(bright upbeat music) >> Welcome to theCUBE's continuing coverage of AMD's fourth generation EPYC launch, along with the way that Dell has integrated this technology into its PowerEdge server lines. We're in for an interesting conversation today. Today, I'm joined by Dilip Ramachandran, Senior Director of Marketing at AMD, and Juergen Zimmermann. Juergen is Principal SAP Solutions Performance Benchmarking Engineer at Dell. Welcome, gentlemen. >> Welcome. >> Thank you David, nice to be here. >> Nice to meet you too, welcome to theCUBE. You will officially be CUBE alumni after this. Dilip, let's start with you. What's this all about? Tell us about AMD's recent launch and the importance of it. >> Thanks, David. I'm excited to actually talk to you today, AMD, at our fourth generation EPYC launch last month in November. And as part of that fourth generation EPYC launch, we announced industry-leading performance based on 96 cores, based on Zen 4 architecture. And new interfaces, PCIe Gen 5, as well as DDR5. Incredible amount of memory bandwidth, memory capacity supported, and a whole lot of other features as well. So we announced this product, we launched it in November last month. And we've been closely working with Dell on a number of benchmarks that we'd love to talk to you more about today. >> So just for some context, when was the last release of this scale? So when was the third generation released? How long ago? >> The third generation EPYC was launched in Q1 of 2021. So it was almost 18 to 24 months ago. And since then we've made a tremendous jump, the fourth generation EPYC, in terms of number of cores. So third generation EPYC supported 64 cores, fourth generation EPYC supports 96 cores. And these are new cores, the Zen 4 cores, the fourth generation of Zen cores. So very high performance, new interfaces, and really world-class performance. >> Excellent. Well, we'll go into greater detail in a moment, but let's go to Juergen. Tell us about the testing that you've been involved with to kind of prove out the benefits of this new AMD architecture. >> Yeah, well, the testing is SAP Standard Performance benchmark, the SAP SD two tier. And this is more or less a industry standard benchmark that is used to size your service for the needs of SAP. Actually, SAP customers always ask the vendors about the SAP benchmark and the SAPS values of their service. >> And I should have asked you before, but give us a little bit of your background working with SAP. Have you been doing this for longer than a week? >> Yeah, yeah, definitely, I do this for about 20 years now. Started with Sun Microsystems, and interestingly in the year 2003, 2004, I started working with AMD service on SAP with Linux, and afterwards parted the SAP application to Solaris AMD, also with AMD. So I have a lot of tradition with SAP and AMD benchmarks, and doing this ever since then. >> So give us some more detail on the results of the recent testing, and if you can, tell us why we should care? >> (laughs) Okay, the recent results actually also surprised myself, they were so good. So I initially installed the benchmark kit, and couldn't believe that the server is just getting, or hitting idle by the numbers I saw. So I cranked up the numbers and reached results that are most likely double the last generation, so Zen 3 generation, and that even passed almost all 8-socket systems out there. So if you want to have the same SAP performance, you can just use 2-socket AMD server instead of any four or 8-socket servers out there. And this is a tremendous saving in energy. >> So you just mentioned savings in terms of power consumption, which is a huge consideration. What are the sort of end user results that this delivers in terms of real world performance? How is a human being at the end of a computer going to notice something like this? >> So actually the results are like that you get almost 150,000 users concurrently accessing the system, and get their results back from SAP within one second response time. >> 150,000 users, you said? >> 150,000 users in parallel. >> (laughs) Okay, that's amazing. And I think it's interesting to note that, and I'll probably say this a a couple of times. You just referenced third generation EPYC architecture, and there are a lot of folks out there who are two generations back. Not everyone is religiously updating every 18 months, and so for a fair number of SAP environments, this is an even more dramatic increase. Is that a fair thing to say? >> Yeah, I just looked up yesterday the numbers from generation one of EPYC, and this was at about 28,000 users. So we are five times the performance now, within four years. Yeah, great. >> So Dilip, let's dig a little more into the EPYC architecture, and I'm specifically also curious about... You mentioned PCIe Gen five, or 5.0 and all of the components that plug into that. You mentioned I think faster DDR. Talk about that. Talk about how all of the components work together to make when Dell comes out with a PowerEdge server, to make it so much more powerful. >> Absolutely. So just to spend a little bit more time on this particular benchmark, the SAP Sales and Distribution benchmark. It's a widely used benchmark in the industry to basically look at how do I get the most performance out of my system for a variety of SAP business suite applications. And we touched upon it earlier, right, we are able to beat a performance of 4-socket and 8-socket servers out there. And you know, it saves energy, it saves cost, better TCO for the data center. So we're really excited to be able to support more users in a single server and meeting all the other dual socket and 4-socket combinations out there. Now, how did we get there, right, is more the important question. So as part of our fourth generation EPYC, we obviously upgraded our CPU core to provide much better single third performance per core. And at the socket level, you know, when you're packing 96 cores, you need to be able to feed these cores, you know, from a memory standpoint. So what we did was we went to 12 channels of memory, and these are DDR5 memory channels. So obviously you get much better bandwidth, higher speed of the memory with DDR5, you know, starting at 4,800 megahertz. And you're also now able to have more channels to be able to send the data from the memory into the CPU subsystem, which is very critical to keep the CPUs busy and active, and get the performance out. So that's on the memory side. On the data side, you know, we do have PCIe Gen five, and any data oriented applications that take data either from the PCIe drives or the network cards that utilize Gen five that are available in the industry today, you can actually really get data into the system through the PCIe I/O, either again, through the disk, or through the net card as well. So those are other ways to actually also feed the CPU subsystem with data to be processed by the CPU complex. So we are, again, very excited to see all of this coming together, and as they say, proof's in the pudding. You know, Juergen talked about it. How over generation after generation we've increased the performance, and now with our fourth generation EPYC, we are absolutely leading world-class performance on the SAP Sales and Distribution benchmark. >> Dilip, I have another question for you, and this may be, it may be a bit of a PowerEdge and beyond question. What are you seeing, or what are you anticipating in terms of end user perception when they go to buy a new server? Obviously server is a very loose term, and they can be configured in a bunch of different ways. But is there a discussion about ROI and TCO that's particularly critical? Because people are going to ask, "Well, wait a minute. If it's more expensive than the last one that I bought, am I getting enough bang for my buck?" Is that going to be part of the conversation, especially around power and cooling and things like that? >> Yeah, absolutely. You know, every data center decision maker has to ask the question, "Why should I upgrade? Should I stay with legacy hardware, or should I go into the latest and greatest that AMD offers?" And the advantages that the new generation products bring is much better performance at much better energy consumption levels, as well as much better performance per dollar levels. So when you do the upgrade, you are actually getting, you know, savings in terms of performance per dollar, as well as saving in space because you can consolidate your work into fewer servers 'cause you have more cores. As we talked about, you have eight, you know. Typically you might do it on a four or 8-socket server which is really expensive. You can consolidate down to a 2-socket server which is much cheaper. As also for maintenance costs, it's much lower maintenance costs as well. All of this, performance, power, maintenance costs, all of that translate into better TCO, right. So lower all of these, high performance, lower power, and then lower maintenance costs, translate to much better TCO for the end user. And that's an