetes, Kubernetes." Well, a bunch of the things sit on Kubernetes. As they've tried to say, it's a platform of platforms. It's not the piece. Many of the things can be with Kubernetes but don't have to be. So, the whole observability piece. We heard the merging of the OpenCensus, OpenTracing with OpenTelemetry. You don't have to have Kubernetes for that to be a piece of it. It can be serverless underneath it. It can be all these other pieces. Cloud Native architecture sits on top of it. So when you say Cloud Native architecture, what defines that? What are the pieces? How do I have to do it? Is it just, I have to have meditated properly and had a certain sense of being? What do we have to do to be Cloud Native? >> Well, an interesting way of looking at it is: What we have subtracted from the equation, so what is intentionally missing. Cloud Native is stateless, it is codeless, and it is trustless. Now, not to say that we don't have ways of dealing with state, and of course there's still plenty of code, and we still need trust. But those are architectural principals that really percolate through everything we do. So containers are inherently stateless; they're ephemeral. Kubernetes deals with ephemeral resources that come and go as needed. This is key part of how we achieve the scale we're looking for. So now we have to deal with state in a stateless environment, and we need to do that in a codeless way. By codeless, I mean declarative. Instead of saying, how are we going to do something? Let's write code for that, we're going to say, how are we going to do that? Let's write a configuration file, a YAML file, or some other declarative representation of what we want to do. And Kubernetes is driven this way. It's driven by configuration, which means that you don't need to fork it. You don't need to go in and monkey with the insides to do something with it. It's essentially configurable and extensible, as opposed to customizable. This is a new way of thinking about how to leverage open-source infrastructure software. In the past, it was open-source. Let's go in an monkey with the code, because that's one of the benefits of open-source. Nobody wants to do that now, because it's declaratively-driven, and it's configurable. >> Okay, I hear what you're saying, and I like what you're saying. But one of the things that people say here is everyone's a little bit different, and it is not one solution. There's lots of different paths, and that's what's causing a little bit of confusion as to which service mesh, or do I have a couple of pieces that overlap. And every deployment that I see of this is slightly different, so how do I have my cake and eat it, too? >> Well, you mentioned that Kubernetes is a platform of platforms, and there's little discussion of what we're actually doing with the Kubernetes here at the show. Occasionally, there's some talk about AI, and there's some talk about a few other things, but it's really up to the users of Kubernetes, who are now the development teams in the enterprises, to figure out what they want to do with it and, as such, figure out what capabilities they require. Depending upon what applications you're running and the business use cases, you may need certain things more than others. Because AI is very different from websites, it's very different from other things you might be running. So that's part of the benefit of a platform of platforms, is it's inherently configurable. You can pick and choose the capabilities you want without having to go into Kubernetes and fork it. We don't want 12 different Kubernetes that are incompatible with each other, but we're perfectly okay with different flavors that are all based on the same, fundamental, identical code base. >> We take a look at this entire conference, and it really comes across as, yes, it's KubeCon and CloudNativeCon. We look at the, I think, 36 projects that are now being managed by this. But if we look at the conversations of what's happening here, it's very clear that the focus of this show is Kubernetes and friends, where it tends to be taking the limelight of a lot of this. One of the challenges you start seeing as soon as you start moving up the stack, out through the rest of the stack, rather, and seeing what all of these Cloud Native technologies are is, increasingly, they're starting to be defined by what they aren't. I mean, you have the old saw of, serverless runs on servers, and other incredibly unhelpful sentiments. And we talk about what things aren't more so than we do what they are. And what about capabilities story? I don't have an answer for this. I think it's one of those areas where language is hard, and defining what these things are is incredibly difficult. But I see what you're saying. We absolutely are seeing a transformative moment. And one of the strangest things about it, to me at least, is the enthusiasm with which we're seeing large enterprises, that you don't generally think of as being particularly agile or fast-moving, are demonstrating otherwise. They're diving into this in fascinating ways. It's really been enlightening to have conversations for the last couple of days with companies that are embracing this new paradigm. >> Right. Well, in our perspective at Intellyx, we're focusing on digital transformation in the enterprise, which really means putting the customer first and having a customer-driven transformation of IT, as well as the organization itself. And it's hard to think in those terms, in customer-facing terms, when you're only talking about IT infrastructure. Be that as it may, it's still all customer-driven. And this is sometimes the missing piece, is how do we connect what we're doing on the infrastructure side with what customers require from these companies that are implementing it? Often, that missing piece centers on the workload. Because, from the infrastructure perspective, we have a notion of a workload, and we want workload portability. And portability is one of the key benefits of Kubernetes. It gives us a lot of flexibility in terms of scalability and deployment options, as well as resilience and other benefits. But the workload also represents the applications we're putting in front of our end users, whether they're employees or end customers. So that's they key piece that is like the keystone that ties the digital story, that is the customer-facing, technology-driven, technology-empowered story, with the IT infrastructure stories. How do we support the flexibility, scalability, resilience of the workloads that the business needs to meet its business goals? >> Yeah, I'm really glad you brought up that digital transformation piece, because I have two questions, and I want to make sure I'm allowing you to cover both of them. One is, the outcome we from people as well: "I need to be faster, and I need to be agile." But at the same point, which pieces should I, as an enterprise, really need to manage? Many of these pieces, shouldn't I just be able to consume it as a managed service? Because I don't need to worry about all of those pieces. The Google presentation this morning about storage was: You have two options. Path one is: we'll take care of all of that for you. Path two is: here's the level of turtles that you're going to go all the way down, and we all know how complicated storage is, and it's got to work. If I lose my state, if I lose my pieces there, I'm probably out of business or at least in really big trouble. The second piece on