important equation that all customers pay attention to. and you know, we love to work with them and demonstrate those TCO benefits to them. >> Juergen, talk to us more in general about what Dell does from a PowerEdge perspective to make sure that Dell is delivering the best infrastructure possible for SAP. In general, I mean, I assume that this is a big responsibility of yours, is making sure that the stuff runs properly and if not, fixing it. So tell us about that relationship between Dell and a SAP. >> Yeah, for Dell and SAP actually, we're more or less partners with SAP. We have people sitting in SAP's Linux lab, and working in cooperative with SAP, also with Linux partners like SUSE and Red Hat. And we are in constant exchange about what's new in Linux, what's new on our side. And we're all a big family here. >> So when the new architecture comes out and they send it to Juergen, the boys back at the plant as they say, or the factory to use Formula One terms, are are waiting with baited breath to hear what Juergen says about the results. So just kind of kind of recap again, you know, the specific benchmarks that you were running. Tell us about that again. >> Yeah, the specific benchmark is the SAP Sales and Distribution benchmark. And for SAP, this is the benchmark that needs to be tested, and it shows the performance of the whole system. So in contrast to benchmarks that only check if the CPU is running, very good, this test the whole system up from the network stack, from the storage stack, the memory, subsystem, and the OS running on the CPUs. >> Okay, which makes perfect sense, since Dell is delivering an integrated system and not just CPU technology. You know, on that subject, Dilip, do you have any insights into performance numbers that you're hearing about with Gen four EPYC for other database environments? >> Yeah, we have actually worked together with Dell on a variety of benchmarks, both on the latest fourth generation EPYC processors as well as the preceding one, the third generation EPYC processors. And published a bunch of world records on database, particularly I would say TPC-H, TPCx-V, as well as TPCx-HS and TPCx-IoT. So a number of TPC related benchmarks that really showcase performance for database and related applications. And we've collaborated very closely with Dell on these benchmarks and published a number of them already, and you know, a number of them are world records as well. So again, we're very excited to collaborate with Dell on the SAP Sales and Distribution benchmark, as well as other benchmarks that are related to database. >> Well, speaking of other benchmarks, here at theCUBE we're going to be talking to actually quite a few people, looking at this fourth generation EPYC launch from a whole bunch of different angles. You two gentlemen have shed light on some really good pieces of that puzzle. I want to thank you for being on theCUBE today. With that, I'd like to thank all of you for joining us here on theCUBE. Stay tuned for continuing CUBE coverage of AMD's fourth generation EPYC launch, and Dell PowerEdge strategy to leverage it.

(calming music) >> Hello and welcome back to fabulous Las Vegas, Nevada. We're here at AWS re:Invent day three of our scintillating coverage here on theCUBE. I'm Savannah Peterson, joined by John Furrier. John Day three energy's high. How you feeling? >> I dunno, it's day two, day three, day four. It feels like day four, but again, we're back. >> Who's counting? >> Three pandemic levels in terms of 50,000 plus people? Hallways are packed. I got pictures. People don't believe it. It's actually happening. Then people are back. So, you know, and then the economy is a big question too and it's still, people are here, they're still building on the cloud and cost is a big thing. This next segment's going to be really important. I'm looking forward to this next segment. >> Yeah, me too. Without further ado let's welcome our guests for this segment. We have Brad from AMD and we have Rahul from you are, well you do a variety of different things. We'll start with CloudFix for this segment, but we could we could talk about your multiple hats all day long. Welcome to the show, gentlemen. How you doing? Brad how does it feel? We love seeing your logo above our stage here. >> Oh look, we love this. And talking about re:Invent last year, the energy this year compared to last year is so much bigger. We love it. We're excited to be here. >> Yeah, that's awesome. Rahul, how are you feeling? >> Excellent, I mean, I think this is my eighth or ninth re:Invent at this point and it's been fabulous. I think the, the crowd, the engagement, it's awesome. >> You wouldn't know there's a looming recession if you look at the activity but yet still the reality is here we had an analyst on yesterday, we were talking about spend more in the cloud, save more. So that you can still use the cloud and there's a lot of right sizing, I call you got to turn the lights off before you go to bed. Kind of be more efficient with your infrastructure as a theme. This re:Invent is a lot more about that now. Before it's about the glory days. Oh yeah, keep building, now with a little bit of pressure. This is the conversation. >> Exactly and I think most companies are looking to figure out how to innovate their way out of this uncertainty that's kind of on everyone's head. And the only way to do it is to be able to be more efficient with whatever your existing spend is, take those savings and then apply them to innovating on new stuff. And that's the way to go about it at this point. >> I think it's such a hot topic, for everyone that we're talking about. I mean, total cost optimization figuring out ways to be more efficient. I know that that's a big part of your mission at CloudFix. So just in case the audience isn't versed, give us the pitch. >> Okay, so a little bit of background on this. So the other hat I wear is CTO of ESW Capital. We have over 150 enterprise software companies within the portfolio. And one of my jobs is also to manage and run about 40 to 45,000 AWS accounts of our own. >> Casual number, just a few, just a couple pocket change, no big deal. >> And like everyone else here in the audience, yeah we had a problem with our costs, just going out of control and as we were looking at a lot of the tools to help us kind of get more efficient one of the biggest issues was that while people give you a lot of recommendations recommendations are way too far from realized savings. And we were running through the challenge of how do you take recommendation and turn them into real savings and multiple different hurdles. The short story being, we had to create CloudFix to actually realize those savings. So we took AWS recommendations around cost, filtered them down to the ones that are completely non-disruptive in nature, implemented those as simple automations that everyone could just run and realize those savings right away. We then took those savings and then started applying them to innovating and doing new interesting things with that money. >> Is there a best practice in your mind that you see merging in this time? People start more focused on it. Is there a method or a purpose kind of best practice of how to approach cost optimization? >> I think one of the things that most people don't realize is that cost optimization is not a one and done thing. It is literally nonstop. Which means that, on one hand AWS is constantly creating new services. There are over a hundred thousand API at this point of time How to use them right, how to use them efficiently You also have a problem of choice. Developers are constantly discovering new services discovering new ways to utilize them. And they are behaving in ways that you had not anticipated before. So you have to stay on top of things all the time. And really the only way to kind of stay on top is to have automation that helps you stay on top of all of these things. So yeah, finding efficiencies, standardizing your practices about how you leverage these AWS services and then automating the governance and hygiene around how you utilize them is really the key >> Brad tell me what this means for AMD and what working with CloudFix and Rahul does for your customers. >> Well, the idea of efficiency and cost optimization is near and dear to our heart. We have the leading. >> It's near and dear to everyone's heart, right now. (group laughs) >> But we are the leaders in x86 price performance and density and power efficiency. So this is something that's actually part of our core culture. We've been doing this a long time and what's interesting is most companies don't understand how much more efficiency they can get out of their applications aside from just the choices they make in cloud. but that's the one thing, the message we're giving to everybody is choice matters very much when it comes to your cloud solutions and just deciding what type of instance types you choose can have a massive impact on your bottom line. And so we are excited to partner with CloudFix, they've got a great model for this and they make it very easier for our customers to help identify those areas. And then AMD can come in as well and then help provide additional insight into those applications what else they can squeeze out of it. So it's a great relationship. >> If I hear you correctly, then there's more choice for the customers, faster selection, so no bad choices means bad performance if they have a workload or an app that needs to run, is that where you you kind of get into the, is that where it is or more? >> Well, I mean from the AMD side right now, one of the things they do very quickly is they identify where the low hanging fruit is. So it's the thing about x86 compatibility, you can shift instance types instantly in most cases without any change to your environment at all. And CloudFix has an automated tool to do that. And that's one thing you can immediately have an impact on your cost without having to do any work at all. And customers love that. >> What's the alternative if this doesn't exist they have to go manually figure it out or it gets them in the face or they see the numbers don't work or what's the, if you don't have the tool to automate what's the customer's experience >> The alternative is that you actually have people look at every single instance of usage of resources and try and figure out how to do this. At cloud scale, that just doesn't make sense. You just can't. >> It's too many different options. >> Correct The reality is that your resources your human resources are literally your most expensive part of your budget. You want to leverage all the amazing people you have to do the amazing work. This is not amazing work. This is mundane. >> So you free up all the people time. >> Correct, you free up wasting their time and resources on doing something that's mundane, simple and should be automated, because that's the only way you scale. >> I think of you is like a little helper in the background helping me save money while I'm not thinking about it. It's like a good financial planner making you money since we're talking about the economy >> Pretty much, the other analogy that I give to all the technologists is this is like garbage collection. Like for most languages when you are coding, you have these new languages that do garbage collection for you. You don't do memory management and stuff where developers back in the day used to do that. Why do that when you can have technology do that in an automated manner for you in an optimal way. So just kind of freeing up your developer's time from doing this stuff that's mundane and it's a standard best practice. One of the things that we leverage AMD for, is they've helped us define the process of seamlessly migrating folks over to AMD based instances without any major disruptions or trying to minimize every aspect of disruption. So all the best practices are kind of borrowed from them, borrowed from AWS in most other cases. And we basically put them in the automation so that you don't ever have to worry about that stuff. >> Well you're getting so much data you have the opportunity to really streamline, I mean I love this, because you can look across industry, across verticals and behavior of what other folks are doing. Learn from that and apply that in the background to all your different customers. >> So how big is the company? How big is the team? >> So we have people in about 130 different countries. So we've completely been remote and global and actually the cloud has been one of the big enablers of that. >> That's awesome, 130 countries. >> And that's the best part of it. I was just telling Brad a short while ago that's allowed us to hire the best talent from across the world and they spend their time building new amazing products and new solutions instead of doing all this other mundane stuff. So we are big believers in automation not only for our world. And once our customers started asking us about or telling us about the same problem that they were having that's when we actually took what we had internally for our own purpose. We packaged it up as CloudFix and launched it last year at re:Invent. >> If the customers aren't thinking about automation then they're going to probably have struggle. They're going to probably struggle. I mean with more data coming in you see the data story here more data's coming in, more automation. And this year Brad price performance, I've heard the word price performance more this year at re:Invent than any other year I've heard it before, but this year, price performance not performance, price performance. So you're starting to hear that dialogue of squeeze, understand the use cases use the right specialized processor instance starting to see that evolve. >> Yeah and and there's so much to it. I mean, AMD right out of the box is any instance is 10% less expensive than the equivalent in the market right now on AWS. They do a great job of maximizing those products. We've got our Zen four core general processor family just released in November and it's going to be a beast. Yeah, we're very excited about it and AWS announced support for it so we're excited to see what they deliver there too. But price performance is so critical and again it's going back to the complexity of these environments. Giving some of these enterprises some help, to help them understand where they can get additional value. It goes well beyond the retail price. There's a lot more money to be shaved off the top just by spending time thinking about those applications. >> Yeah, absolutely. I love that you talked about collaboration we've been talking about community. I want to acknowledge the AWS super fans here, standing behind the stage. Rahul, I know that you are an AWS super fan. Can you tell us about that community and the program? >> Yeah, so I have been involved with AWS and building products with AWS since 2007. So it's kind of 15 years back when literally there were just a handful of API for launching EC2 instances and S3. >> Not the a hundred thousand that you mentioned earlier, my goodness, the scale. >> So I think I feel very privileged and honored that I have been part of that journey and have had to learn or have had the opportunity to learn both from successes and failures. And it's just my way of contributing back to that community. So we are part of the FinOps foundation as well, contributing through that. I run a podcast called AWS Insiders and a livestream called AWS Made Easy. So we are trying to make sure that people out there are able to understand how to leverage AWS in the best possible way. And yeah, we are there to help and hold their hand through it. >> Talk about the community, take a minute to explain to the audience watching the community around this cost optimization area. It's evolving, you mentioned FinOps. There's a whole large community developing, of practitioners and technologists coming together to look at this. What does this all mean? Talk about this community. >> So cost management within organizations is has evolved so drastically that organizations haven't really coped with it. Historically, you've had finance teams basically buy a lot of infrastructure, which is CapEx and the engineering teams had kind of an upper bound on what they would spend and where they would spend. Suddenly with cloud, that's kind of enabled so much innovation all of a sudden, everyone's realized it, five years was spent figuring out whether people should be on the cloud or not. That's no longer a question, right. Everyone needs to be in the cloud and I think that's a no-brainer. The problem there is that suddenly your operating model has moved from CapEx to OpEx. And organizations haven't really figured out how to deal with it. Finance now no longer has the controls to control and manage and forecast costs. Engineering has never had to deal with it in the past and suddenly now they have to figure out how to do all this finance stuff. And procurement finds itself in a very awkward way position because they are no longer doing these negotiations like they were doing in the past where it was okay right up front before you engage, you do these negotiations. Now it's kind of an ongoing thing and it's constantly changing. Like every day is different. >> And you got marketplace >> And you got marketplace. So it's a very complex situation and I think what we are trying to do with the FinOps foundation is try and take a lot of the best practices across organizations that have been doing this at least for the last 10, 15 years. Take all the learnings and failures and turn them into hopefully opinionated approaches that people can take organizations can take to navigate through this faster rather than kind of falter and then decide that oh, this is not for us. >> Yeah. It's a great model, it's a great model. >> I know it's time John, go ahead. >> All right so, we got a little bumper sticker exercise we used to say what's the bumper sticker for the show? We used to say that, now we're modernizing, we're saying if you had to do an Instagram reel right now, short hot take of what's going on at re:Invent this year with AMD or CloudFix or just in general what would be the sizzle reel, that would be on Instagram or TikTok, go. >> Look, I think when you're at re:Invent right now and number one the energy is fantastic. 