that, you talked about the application. And digital transformation. Speed's great and everything, but we've said at Wikibon that the thing that will differentiate the traditional companies and the digitally transformed is data will drive your business. You will have data, it will add value of business, and I don't feel that story has come out yet. Do you see that as the end result from this? And apologies for having two big, complex questions here for you. >> Well, data are core to the digital transformation story, and it's also an essential part of the Kubernetes story. Although, from the infrastructure perspective, we're really thinking more about compute than about data. But of course, everything boils down to the data. That is definitely always a key part of the story. And you're talking about the different options. You could run it yourself or run it as a managed service. This is a key part of the story as well, is that it's not about making a single choice. It's about having options, and this is part of the modern cloud storage. It's not just about, "Okay, we'll put everything in one public cloud." It's about having multiple public clouds, private clouds, on-premises virtualization, as well as legacy environments. This is what you call hybrid IT. Having an abstracted collection of environments that supports workload portability in order to meet the business needs for the infrastructure. And that workload portability, in the context of multiple clouds, that is becoming increasingly dependent on Kubernetes as an essential element of the infrastructure. So Kubernetes is not the be-all and end-all, but it's become an essentially necessary part of the infrastructure, to make this whole vision of hybrid IT and digital transformation work. >> For now. I mean, I maintain that, five years from now, no one is going to care about Kubernetes. And there's two ways that goes. Either it dries up, blows away, and something else replaces it, which I don't find likely, or, more likely, it slips beneath the surface of awareness for most people. >> I would agree, yeah. >> The same way that we're not sitting here, having an in-depth conversation about which distribution of Linux, or what Linux kernel or virtual memory manager we're working with. That stuff has all slipped under the surface, to the point where there are people who care tremendously about this, but you don't need to employ them at every company. And most companies don't even have to think about it. I think Kubernetes is heading that direction. >> Yeah, it looks like it. Obviously, things continue to evolve. Yeah, Linux is a good example. TCP/IP as well. I remember the network protocol wars of the early 90s, before the web came along, and it was, "Are we going to use Banyan VINES, "are we going to use NetWare?" Remember NetWare? "Or are we going to use TCP/IP or Token Ring?" Yeah! >> Thank you. >> We could use GDP, but I don't get it. >> Come on, KOBOL's coming back, we're going to bring back Token Ring, too. >> KOBOL never went away. Token Ring, though, it's long gone. >> I am disappointed in Corey, here, for not asking the question about portability. The concern we have, as you say: okay, I put Kubernetes in here because I want portability. Do I end up with least-common-denominator cloud? I'm making a decision that I'm not going to go deep on some of the pieces, because nice as the IPI lets things through, but we understand if I need to work across multiple environments, I'm usually making a trade-off there. What do you hear from customers? Are they aware that they're doing this? Is this a challenge for people, not getting the full benefit out of whichever primary or whichever clouds they are using? >> Well, portability is not just one thing. It's actually a set of capabilities, depending upon what you are trying to accomplish. So for instance, you may want to simply support backing up your workload, so you want to be able to move it from here to there, to back it up. Or you may want to leverage different public clouds, because different public clouds have different strengths. There may be some portability there. Or you may be doing cloud migration, where you're trying to move from on-premises to cloud, so it's kind of a one-time portability. So there could be a number of reasons why portability is important, and that could impact what it means to you, to move something from here to there. And why, how often you're going to do it, how important it is, whether it's a one-to-many kind of thing, or it's a one-to-one kind of thing. It really depends on what you're trying to accomplish. >> Jason, last thing real quick. What research do you see coming out of this? What follow-up? What should people be looking for from Intellyx in this space in the near future? >> Well, we continue to focus on hybrid IT, which include Kubernetes, as well as some of the interesting trends. One of the interesting stories is how Kubernetes is increasingly being deployed on the edge. And there's a very interesting story there with edge computing, because the telcos are, in large part, driving that, because of their 5G roll-outs. So we have this interesting confluence of disruptive trends. We have 5G, we have edge computing, we have Kubernetes, and it's also a key use case for OpenStack, as well. So it's like all of these interesting trends are converging to meet a new class of challenges. And AI is part of that story as well, because we want to run AI at the edge, as well. That's the sort of thing we do at Intellyx, is try to take multiple disruptive trends and show the big picture overall. And for my articles for SiliconANGLE, that's what I'm doing as well, so stay tuned for those. >> All right. Jason Bloomberg, thank you for helping us break down what we're doing in this environment. And as you said, actually, some people said OpenStack is dead. Look, it's alive and well in the Telco space and actually merging into a lot of these environments. Nothing ever dies in IT, and theCUBE always keeps rolling throughout all the shows. For Corey Quinn, I'm Stu Miniman. We have a full-packed day of interviews here, so be sure to stay with us. And thank you for watching theCUBE. (upbeat techno music)

>> Live from New York City it's theCube. Covering Lenovo Transform 2.0. Brought to you by Lenovo. >> Welcome back to theCube's live coverage of Lenovo Transform, I'm your host Rebecca Knight along with my co-host Stu Miniman. We're joined by Madhu Matta; He is the VP and GM High Performance Computing and Artificial Intelligence at Lenovo and Dr. Daniel Gruner the CTO of SciNet at University of Toronto. Thanks so much for coming on the show gentlemen. >> Thank you for having us. >> Our pleasure. >> So, before the cameras were rolling, you were talking about the Lenovo mission in this area to use the power of supercomputing to help solve some of society's most pressing challenges; and that is climate change, and curing cancer. Can you talk a little bit, tell our viewers a little bit about what you do and how you see your mission. >> Yeah so, our tagline is basically, Solving humanity's greatest challenges. We're also now the number one supercomputer provider in the world as measured by the rankings of the top 500 and that comes with a lot of responsibility. One, we take that responsibility very seriously, but more importantly, we work with some of the largest research institutions, universities all over the world as they do research, and it's amazing research. Whether it's