23 is going to be a building year. We've got a lot of difficult times ahead financially but it's the time, the ones that come out of 23 stronger and more efficient, and cost optimize are going to survive the long run. So now's the time to build. >> Well done, Rahul let's go for it. >> Yeah, so like Brad said, cost and efficiencies at the top of everyone's mind. Stuff that's the low hanging fruit, easy, use automation. Apply your sources to do most of the innovation. Take the easiest part to realizing savings and operate as efficiently as you possibly can. I think that's got to be key. >> I think they nailed it. They both nailed it. Wow, well it was really good. >> I put you on our talent list of >> And alright, so we repeat them. Are you part of our host team? I love this, I absolutely love this Rahul we wish you the best at CloudFix and your 17 other jobs. And I am genuinely impressed. Do you sleep actually? Last question. >> I do, I do. I have an amazing team that really helps me with all of this. So yeah, thanks to them and thank you for having us here. >> It's been fantastic. >> It's our pleasure. And Brad, I'm delighted we get you both now and again on our next segment. Thank you for being here with us. >> Thank you very much. >> And thank you all for tuning in to our live coverage here at AWS re:Invent, in fabulous Sin City with John Furrier, my name's Savannah Peterson. You're watching theCUBE, the leader in high tech coverage. (calm music)

(upbeat music) >> The market for enterprise servers is large and it generates well north of $100 billion in annual revenue, and it's growing consistently in the mid to high single digit range. Right now, like many segments, the market for servers is, it's like slingshotting, right? Organizations, they've been replenishing their install bases and upgrading, especially at HQs coming out of the isolation economy. But the macro headwinds, as we've reported, are impacting all segments of the market. CIOs, you know, they're tapping the brakes a little bit, sometimes quite a bit and being cautious with both capital expenditures and discretionary opex, particularly in the cloud. They're dialing it down and just being a little bit more, you know, cautious. The market for enterprise servers, it's dominated as you know, by x86 based systems with an increasingly large contribution coming from alternatives like ARM and NVIDIA. Intel, of course, is the largest supplier, but AMD has been incredibly successful competing with Intel because of its focus, it's got an outsourced manufacturing model and its innovation and very solid execution. Intel's frequent delays with its next generation Sapphire Rapid CPUs, now slated for January 2023 have created an opportunity for AMD, specifically AMD's next generation EPYC CPUs codenamed Genoa will offer as many as 96 Zen 4 cores per CPU when it launches later on this month. Observers can expect really three classes of Genoa. There's a standard Zen 4 compute platform for general purpose workloads, there's a compute density optimized Zen 4 package and then a cache optimized version for data intensive workloads. Indeed, the makers of enterprise servers are responding to customer requirements for more diversity and server platforms to handle different workloads, especially those high performance data-oriented workloads that are being driven by AI and machine learning and high performance computing, HPC needs. OEMs like Dell, they're going to be tapping these innovations and try to get to the market early. Dell, in particular, will be using these systems as the basis for its next generation Gen 16 servers, which are going to bring new capabilities to the market. Now, of course, Dell is not alone, there's got other OEM, you've got HPE, Lenovo, you've got ODMs, you've got the cloud players, they're all going to be looking to keep pace with the market. Now, the other big trend that we've seen in the market is the way customers are thinking about or should be thinking about performance. No longer is the clock speed of the CPU the soul and most indicative performance metric. There's much more emphasis in innovation around all those supporting components in a system, specifically the parts of the system that take advantage, for example, of faster bus speeds. We're talking about things like network interface cards and RAID controllers and memories and other peripheral devices that in combination with microprocessors, determine how well systems can perform and those kind of things around compute operations, IO and other critical tasks. Now, the combinatorial factors ultimately determine the overall performance of the system and how well suited a particular server is to handling different workloads. So we're seeing OEMs like Dell, they're building flexibility into their offerings and putting out products in their portfolios that can meet the changing needs of their customers. Welcome to our ongoing series where we investigate the critical question, does hardware matter? My name is Dave Vellante, and with me today to discuss these trends and the things that you should know about for the next generation of server architectures is former CTO from Oracle and EMC and adjunct faculty and Wharton CTO Academy, David Nicholson. Dave, always great to have you on "theCUBE." Thanks for making some time with me. >> Yeah, of course, Dave, great to be here. >> All right, so you heard my little spiel in the intro, that summary, >> Yeah. >> Was it accurate? What would you add? What do people need to know? >> Yeah, no, no, no, 100% accurate, but you know, I'm a resident nerd, so just, you know, some kind of clarification. If we think of things like microprocessor release cycles, it's always going to be characterized as rolling thunder. I think 2023 in particular is going to be this constant release cycle that we're going to see. You mentioned the, (clears throat) excuse me, general processors with 96 cores, shortly after the 96 core release, we'll see that 128 core release that you referenced in terms of compute density. And then, we can talk about what it means in terms of, you know, nanometers and performance per core and everything else. But yeah, no, that's the main thing I would say, is just people shouldn't look at this like a new car's being released on Saturday. This is going to happen over the next 18 months, really. >> All right, so to that point, you think about Dell's next generation systems, they're going to be featuring these new AMD processes, but to your point, when you think about performance claims, in this industry, it's a moving target. It's that, you call it a rolling thunder. So what does that game of hopscotch, if you will, look like? How do you see it unfolding over the next 12 to 18 months? >> So out of the gate, you know, slated as of right now for a November 10th release, AMD's going to be first to market with, you know, everyone will argue, but first to market with five nanometer technology in production systems, 96 cores. What's important though is, those microprocessors are going to be resident on motherboards from Dell that feature things like PCIe 5.0 technology. So everything surrounding the microprocessor complex is faster. Again, going back to this idea of rolling thunder, we expect the Gen 16 PowerEdge servers from Dell to similarly be rolled out in stages with initial releases that will address certain specific kinds of workloads and follow on releases with a variety of systems configured in a variety of ways. >> So I appreciate you painting a picture. Let's kind of stay inside under the hood, if we can, >> Sure. >> And share with us what we should know about these kind of next generation CPUs. How are companies like Dell going to be configuring them? How important are clock speeds and core counts in these new systems? And what about, you mentioned motherboards, what about next gen motherboards? You mentioned PCIe Gen 5, where does that fit in? So take us inside deeper into the system, please. >> Yeah, so if you will, you know, if you will join me for a moment, let's crack open the box and look inside. It's not just microprocessors. Like I said, they're plugged into a bus architecture that interconnect. How quickly that interconnect performs is critical. Now, I'm going to give you a statistic that doesn't require a PhD to understand. When we go from PCIe Gen 4 to Gen 5, which is going to be featured in all of these systems, we double the performance. So just, you can write that down, two, 2X. The performance is doubled, but the numbers are pretty staggering in terms of giga transactions per second, 128 gigabytes per second of aggregate bandwidth on the motherboard. Again, doubling when going from 4th Gen to 5th Gen. But the reality is, most users of these systems are still on PCIe Gen 3 based systems. So for them, just from a bus architecture perspective, you're doing a 4X or 8X leap in performance, and then all of the peripherals that plug into that faster bus are faster, whether it's RAID control cards from RAID controllers or storage controllers or network interface cards. Companies like Broadcom come to mind. All of their components are leapfrogging their prior generation to fit into this ecosystem. >> So I wonder if we could stay with PCIe for a moment and, you know, just understand what Gen 5 brings. You said, you know, 2X, I think we're talking bandwidth here. Is there a latency impact? You know, why does this matter? And just, you know, this premise that these other components increasingly matter more, Which components of the system are we talking about that can actually take advantage of PCIe Gen 5? >> Pretty much all of them, Dave. So whether it's memory plugged in or network interface cards, so communication to the outside world, which computer servers tend to want to do in 2022, controllers that are attached to internal and external storage devices. All of them benefit from this enhancement and performance. And it's, you know, PCI express performance is measured in essentially bandwidth and throughput in the sense of the numbers of transactions per second that you can do. It's mind numbing, I want to say it's 32 giga transfers per second. And then in terms of bandwidth, again, across the lanes that are available, 128 gigabytes per second. I'm going to have to check if it's gigabits or gigabytes. It's a massive number. And again, it's double what PCIe 4 is before. So what does that mean? Just like the advances in microprocessor technology, you can consolidate massive amounts of work into a much smaller footprint. That's critical because everything in that server is consuming power. So when you look at next generation hardware that's driven by things like AMD Genoa or you know, the EPYC processors, the Zen with the Z4 microprocessors, for every dollar that you're spending on power and equipment and everything else, you're getting far greater return on your investment. Now, I need to say that we anticipate that these individual servers, if you're out shopping for a server, and that's a very nebulous term because they come in all sorts of shapes and sizes, I think there's going to be a little bit of sticker shock at first until you run the numbers. People will look at an individual server and they'll say, wow, this is expensive and the peripherals, the things that are going into those slots are more expensive, but you're getting more bang for your buck. You're getting much more consolidation, lower power usage and for every dollar, you're getting a greater amount of performance and transactions, which translates up the stack through the application layer and, you know, out to the end user's desire to get work done. >> So I want to come back to that, but let me stay on performance for a minute. You know, we all used to be, when you'd go buy a new PC, you'd be like, what's the clock speed of that? And so, when you think about performance of a system today and how measurements are changing, how should customers think about performance in these next gen systems? And where does that, again, where does that supporting ecosystem play? >> So if you are really into the speeds and feeds and what's under the covers, from an academic perspective, you can go in and you can look at the die size that was used to create the microprocessors, the clock speeds, how many cores there are, but really, the answer is look at the benchmarks that are created through testing, especially from third party organizations that test these things for workloads that you intend to use these servers for. So if you are looking to support something like a high performance environment for artificial intelligence or machine learning, look at the benchmarks as they're recorded, as they're delivered by the entire system. So it's not just about the core. So yeah, it's interesting to look at clock speeds to kind of compare where we are with regards to Moore's Law. Have we been able to continue to track along that path? We know there are physical limitations to Moore's Law from an individual microprocessor perspective, but none of that really matters. What really matters is what can this system that I'm buying deliver in terms of application performance and user requirement performance? So that's what I'd say you want to look for. >> So I presume we're going to see these benchmarks at some point, I'm hoping we can, I'm hoping we can have you back on to talk about them. Is that something that we can expect in the future? >> Yeah, 100%, 100%. Dell, and I'm sure other companies, are furiously working away to demonstrate the advantages of this next gen architecture. If I had to guess, I would say that we are going to see quite a few world records set because of the combination of things, like faster network interface cards, faster storage cards, faster memory, more memory, faster cache, more cache, along with the enhanced microprocessors that are going to be delivered. And you mentioned this is, you know, AMD is sort of starting off this season of rolling thunder and in a few months, we'll start getting the initial entries from Intel also, and we'll be able to compare where they fit in with what AMD is offering. I'd expect OEMs like Dell to have, you know, a portfolio of products that highlight the advantages of each processor's set. >> Yeah, I talked in my open Dave about the diversity of workloads. What are some of those emerging workloads and how will companies like Dell address them in your view? >> So a lot of the applications that are going to be supported are what we think of as legacy application environments. A lot of Oracle databases, workloads associated with ERP, all of those things are just going to get better bang for their buck from a compute perspective. But what we're going to be hearing a lot about and what the future really holds for us that's exciting is this arena of artificial intelligence and machine learning. These next gen platforms offer performance that allows us to do things in areas like natural language processing that we just couldn't do before cost effectively. So I think the next few years are going to see a lot of advances in AI and ML that will be debated in the larger culture and that will excite a lot of computer scientists. So that's it, AI/ML are going to be the big buzzwords moving forward. >> So Dave, you talked earlier about this, some people might have sticker shocks. So some of the infrastructure pros that are watching this might be, oh, okay, I'm going to have to pitch this, especially in this, you know, tough macro environment. I'm going to have to sell this to my CIO, my CFO. So what does this all mean? You know, if they're going to have to pay more, how is it going to affect TCO? How would you pitch that to your management? >> As long as you stay away from per unit cost, you're fine. And again, we don't have necessarily, or I don't have necessarily insider access to street pricing on next gen servers yet, but what I do know from examining what the component suppliers tell us is that, these systems are going to be significantly more expensive on a per unit basis. But what does that mean? If the server that you're used to buying for five bucks is now 10 bucks, but it's doing five times as much work, it's a great deal, and anyone who looks at it and says, 10 bucks? It used to only be five bucks, well, the ROI and the TCO, that's where all of this really needs to be measured and a huge part of that is going to be power consumption. And along with the performance tests that we expect to see coming out imminently, we should also be expecting to see some of those ROI metrics, especially around power consumption. So I don't think it's going to be a problem moving forward, but there will be some sticker shock. I imagine you're going to be able to go in and configure a very, very expensive, fully loaded system on some of these configurators online over the next year. >> So it's consolidation, which means you could do more with less. It's going to be, or more with the same, it's going to be lower power, less cooling, less floor space and lower management overhead, which is kind of now you get into staff, so you're going to have to sort of identify how the staff can be productive in other areas. You're probably not going to fire people hopefully. But yeah, it sounds like it's going to be a really consolidation play. I talked at the open about Intel and AMD and Intel coming out with Sapphire Rapids, you know, of course it's been well documented, it's late but they're now scheduled for January. Pat Gelsinger's talked about this, and of course they're going to try to leapfrog AMD and then AMD is going to respond, you talked about this earlier, so that game is going to continue. How long do you think this cycle will last? >> Forever. (laughs) It's just that, there will be periods of excitement like