particle physics, like you saw this morning, whether it's cancer research, whether it's climate modeling. I mean, we are sitting here in New York City and our headquarters is in Raleigh, right in the path of Hurricane Florence, so the ability to predict the next anomaly, the ability to predict the next hurricane is absolutely critical to get early warning signs and a lot of survival depends on that. So we work with these institutions jointly to develop custom solutions to ensure that all this research one it's powered and second to works seamlessly, and all their researchers have access to this infrastructure twenty-four seven. >> So Danny, tell us a little bit about SciNet, too. Tell us what you do, and then I want to hear how you work together. >> And, no relation with Skynet, I've been assured? Right? >> No. Not at all. It is also no relationship with another network that's called the same, but, it doesn't matter. SciNet is an organization that's basically the University of Toronto and the associated research hospitals, and we happen to run Canada's largest supercomputer. We're one of a number of computer sites around Canada that are tasked with providing resources and support, support is the most important, to academia in Canada. So, all academics, from all the different universities, in the country, they come and use our systems. From the University of Toronto, they can also go and use the other systems, it doesn't matter. Our mission is, as I said, we provide a system or a number of systems, we run them, but we really are about helping the researchers do their research. We're all scientists. All the guys that work with me, we're all scientists initially. We turned to computers because that was the way we do the research. You can not do astrophysics other than computationally, observationally and computationally, but nothing else. Climate science is the same story, you have so much data and so much modeling to do that you need a very large computer and, of course, very good algorithms and very careful physics modeling for an extremely complex system, but ultimately it needs a lot of horsepower to be able to even do a single simulation. So, what I was showing with Madhu at that booth earlier was results of a simulation that was done just prior us going into production with our Lenovo system where people were doing ocean circulation calculations. The ocean is obviously part of the big Earth system, which is part of the climate system as well. But, they took a small patch of the ocean, a few kilometers in size in each direction, but did it at very, very high resolution, even vertically going down to the bottom of the ocean so that the topography of the ocean floor can be taken into account. That allows you to see at a much smaller scale the onset of tides, the onset of micro-tides that allow water to mix, the cold water from the bottom and the hot water from the top; The mixing of nutrients, how life goes on, the whole cycle. It's super important. Now that, of course, gets coupled with the atmosphere and with the ice and with the radiation from the sun and all that stuff. That calculation was run by a group from, the main guy was from JPL in California, and he was running on 48,000 cores. Single runs at 48,000 cores for about two- to three-weeks and produced a petabyte of data, which is still being analyzed. That's the kind of resolution that's been enabled... >> Scale. >> It gives it a sense of just exactly... >> That's the scale. >> By a system the size of the one we have. It was not possible to do that in Canada before this system. >> I tell you both, when I lived on the vendor side and as an analyst, talking to labs and universities, you love geeking out. Because first of all, you always have a need for newer, faster things because the example you just gave is like, "Oh wait." "If I can get the next generation chipset." "If the networking can be improved." You know you can take that petabyte of data and process it so much faster. >> If I could only get more money to buy a bigger one. >> We've talked to the people at CERN and JPL and things like that. - Yeah. >> And it's like this is where most companies are it's like, yeah it's a little bit better, and it might make things a little better and make things nice, but no, this is critical to move along the research. So talk a little bit more about the infrastructure and what you look for and how that connects to the research and how you help close that gap over time. >> Before you go, I just want to also highlight a point that Danny made on solving humanity's greatest challenges which is our motto. He talked about the data analysis that he just did where they are looking at the surface of the ocean, as well as, going down, what is it, 264 nautical layers underneath the ocean? To analyze that much data, to start looking at marine life and protecting marine life. As you start to understand that level of nautical depth, they can start to figure out the nutrients value and other contents that are in that water to be able to start protecting the marine life. There again, another of humanity's greatest challenge right there that he's giving you... >> Nothing happens in isolation; It's all interconnected. >> Yeah. >> When you finally got a grant, you're able to buy a computer, how do you buy the computer that's going to give you the most bang for your buck? The best computer to do the science that we're all tasked with doing? It's tough, right? We don't fancy ourselves as computer architects; we engage the computer companies who really know about architecture to help us do it. The way we did our procurement was, 'Ok vendors, we have a set pot of money, we're willing to spend every last penny of this money, you give us the biggest and the baddest for our money." Now, it has to have a certain set of criteria. You have to be able to solve a number of benchmarks, some sample calculations that we provided. The ones that give you the best performance that's a bonus. It also has to be able to do it with the least amount of power, so we don't have to heat up the world and pay through the nose with power. Those are objective criteria that anybody can understand. But then, there's also the other criteria, so, how well will it run? How is it architected? How balanced is it? Did we get the iOS sub-system for all the storage that was the one that actually meets the criteria? What other extras do we have that will help us make the system run in a much smoother way and for a wide variety of disciplines because we run the biologists together with the physicists and the engineers and the humanitarians, the humanities people. Everybody uses the system. To make a long story short, the proposal that we got from Lenovo won the bid both in terms of what we got for in terms of hardware and also the way it was put together, which was quite innovative. >> Yeah. >> I want to hear about, you said give us the biggest, the baddest, we're willing to empty our coffers for this, so then where do you go from there? How closely do you work with SciNet, how does the relationship evolve and do you work together to innovate and kind of keep going? >> Yeah. I see it as not a segment or a division. I see High Performance Computing as a practice, and with any practice, it's many pieces that come together; you have a conductor, you have the orchestra, but the end of the day the delivery of that many systems is the concert. That's the way to look at it. To deliver this, our