we're going to experience over at least the next year and then there will be a lull and then there will be a period of excitement. But along the way, we've got lurkers who are trying to disrupt this market completely. You know, specifically you think about ARM where the original design point was, okay, you're powered by a battery, you have to fit in someone's pocket. You can't catch on fire and burn their leg. That's sort of the requirement, as opposed to the, you know, the x86 model, which is okay, you have a data center with a raised floor and you have a nuclear power plant down the street. So don't worry about it. As long as an 18-wheeler can get it to where it needs to be, we'll be okay. And so, you would think that over time, ARM is going to creep up as all destructive technologies do, and we've seen that, we've definitely seen that. But I would argue that we haven't seen it happen as quickly as maybe some of us expected. And then you've got NVIDIA kind of off to the side starting out, you know, heavy in the GPU space saying, hey, you know what, you can use the stuff we build for a whole lot of really cool new stuff. So they're running in a different direction, sort of gnawing at the traditional x86 vendors certainly. >> Yes, so I'm glad- >> That's going to be forever. >> I'm glad you brought up ARM and NVIDIA, I think, but you know, maybe it hasn't happened as quickly as many thought, although there's clearly pockets and examples where it is taking shape. But this to me, Dave, talks to the supporting cast. It's not just about the microprocessor unit anymore, specifically, you know, generally, but specifically the x86. It's the supporting, it's the CPU, the NPU, the XPU, if you will, but also all those surrounding components that, to your earlier point, are taking advantage of the faster bus speeds. >> Yeah, no, 100%. You know, look at it this way. A server used to be measured, well, they still are, you know, how many U of rack space does it take up? You had pizza box servers with a physical enclosure. Increasingly, you have the concept of a server in quotes being the aggregation of components that are all plugged together that share maybe a bus architecture. But those things are all connected internally and externally, especially externally, whether it's external storage, certainly networks. You talk about HPC, it's just not one server. It's hundreds or thousands of servers. So you could argue that we are in the era of connectivity and the real critical changes that we're going to see with these next generation server platforms are really centered on the bus architecture, PCIe 5, and the things that get plugged into those slots. So if you're looking at 25 gig or 100 gig NICs and what that means from a performance and/or consolidation perspective, or things like RDMA over Converged Ethernet, what that means for connecting systems, those factors will be at least as important as the microprocessor complexes. I imagine IT professionals going out and making the decision, okay, we're going to buy these systems with these microprocessors, with this number of cores in memory. Okay, great. But the real work starts when you start talking about connecting all of them together. What does that look like? So yeah, the definition of what constitutes a server and what's critically important I think has definitely changed. >> Dave, let's wrap. What can our audience expect in the future? You talked earlier about you're going to be able to get benchmarks, so that we can quantify these innovations that we've been talking about, bring us home. >> Yeah, I'm looking forward to taking a solid look at some of the performance benchmarking that's going to come out, these legitimate attempts to set world records and those questions about ROI and TCO. I want solid information about what my dollar is getting me. I think it helps the server vendors to be able to express that in a concrete way because our understanding is these things on a per unit basis are going to be more expensive and you're going to have to justify them. So that's really what, it's the details that are going to come the day of the launch and in subsequent weeks. So I think we're going to be busy for the next year focusing on a lot of hardware that, yes, does matter. So, you know, hang on, it's going to be a fun ride. >> All right, Dave, we're going to leave it there. Thanks you so much, my friend. Appreciate you coming on. >> Thanks, Dave. >> Okay, and don't forget to check out the special website that we've set up for this ongoing series. Go to doeshardwarematter.com and you'll see commentary from industry leaders, we got analysts on there, technical experts from all over the world. Thanks for watching, and we'll see you next time. (upbeat music)

(upbeat jingle) >> The history of Exadata in the platform is really unique. And from my vantage point, it started earlier this century as a skunkworks inside of Oracle called Project Sage back when grid computing was the next big thing. Oracle saw that betting on standard hardware would put it on an industry curve that would rapidly evolve. Last April, for example, Oracle announced the availability of Exadata X9M in OCI, Oracle Cloud Infrastructure. One thing that hasn't been as well publicized is that Exadata on OCI is using AMD's EPYC processors in the database service. EPYC is not Eastern Pacific Yacht Club for all you sailing buffs, rather it stands for Extreme Performance Yield Computing, the enterprise grade version of AMD's Zen architecture which has been a linchpin of AMD's success in terms of penetrating enterprise markets. And to focus on the innovations that AMD and Oracle are bringing to market, we have with us today, Juan Loaiza, who's executive vice president of mission critical technologies at Oracle, and Mark Papermaster, who's the CTO and EVP of technology and engineering at AMD. Juan, welcome back to the show. Mark, great to have you on The Cube in your first appearance, thanks for coming on. Juan, let's start with you. You've been on The Cube a number of times, as I said, and you've talked about how Exadata is a top platform for Oracle database. We've covered that extensively. What's different and unique from your point of view about Exadata Cloud Infrastructure X9M on OCI? >> So as you know, Exadata, it's designed top down to be the best possible platform for database. It has a lot of unique capabilities, like we make extensive use of RDMA, smart storage. We take advantage of everything we can in the leading hardware platforms. X9M is our next generation platform and it does exactly that. We're always wanting to be, to get all the best that we can from the available hardware that our partners like AMD produce. And so that's what X9M in it is, it's faster, more capacity, lower latency, more iOS, pushing the limits of the hardware technology. So we don't want to be the limit, the software database software should not be the limit, it should be the actual physical limits of the hardware. That that's what X9M's all about. >> Why, Juan, AMD chips in X9M? >> We're introducing AMD chips. We think they provide outstanding performance, both for OTP and for analytic workloads. And it's really that simple, we just think the performance is outstanding in the product. >> Mark, your career is quite amazing. I could riff on history for hours but let's focus on the Oracle relationship. Mark, what are the relevant capabilities and key specs of the AMD chips that are used in Exadata X9M on Oracle's cloud? >> Well, thanks. It's really the basis of the great partnership that we have with Oracle on Exadata X9M and that is that the AMD technology uses our third generation of Zen processors. Zen was architected to really bring high performance back to X86, a very strong roadmap that we've executed on schedule to our commitments. And this third generation does all of that, it uses a seven nanometer CPU that is a core that was designed to really bring throughput, bring really high efficiency to computing and just deliver raw capabilities. And so for Exadata X9M, it's really leveraging all of that. It's really a balanced processor and it's implemented in a way to really optimize high performance. That is our whole focus of AMD. It's where we've reset the company focus on years ago. And again, great to see the super smart database team at Oracle really partner with us, understand those capabilities and it's been just great to partner with them to enable Oracle to really leverage the capabilities of the Zen processor. >> Yeah. It's been a pretty amazing 10 or 11 years for both companies. But Mark, how specifically are you working with Oracle at the engineering and product level and what does that mean for your joint customers in terms of what they can expect from the collaboration? >> Well, here's where the collaboration really comes to play. You think about a processor and I'll say, when Juan's team first looked at it, there's general benchmarks and the benchmarks are impressive but they're general benchmarks. And they showed the base processing capability but the partnership comes to bear when it means optimizing for the workloads that Exadata X9M is really delivering to the end customers. And that's where we dive down and as we learn from the Oracle team, we learn to understand where bottlenecks could be, where is there tuning that we could in fact really boost the performance above that baseline that you get in the generic benchmarks. And that's what the teams have done, so for instance, you look at optimizing latency to our DMA, you look at optimizing throughput on oil TP and database processing. When you go through the workloads and you take the traces and you break it down and you find the areas that are bottlenecking and then you can adjust, we have thousands of parameters that can be adjusted for a given workload. And that's the beauty of the partnership. So we have the expertise on the CPU engineering, Oracle Exadata team knows innately what the customers need to get the most out of their platform. And when the teams came together, we actually achieved anywhere from 20% to 50% gains on specific workloads, it is really exciting to see. >> Mark, last question for you is how do you see this relationship evolving in the future? Can you share a little roadmap for the audience? >> You bet. First off, given the deep partnership that we've had on Exadata X9M, it's really allowed us to inform our future design. So in our current third generation, EPYC is that is really what we call our epic server offerings. And it's a 7,003 third gen and Exadara X9M. So what about fourth gen? Well, fourth gen is well underway, ready for the future, but it incorporates learning that we've done in partnership with Oracle. It's going to have even more through capabilities, it's going to have expanded memory capabilities because there's a CXL connect express link that'll expand even more memory opportunities. And I could go on. So that's the beauty of a deep partnership as it enables us to really take that learning going forward. It pays forward and we're very excited to fold all of that into our future generations and provide even a better capabilities to Juan and his team moving forward. >> Yeah, you guys have been obviously very forthcoming. You have to be with Zen and EPYC. Juan, anything you'd like to add as closing comments? >> Yeah. I would say that in the processor market there's been a real acceleration in innovation in the last few years, there was a big move 10, 15 years ago when multicore processors came out. And then we were on that for a while and then things started stagnating, but in the last two or three years, AMD has been leading this, there's been a dramatic acceleration in innovation so it's very exciting to be part of this and customers are getting a big benefit from this. >> All right. Hey, thanks for coming back on The Cube today. Really appreciate your time. >> Thanks. Glad to be here. >> All right and thank you for watching this exclusive Cube conversation. This is Dave Vellante from The Cube and we'll see you next time. (upbeat jingle)

(upbeat music) >> Hello everyone. This is Dave Vellante. We're diving into the deep end with AMD and Oracle on the topic of mySQL HeatWave performance. And we want to explore the important issues around machine learning. As applications become more data intensive and machine intelligence continues to evolve, workloads increasingly are seeing a major shift where data and AI are being infused into applications. And having a database that simplifies the convergence of transaction and analytics data without the need to context, switch and move data out of and into different data stores. And eliminating the need to perform extensive ETL operations is becoming an industry trend that customers are demanding. At the same time, workloads are becoming more automated and intelligent. And to explore these issues further, we're happy to have back in theCUBE Nipun Agarwal, who's the Senior Vice President of mySQL HeatWave and Kumaran Siva, who's the Corporate Vice President Strategic Business Development at AMD. Gents, hello again. Welcome back. >> Hello. Hi Dave. >> Thank you, Dave. >> Okay. Nipun, obviously machine learning has become a must have for analytics offerings. It's integrated into mySQL HeatWave. Why did you take this approach and not the specialized database approach as many competitors do right tool for the right job? >> Right? So, there are a lot of customers of mySQL who have the need to run machine learning on the data which is store in mySQL database. So in the past, customers would need to extract the data out of mySQL and they would take it to a specialized service for running machine learning. Now, the reason we decided to incorporate machine learning inside the database, there are multiple reasons. One, customers don't need to move the data. And if they don't need to move the data, it is more secure because it's protected by the same access controlled mechanisms as rest of the data There is no need for customers to manage multiple services. But in addition to that, when we run the machine learning inside the database customers are able to leverage the same service the same hardware, which has been provisioned for OTP analytics and use machine learning capabilities at no additional charge. So from a customer's perspective, they get the benefits that it is a single database. They don't need to manage multiple services. And it is offered at no additional charge. And then as another aspect, which is kind of hard to learn which is based on the IP, the work we have done it is also significantly faster than what customers would get by having a separate service. >> Just to follow up on that. How are you seeing customers use HeatWaves machine learning capabilities today? How is that evolving? >> Right. So one of the things which, you know customers very often want to do is to train their models based on the data. Now, one of the things is that data in a database or in a transaction database changes quite rapidly. So we have introduced support for auto machine learning as a part of HeatWave ML. And what it does is that it fully automates the process of training. And this is something which is very important to database users, very important to mySQL users that they don't really want to hire or data scientists or specialists for doing training. So that's the first part that training in HeatWave ML is fully automated. Doesn't require the user to provide any like specific parameters, just the source data and the task which they want to train. The second aspect is the training is really fast. So the training is really fast. The benefit is that customers can retrain quite often. They can make sure that the model is up to date with any changes which have been made to their transaction database. And as a result of the models being up to date, the accuracy of the prediction is high. Right? So that's the first aspect, which is training. The second aspect is inference, which customers run once they have the models trained. And the third thing, which is perhaps been the most sought after request from the mySQL customers is the ability to provide explanations. So, HeatWave ML provides explanations for any model which has been generated or trained by HeatWave ML. So these are the three capabilities- training, inference and explanations. And this whole process is completely automated, doesn't require a specialist or a data scientist. >> Yeah, that's nice. I mean, training obviously very popular today. I've said inference I think is going to explode in the coming decade. And then of course, AI explainable AI is a very important issue. Kumaran, what are the relevant capabilities of the AMD chips that are used in OCI to support HeatWave ML? Are they different from say the specs for HeatWave in general? >> So, actually they aren't. And this is one of the key features of this architecture or this implementation that is really exciting. Um, there with HeatWave ML, you're using the same CPU. And by the way, it's not a GPU, it's a CPU for both for all three of the functions that Nipun just talked about- inference, training and explanation all done on CPU. You know, bigger picture with the capabilities we bring here we're really providing a balance, you know between the CPU cores, memory and the networking. And what that allows you to do here is be able to feed the CPU cores appropriately. And within the cores, we have these AVX instruc... extensions in with the Zen 2 and Zen 3 cores. We had AVX 2, and then with the Zen 4 core coming out we're going to have AVX 512. But we were able to with that balance of being able to bring in the data and utilize the high memory bandwidth and then use the computation to its maximum we're able to provide, you know, build pride enough AI processing that we are able to get the job done. And then we're built to build a fit into that larger pipeline that that we build out here with the HeatWave. >> Got it. Nipun you know, you and I every time we have a conversation we've got to talk benchmarks. So you've done machine