practice starts with multiple teams; one's a benchmarking team that understands the application that Dr. Gruner and SciNet will be running because they need to tune to the application the performance of the cluster. The second team is a set of solution architects that are deep engineers and understand our portfolio. Those two work together to say against this application, "Let's build," like he said, "the biggest, baddest, best-performing solution for that particular application." So, those two teams work together. Then we have the third team that kicks in once we win the business, which is coming on site to deploy, manage, and install. When Dr. Gruner talks about the infrastructure, it's a combination of hardware and software that all comes together and the software is open-source based that we built ourselves because we just felt there weren't the right tools in the industry to manage this level of infrastructure at that scale. All this comes together to essentially rack and roll onto their site. >> Let me just add to that. It's not like we went for it in a vacuum. We had already talked to the vendors, we always do. You always go, and they come to you and 'when's your next money coming,' and it's a dog and pony show. They tell you what they have. With Lenovo, at least the team, as we know it now, used to be the IBM team, iXsystems team, who built our previous system. A lot of these guys were already known to us, and we've always interacted very well with them. They were already aware of our thinking, where we were going, and that we're also open to suggestions for things that are non-conventional. Now, this can backfire, some data centers are very square they will only prescribe what they want. We're not prescriptive at all, we said, "Give us ideas about what can make this work better." These are the intangibles in a procurement process. You also have to believe in the team. If you don't know the team or if you don't know their track record then that's a no-no, right? Or, it takes points away. >> We brought innovations like DragonFly, which Dr. Dan will talk about that, as well as, we brought in for the first time, Excelero, which is a software-defined storage vendor and it was a smart part of the bid. We were able to flex muscles and be more creative versus just the standard. >> My understanding, you've been using water cooling for about a decade now, maybe? - Yes. >> Maybe you could give us a little bit about your experiences, how it's matured over time, and then Madhu will talk and bring us up to speed on project Neptune. >> Okay. Our first procurement about 10 years ago, again, that was the model we came up with. After years of wracking our brains, we could not decide how to build a data center and what computers to buy, it was like a chicken and egg process. We ended up saying, 'Okay, this is what we're going to do. Here's the money, here's is our total cost of operation that we can support." That included the power bill, the water, the maintenance, the whole works. So much can be used for infrastructure, and the rest is for the operational part. We said to the vendors, "You guys do the work. We want, again, the biggest and the baddest that we can operate within this budget." So, obviously, it has to be energy efficient, among other things. We couldn't design a data center and then put in the systems that we didn't know existed or vice-versa. That's how it started. The initial design was built by IBM, and they designed the data center for us to use water cooling for everything. They put rear door heat exchanges on the racks as a means of avoiding the use of blowing air and trying to contain the air which is less efficient, the air, and is also much more difficult. You can flow water very efficiently. You open the door of one of these racks. >> It's amazing. >> And it's hot air coming out, but you take the heat, right there in-situ, you remove it through a radiator. It's just like your car radiator. >> Car radiator. >> It works very well. Now, it would be nice if we could do even better by doing the hot water cooling and all that, but we're not in a university environment, we're in a strip mall out in the boonies, so we couldn't reuse the heat. Places like LRZ they're reusing the heat produced by the computers to heat their buildings. >> Wow. >> Or, if we're by a hospital, that always needs hot water, then we could have done it. But, it's really interesting how the option of that design that we ended up with the most efficient data center, certainly in Canada, and one of the most efficient in North America 10 years ago. Our PUE was 1.16, that was the design point, and this is not with direct water cooling through the chip. >> Right. Right. >> All right, bring us up to speed. Project Neptune, in general? >> Yes, so Neptune, as the name suggests, is the name of the God of the Sea and we chose that to brand our entire suite of liquid cooling products. Liquid cooling products is end to end in the sense that it's not just hardware, but, it's also software. The other key part of Neptune is a lot of these, in fact, most of these, products were built, not in a vacuum, but designed and built in conjunction with key partners like Barcelona Supercomputer, LRZ in Germany, in Munich. These were real-life customers working with us jointly to design these products. Neptune essentially allows you, very simplistically put, it's an entire suite of hardware and software that allows you to run very high-performance processes at a level of power and cooling utilization that's like using a much lower processor, it dissipates that much heat. The other key part is, you know, the normal way of cooling anything is run chilled water, we don't use chilled water. You save the money of chillers. We use ambient temperature, up to 50 degrees, 90% efficiency, 50 degree goes in, 60 degree comes out. It's really amazing, the entire suite. >> It's 50 Celsius, not Fahrenheit. >> It's Celsius, correct. >> Oh. >> Dr. Bruner talked about SciNet with the rado-heat exchanger. You actually got to stand in front of it to feel the magic of this, right? As geeky as that is. You open the door and it's this hot 60-, 65-degree C air. You close the door it's this cool 20-degree air that's coming out. So, the costs of running a data center drop dramatically with either the rado-heat exchanger, our direct to node product, which we just got released the SE650, or we have something call the thermal-transfer module, which replaces a normal heat sink. Where for an air cool we bring water cool goodness to an air cool product. >> Danny, I wonder if you can give us the final word, just the climate science in general, how's the community doing? Any technological things that are holding us back right now or anything that excites you about the research right now? >> Technology holds you back by the virtual size of the calculations that you need to do, but, it's also physics that hold you back. >> Yes. Because doing the actual modeling is very difficult and you have to be able to believe that the physics models actually work. This is one of the interesting things that Dick Peltier, who happens to be our scientific director and he's also one of the top climate scientists in the world, he's proven through some of his calculations that the models are actually pretty good. The models were designed for current conditions, with current data, so that they would reproduce the evolution of the climate that we can measure today. Now, what about climate that started happening 10,000 years ago, right? The climate was going on; it's been going on forever and ever. There's been glaciations; there's been all these events. It turns out that it has been recorded in history that there are some oscillations in temperature and other quantities that happen about every 1,000 years and nobody had been able to prove why they would happen. It turns out that the same models that we use for climate calculations today, if you take them back and do what's called paleoclimate, you start with approximating the conditions that happened 10,000 years ago, and then you move it forward, these things reproduce, those oscillations, exactly. It's very encouraging that the climate models actually make sense. We're not talking in a vacuum. We're not predicting the end of the world, just because. These calculations are right. They're correct. They're predicting the temperature of the earth is climbing and it's true, we're seeing it, but it will continue unless we do something. Right? It's extremely interesting. Now he's he's beginning to apply those results of the paleoclimate to studies with anthropologists and archeologists. We're trying to understand the events that happened in the Levant in the Middle East thousands of years ago and correlate them with climate events. Now, is that cool or what? >> That's very cool. >> So, I think humanity's greatest challenge is again to... >> I know! >> He just added global warming to it. >> You have a fun job. You have a fun job. >> It's all the interdisciplinarity that now has been made possible. Before we couldn't do this. Ten years ago we couldn't run those calculations, now we can. So it's really cool. - Amazing. Great. Well, Madhu, Danny, thank you so much for coming on the show. >> Thank you for having us. >> It was really fun talking to you. >> Thanks. >> I'm Rebecca Knight for Stu Miniman. We will have more from the Lenovo Transform just after this. (tech music)

>> Live, from Washington DC, it's theCube, covering the AWS Public Sector Summit 2018. Brought to you by Amazon Web Services, and its ecosystem partners. (techno music) >> Welcome back everyone it's theCube's exclusive coverage here, day two of the Amazon Web Sources public sector summit. This is the public sector across the globe. This is their reinvent, this is their big event. I'm John Furrier, Stu Miniman, and also David Vellante's been here doing interviews. Our next guest is, we got Doug Van Dyke, he's the director of U.S. Federal Civilian and Non Profit Sectors of the group, welcome to theCube, good to see you. >> John, thank you very much for having me. >> So you've been in the federal, kind of game, and public sector for a while. You've known, worked with Theresa, at Microsoft before she came to Reinvent. >> 15 years now. >> How is she doing? >> She's doing great, we saw her on main stage yesterday. Force of nature, love working with her, love working for her. This is, like you were saying, this is our re-invent here in D.C. and 14,000 plus, 15,000 registrations, she's on the top of her game. >> What I'm really impressed with her and your team as well, is the focus on growth, but innovation, right? it's not just about, knock down the numbers and compete. Certainly you're competing against people who are playing all kinds of tricks. You got Oracle out there, you got IBM, we've beaten at the CIA. It's a street battle out there in this area in D.C. You guys are innovative, in that you're doing stuff with non-profits, you got mission driven, you're doing the educate stuff, so it's not just a one trick pony here. Take us through some of the where you guys heads are at now, because you're successful, everyone's watching you, you're not small anymore. What's the story? >> So, I think the differentiator for us is our focus on the customers. You know, we've got a great innovation story at the Department of Veterans Affairs with vets.gov. So five years ago if a veteran went out to get the services that the government was going to provide them, they've have to pick from 200 websites. It just wasn't to navigate through 200 websites. So, the innovation group at Veteran's Affairs, the digital services team, figured out, let's pull this all together under a single portal with vets.gov. It's running on AWS, and now veterans have a single interface into all the services they want. >> Doug, one of the things I've been impressed, my first year coming to this. I've been to many other AWS shows, but you've got all these kind of overlapping communities. Of course, the federal government, plus state and local, education. You've got this civilian agencies, so give us a little bit of flavor about that experience here at the show. What trends your hearing from those customers. >> So what's great for me is I've been here almost six and a half years, and I've seen the evolution. And you know, there were the early customers who were just the pioneers like Tom Soderstrom, from JPL, who was on main stage. And then we saw the next wave where there were programs that needed a course correction, like Center for Medicare Medicaid with Healthcare.gov. Where Amazon Web Services came in, took over, helped them with the MarketPlace, you know, get that going. And now we're doing some great innovative things at CMS, aggregating data from all 50 states, about 75 terabytes, so they can do research on fraud, waste, and abuse that they couldn't do before. So we're helping our customers innovate on the cloud, and in the cloud, and it's been a great opportunity. >> Oh my God, I had the pleasure of interviewing Tom Soderstrom two years ago. >> Okay. >> Everybody gets real excited when you talk about space. It's easy to talk about innovation there, but you know, talk about innovation throughout the customers, because some people will look at it, and be like, oh come on, government and their bureaucracy, and they're behind. What kind of innovation are you hearing from your customers? >> So there's an exciting with Department of Energy. They, you know there's a limited amount of resources that you have on premise. Well, they're doing research on the large Hadron Collider in Cern, Switzerland. And they needed to double the amount of capacity that they had on premise. So, went to the AWS cloud, fired up 50,000 cores, brought the data down, and they could do research on it. And so, we're making things possible that couldn't be done previously. >> What are some of the examples that government entities and organizations are doing to create innovation in the private sector? Cause the private sector's been the leader to the public sector, and know you're seeing people starting to integrate it. I mean, half the people behind us, that are exhibiting here, are from the commercial side doing business in the public sector. And public sector doing, enabling action in the private sector. Talk about that dynamic, cause it's not just public sector. >> Right. >> Can you just share your? >> These public, private. Great example with NOAA, the National Oceanic Atmospheric Administration. They have a new program called NEXRAD. It's the next generation of doppler radar. They have 160 stations across the world, collecting moisture, air pressure, all of the indicators that help predict the weather. They partner with us at AWS to put this data out, and through our open data program. And then organizations like the Weather Bug can grab that information, government information, and use it to build the application that you have on your I-Phone that predicts the weather. So you know whether to bring an umbrella to work tomorrow. >> So you're enabling the data from, or stuff from the public, for private, entrepreneurial activity? >> Absolutely. >> Talk about the non-profits. What's going on there? Obviously, we heard som stuff on stage with Teresa. The work she's showcasing, a lot of the non-profit. A lot of mission driven entrepreneurships happening. Here in D.C, it's almost a Silicon Valley like dynamic, where stuff that was never funded before is getting funded because they can do Cloud. They can stand it up pretty quickly and get it going. So, you're seeing kind of a resurgence of mission driven entrepreneurships. What is the nonprofit piece of it look now for AWS? How do you talk about that? >> Sure. Well again, one of the areas that I'm really passionate about being here, and being one of the people who helped start our nonprofit vertical inside of AWS, we now have over 12, I'm sorry, 22,000 nonprofits using AWS to keep going. And the mission of our nonprofit vertical is just to make sure that no nonprofit would ever fail for lack of infrastructure. So we partnered with Tech Soup, which is an organization that helps vet and coordinate our Cloud credits. So nonprofits, small nonprofit organizations can go out through Tech Soup, get access to credits, so they don't have to worry about their infrastructure. And you know we.. >> Free credits? >> Those credits, with the Tech Soup membership, they get those, yeah, and using the word credit, it's more like a grant of AWS cloud. >> You guys are enabling almost grants. >> Yes, cloud grants. Not cash grants, but cloud grants. >> Yeah, yeah great. So, how is that converting for you, in your mind? Can you share some examples of some nonprofits that are successful? >> Sure. A great presentation, and I think it was your last interview. A game changer. Where these smaller nonprofits can have a really large impact. And, but then we're also working with some of the larger nonprofits too. The American Heart Association, that built their precision medicine platform to match genotype, phenotype information, so we can further cardiovascular research. They have this great mission statement, they want to reduce cardiovascular disease by 20 percent by 2020. And we're going to help them do that. >> You guys are doing a great job, I got to say. It's been fun to watch, and now, we've been covering you guys for the past two years now, here at the event. A lot more coming on, in D.C. The CIA went in a few years ago. Certainly a shot heard around the cloud. That's been well documented. The Department of Defense looking good off these certain indicators. But, what's going on in the trends in the civilian agencies? Can you take a minute to give an update on that? >> Yeah, so I started earlier saying I've seen the full spectrum. I saw the very beginning, and then I've seen all the way to the end. Where, I think it was three years ago at this event, I talked to Joe Piva, who is the former CIO for the Department of Commerce ITA, the International Trade Association. He had data center contracts coming up for renewal. And he made a really brave decision to cancel those contracts. So he had 18 months to migrate the entire infrastructure for ITA over on to AWS. And you know, there's nothing like an impending date to move. So, we've got agencies that are going all in on AWS, and I think that's just a sign of the times. >> Data centers, I mean anyone who were startup nine years into it, we've never had a data center. I think most startups don't.. >> Born in the cloud. >> Born in the cloud. Thanks so much Dave, for coming on. Appreciate the time. Congratulations on your success. AWS public sector doing great, global public sector. You guys are doing great. Building nations, we had Baharain on as well. Good luck, and the ecosystems looks good. You guys did a good job. So, congratulations. >> John, Stu, thank you very much for having me here today. >> Live coverage here, we are in Washington D.C. For Cube. Coverage of AWS Public Sector Summit. We'll be back with more. Stay with us, we've got some more interviews after this short break. (techno music)

(upbeat music) >> Announcer: Live from Vancouver, Canada. It's theCUBE covering OpenStack Summit North America 2018. Brought to you by Red Hat, the OpenStack Foundation, and its ecosystem partners. >> Welcome back. This is theCUBE's exclusive coverage of OpenStack Summit 2018 in Vancouver. I'm Stu Miniman with my co-host John Troyer. Happy to welcome back to the program two CUBE alums. We have Alan Clark, who's the board chair of the OpenStack Foundation and in the CTO office of SUSE. >> Yep, thank you. >> Thanks for joining us again. It's been a few years. >> It's been a while, I appreciate being back. >> And Lew Tucker, the vice chair of the OpenStack Foundation and vice president and CTO of Cisco. Lew, it's been weeks. >> Exactly right. >> All right. >> I've become a regular here. >> Yeah, absolutely. So, first of all, John Furrier sent his regard. He wishes he was here, you know. John's always like come on Lew and I, everybody, we were talking about when this Kubernetes thing started and all the conferences, so it's been a pleasure for us to be here. Six years now at this show, as well as some of the remote days and other things there. It's been fun to watch the progressions of-- >> Isn't it amazing how far we've come? >> Yeah, absolutely. Here's my first question for you, Alan. On the one hand, I want you to talk about how far we've gone. But the other thing is, people, when they learn about something, whenever they first learn about it tends to fossilize in their head, this is what it is and always will be. So I think most people know that this isn't the Amazon killer or you know it's free VMware. That we talked about years ago. Bring us a little bit of that journey. >> Well, so, you know, it started with the basic compute storage and as we've watched open-source grow and adoption of open-source grow, the demands on services grow. We're in this transformation period where everything's growing and changing very rapidly. Open-source is driving that. OpenStack could not stay static. When it started, it solved a need, but the needs continued to grow and continued to change. So it's not surprising at all that OpenStack has grown and changed and will continue to grow and change. >> So Lew, it's been fascinating for me, you know. I've worked with and all these things with Cisco and various pieces for my entire career. You're here wearing the OpenStack @ Cisco shirt. And Cisco's journey really did through that to digital transformation themselves. When I talked to Rowan at Cisco Live Barcelona, the future of Cisco is as a software company. So, help set OpenStack into that kind of broader picture. >> Sure, I think one of the aspects of that is that we're seeing now it is becoming this multi-cloud world. And that we see all of our customers are running in the public cloud. They have their own private data centers. And what they're looking for is they want their whole development model and everything else to now become targeted towards that multi-cloud world. They're going to do services in the public cloud, they still have their private data center. OpenStack is a place for them to actually meet and run all their services 'cause now you can build your environment within your data center that makes it look very much like your public cloud, so your developers don't have two completely different mindsets. They have the same one, it's extracting resources on demand. And that one, we're putting on top of that other newer technology that's coming, such as Kubernetes. We've got a real consistency between those environments. >> Yeah, please Alan. >> I was going to say, it enables you to leverage your existing infrastructure so you don't want to make them, particularly those SUSE's customers, they don't want us to come in and say throw everything away, start afresh right? But at the same time, you've got to be able to embrace what's new and what's coming. We're talking about many new technologies here in OpenStack Summit today right? Containers and all sorts of stuff. A lot of those things are still very new to our customers and they're preparing for that. As Lew said, we're building that infrastructure. >> One of the things, as I'm thinking about it, some people look at, they look at codec containers and some of these pieces outside of the OpenStack project and they're like, well what's the Foundation doing? But I believe it should be framed, and please, please, I would love your insight on this, in that multi-cloud discussion because this is, it can't just be, well, this is how you build private. It needs to be, this is how you live in this multi-cloud environment. >> That's why I think, you're beginning to see us talk about open infrastructure. And this is using open-source software to use software to manage your infrastructure and build it out instead of configuration, cabling, having guys going out, plugging in, unplugging network ports and whatever. We want software and automations to do all that, so OpenStack is one of the cloud platforms. But these other projects are now coming into the Foundation, which also expand that notion of open infrastructure, and that's why we're seeing these projects expand. >> Lew's exactly right and it goes beyond that. Back in 2017, early 2017, we recognized, as a board, that it's not going to be just about the projects within OpenStack. We have to embrace our adjacent communities and embrace those technologies. So that's why you're hearing a lot about Kubernetes and containers and networking and all sorts of projects that are not necessarily being done within OpenStack but you're seeing how we're collaborating with all those other communities. >> And codec is a perfect example of that. Codec containers came out of those clear containers. It's now combining the best of both worlds, 'cause now you get the speed of containers bringing up, but you get the security and isolation of virtual machines. That's important in the OpenStack community, in our world, because that's what we want out of our clouds. >> Well you both have just mentioned community a few times. I saw one thing coming in to this conference, I'm so impressed by the prominence of community. It's up on stage from the first minutes of the first keynote. People, the call to action, the pleas, for the folks, some of us have been here years and years, for the new folks, please come meet us right? That's really inviting, it's very clear that this is a community. >> Yeah I was surprised, actually, 'cause we saw it when we were asked when up on stage how many people were here for the first time? More than half the audience raised their hand. >> Alan: I was surprised by that as well. >> That was the real surprise. And at the same time, we're seeing, increasingly, users of OpenStack coming in as opposed the people who are in core projects. We're seeing Progressive insurance coming in. We're seeing Adobe Marketing Cloud having over 100,000 cores running OpenStack. That's in addition to what we've had with Walmart and others so the real users are coming. So our communities, not just the developers but the users of OpenStack and the operators. >> That's always an interesting intention for an open-source project right. You have the open-source contributors, and then you have the users and operators. But here at the show right? All of these different technology tracks. Part of community is identity. And so, as the technical work has been split-off, and is actually at another event, these are the users. But it does, with all these other technology conversations, I wonder what the core identity of, I'm an OpenStack member, like what does that end up meaning in a world of open infrastructure? if the projects, if the OpenStack itself is more mature, and as we get up the letters of the alphabet towards Z, How do you all want to steer what it means to be a member of the OpenStack community. >> We met on Sunday as a joint leadership. So we had, it wasn't just a board meeting, it was a meeting with the technical committee, it was a meeting with the user committee. So we're very much pushing to make sure we have those high interactions, that the use cases are getting translated into requirements and getting translated into blueprints and so forth. We're working very, very hard to make sure we have that communication open. And I think one of the things that sets the OpenStack community apart is what we call our Four Opens. We base everything on our Four Opens and one of those is communication, transparency and communication. And that's what people are finding enticing. And one of the big reasons is I think they're coming to OpenStack to do that innovation and collaboration. >> We've seen the same thing with Linux, for example. Linux is no longer just the operating system when people think about the Linux community. Linux community is the operating system and then all of these other projects associated with them. That's the same thing that we're seeing with OpenStack. That's why we're continuing to see, wherever there's a need as people are deploying OpenStack and operating it and running it, all of these other open-source components are coming into it because that's what they really were running, that conglomerate of projects around it. >> Certainly, the hype cycle, and maybe Linux went through it's own hype cycle, back in the day and I'm from Silicon Valley. I think the hype cycle outside the community and what's actually happening on the ground here actually are meshed quite well. What I saw this week, like you said, real users, big users, infrastructure built into every bank, transport, telecom in the world. That's a global necessary part of the infrastructure of our planet. So outside of investment, things like that-- >> Well I hope you can help us get the message out. Because that is, a major thing that we see and we experience the conf, people who are not here. They still, then maybe look at OpenStack the way it was, maybe, four years ago, and it was difficult to deploy, and people were struggling with it, and there was a lot of innovation happening at a very, very fast rate. Well now, it's proven, it's sort of industrial grade, it's being deployed at a very large scale across many, many industries. >> Well it's interesting. Remember, Lew, when we were talking about ethernet fabrics. We would talk about some of SDN and some of these big things. Well, look sometimes these things are over-hyped. It's like, well, there's a certain class of the market who absolutely needs this. If I'm at Telco, and I sat here a couple of years ago, and was like, okay, is it 20 or 50 companies in the world that it is going to be absolutely majorly transformative for them and that's hugely important. If I'm a mid-sized enterprise, I'm still not sure how much I'm caring about what's happening here, no offense, I'd love to hear some points there. But what it is and what it isn't with targets, absolutely, there are massive, massive clouds. Go to China, absolutely. You hear a lot about OpenStack here. Coming across the US, I don't hear a lot about it. We've known that for years. But I've talked to cloud provider in Australia, we've talked to Europeans that the @mail who's the provider for emails for certain providers around the world. It's kind of like okay, what part of the market and how do we make sure we target that because otherwise, it's this megaphone of yeah, OpenStack, well I'm not sure that was for me. >> So, yeah, what's your thought? >> We're seeing a lot of huge variety of implementations, users that are deploying OpenStack. And yeah we always think about the great big ones right? I love CERN, we love the Walmarts. We love China Mobiles, because they're huge, great examples. But I have to say we're actually seeing a whole range of deployments. They don't get the visibility 'cause they're small. Everybody goes, oh you're running on three machines or 10 machines, okay, right? Talk to me when you're the size of CERN. But that's not the case, we're seeing this whole range of deployments. They probably don't get much visibility, but they're just as important. So there's tons of use cases out there. There's tons of use cases published out there and we're seeing it. >> One of the interesting use cases with a different scale has been that edge discussion. I need a very small-- >> In fact that's a very pointed example, because they've had a ton of discussion because of that variety of needs. You get the telcos with their large-scale needs, but you've also got, you know, everybody else. >> It's OpenStack sitting at the bottom of a telephone pole. On a little blade with something embedded. >> In a retail store. >> It's in a retail store. >> Or in a coffee shop. >> Yeah. >> So this is really where we recognizing over and over again we go through these transitions that it used to be, even the fixed devices out of the edge. To change that, you have to replace that device. Instead, we want automation and we want software to do it. That's why OpenStack, moving to the edge, where it's a smaller device, much more capability, but it still computes storage and networking. And you want to have virtualized applications there so you can upgrade that, you can add new services without sending a truck out to replace that. >> Moving forward, do we expect to see more interaction between the Foundation itself and other foundations and open-source projects? And what might that look like? >> It depends on the community. It really does, we definitely have communications from at the board level from board-to-board between adjacent communities. It happens at the grassroots level, from, what we call SIGs or work groups with SIGs and work groups from those adjacent communities. >> I happen to sit on three boards, which is the OpenStack board the CNCF board, Cloud Foundry. And so what we're also seeing, though, now. For example, running Kubernetes, we just have now the cloud provider, which, OpenStack, being a cloud provider for Kubernetes similar in the open way that Amazon had the cloud provider for Kubernetes or Google is the cloud provider. So that now we're seeing the communities working together 'cause that's what our customers want. >> And now it's all driven by SIGs. >> The special interest groups, both sides getting together and saying, how do we make this happen? >> How do we make this happen? >> All right. One of the things you look at, there's a lot going on at the show. There's the OpenDev activity, there's a container track, there's an edge track. Sometimes, you know, where it gets a little unfocused, it's like let's talk about all the adjacencies, wait what about the core? I'd love to get your final takeaways, key things you've seen at the show, takeaways you want people to have when they think about OpenStack the show and OpenStack the Foundation. >> From my point of view it actually is back to where we started the conversation, is these users that are now coming out and saying, "I've been running OpenStack for the last three years, "now we're up to 100,000 or 200,000 cores." That shows the real adoption and those are the new operators. You don't think of Walmart or Progressive as being a service provider but they're delivering their service through the internet and they need a cloud platform in which to do that. So that's one part that I find particularly exciting. >> I totally agree with Lew. The one piece I would add is I think we've proven that it's the right infrastructure for the technology of the future, right? That's why we're able to have these additional discussions around edge and additional container technologies and Zuul with containers testing and deployment. It fits right in, so it's not a distraction. It's an addition to our infrastructure. >> I think the idea around, and that's why we actually broke up into these different tracks and had different keynotes around containers and around edge because those are primary use cases now. Two years ago when I think we were talking here, and like NFV and all the telcos were, and now that has succeeded because almost all the NFV deployments now are based on OpenStack. Now we're seeing it go to containers and edge, which are more application specific deployments. >> I'd love for you to connect the dots for us from the NFV stuff we were talking about a couple of years ago to the breadth of edge. There is no edge, it depends on who you are as to what the edge is, kind of like cloud was a few years ago. >> I mean, we actually have a white paper. If you go to OpenStack.org or just Google OpenStack edge white paper, I think you'll see that there are a variety of cases that are from manufacturing, retail, telco, I saw even space, remote driving vehicles and everything else like that. It's where latency really matters. So that we know that cloud computing is the fastest way to deploy and maintain, upgrade new applications, virtualize applications on a cloud. It's unfortunately too far away from many the places that have much more real-time characteristics. So if you're under 40 milliseconds or whatever, or you want to get something done in a VR environment or whatever, under five milliseconds, you can't go back to the cloud. It also, if you have an application, for example, a security monitoring application, whatever. 99% of the time, the video frames are the same and they're not interesting, don't push all that information back into the central cloud. Process it locally, now when you see frames that are changing, or whatever, you only use the bandwidth and the storage in the central cloud. So we're seeing this relationship between what do you want computed at the edge and how much computing can you do as we get more powerful there and then what do you want back in the centralized data centers. >> Daniel: While you simplify the management. >> Exactly right. >> Orchestration, policy. >> But you still need the automation, you need it to be virtualized, you need it to be managed in that way, so you can upgrade it. >> Alan Clark, Lew Tucker, always a pleasure to catch up. >> Thank you, yeah, >> Thank you so much for joining us. >> It's good to be here. >> John Troyer and I will be back with lots more coverage from OpenStack Summit 2018 here in Vancouver. Thanks for watching theCUBE. (upbeat music)