learning benchmarks with HeatWave. You might even be the first in the industry to publish you know, transparent, open ML benchmarks on GitHub. I mean, I, I wouldn't know for sure but I've not seen that as common. Can you describe the benchmarks and the data sets that you used here? >> Sure. So what we did was we took a bunch of open data sets for two categories of tasks- classification and regression. So we took about a dozen data sets for classification and about six for regression. So to give an example, the kind of data sets we used for classifications like the airlines data set, hex sensors bank, right? So these are open data sets. And what we did was for on these data sets we did a comparison of what would it take to train using HeatWave ML? And then the other service we compared with is that RedShift ML. So, there were two observations. One is that with HeatWave ML, the user does not need to provide any tuning parameters, right? The HeatWave ML using RML fully generates a train model, figures out what are the right algorithms? What are the right features? What are the right hyper parameters and sets, right? So no need for any manual intervention not so the case with Redshift ML. The second thing is the performance, right? So the performance of HeatWave ML aggregate on these 12 data sets for classification and the six data sets on regression. On an average, it is 25 times faster than Redshift ML. And note that Redshift ML in turn involves SageMaker, right? So on an average, HeatWave ML provides 25 times better performance for training. And the other point to note is that there is no need for any human intervention. That's fully automated. But in the case of Redshift ML, many of these data sets did not even complete in the set duration. If you look at price performance, one of the things again I want to highlight is because of the fact that AMD does pretty well in all kinds of workloads. We are able to use the same cluster users and use the same cluster for analytics, for OTP or for machine learning. So there is no additional cost for customers to run HeatWave ML if they have provision HeatWave. But assuming a user is provisioning a HeatWave cluster only to run HeatWave ML, right? That's the case, even in that case the price performance advantage of HeatWave ML over Redshift ML is 97 times, right? So 25 times faster at 1% of the cost compared to Redshift ML And all these scripts and all this information is available on GitHub for customers to try to modify and like, see, like what are the advantages they would get on their workloads? >> Every time I hear these numbers, I shake my head. I mean, they're just so overwhelming. Um, and so we'll see how the competition responds when, and if they respond. So, but thank you for sharing those results. Kumaran, can you elaborate on how the specs that you talked about earlier contribute to HeatWave ML's you know, benchmark results. I'm particularly interested in scalability, you know Typically things degrade as you push the system harder. What are you seeing? >> No, I think, I think it's good. Look, yeah. That's by those numbers, just blow me, blow my head too. That's crazy good performance. So look from, from an AMD perspective, we have really built an architecture. Like if you think about the chiplet architecture to begin with, it is fundamentally, you know, it's kind of scaling by design, right? And, and one of the things that we've done here is been able to work with, with the HeatWave team and heat well ML team, and then been able to, to within within the CPU package itself, be able to scale up to take very efficient use of all of the course. And then of course, work with them on how you go between nodes. So you can have these very large systems that can run ML very, very efficiently. So it's really, you know, building on the building blocks of the chiplet architecture and how scaling happens there. >> Yeah. So it's you're saying it's near linear scaling or essentially. >> So, let Nipun comment on that. >> Yeah. >> Is it... So, how about as cluster sizes grow, Nipun? >> Right. >> What happens there? >> So one of the design points for HeatWave is scale out architecture, right? So as you said, that as we add more data set or increase the size of the data, or we add the number of nodes to the cluster, we want the performance to scale. So we show that we have near linear scale factor, or nearly near scale scalability for SQL workloads in the case of HeatWave ML, as well. As users add more nodes to the cluster so the size of the cluster the performance of HeatWave ML improves. So I was giving you this example that HeatWave ML is 25 times faster compared to Redshift ML. Well, that was on a cluster size of two. If you increase the cluster size of HeatWave ML to a larger number. But I think the number is 16. The performance advantage over Redshift ML increases from 25 times faster to 45 times faster. So what that means is that on a cluster size of 16 nodes HeatWave ML is 45 times faster for training these again, dozen data sets. So this shows that HeatWave ML skills better than the computation. >> So you're saying adding nodes offsets any management complexity that you would think of as getting in the way. Is that right? >> Right. So one is the management complexity and which is why by features like last customers can scale up or scale down, you know, very easily. The second aspect is, okay What gives us this advantage, right, of scalability? Or how are we able to scale? Now, the techniques which we use for HeatWave ML scalability are a bit different from what we use for SQL processing. So in the case of HeatWave ML, they really like, you know, three, two trade offs which we have to be careful about. One is the accuracy. Because we want to provide better performance for machine learning without compromising on the accuracy. So accuracy would require like more synchronization if you have multiple threads. But if you have too much of synchronization that can slow down the degree of patterns that we get. Right? So we have to strike a fine balance. So what we do is that in HeatWave ML, there are different phases of training, like algorithm selection, feature selection, hyper probability training. Each of these phases is analyzed. And for instance, one of the ways techniques we use is that if you're trying to figure out what's the optimal hyper parameter to be used? We start up with the search space. And then each of the VMs gets a part of the search space. And then we synchronize only when needed, right? So these are some of the techniques which we have developed over the years. And there are actually paper's filed, research publications filed on this. And this is what we do to achieve good scalability. And what that results to the customer is that if they have some amount of training time and they want to make it better they can just provision a larger cluster and they will get better performance. >> Got it. Thank you. Kumaran, when I think of machine learning, machine intelligence, AI, I think GPU but you're not using GPU. So how are you able to get this type of performance or price performance without using GPU's? >> Yeah, definitely. So yeah, that's a good point. And you think about what is going on here and you consider the whole pipeline that Nipun has just described in terms of how you get you know, your training, your algorithms And using the mySQL pieces of it to get to the point where the AI can be effective. In that process what happens is you have to have a lot of memory to transactions. A lot of memory bandwidth comes into play. And then bringing all that data together, feeding the actual complex that does the AI calculations that in itself could be the bottleneck, right? And you can have multiple bottlenecks along the way. And I think what you see in the AMD architecture for epic for this use case is the balance. And the fact that you are able to do the pre-processing, the AI, and then the post-processing all kind of seamlessly together, that has a huge value. And that goes back to what Nipun was saying about using the same infrastructure, gets you the better TCO but it also gets you gets you better performance. And that's because of the fact that you're bringing the data to the computation. So the computation in this case is not strictly the bottleneck. It's really about how you pull together what you need and to do the AI computation. And that is, that's probably a more, you know, it's a common case. And so, you know, you're going to start I think the least start to see this especially for inference applications. But in this case we're doing both inference explanation and training. All using the the CPU in the same OCI infrastructure. >> Interesting. Now Nipun, is the secret sauce for HeatWave ML performance different than what we've discussed before you and I with with HeatWave generally? Is there some, you know, additive engine additive that you're putting in? >> Right? Yes. The secret sauce is indeed different, right? Just the way I was saying that for SQL processing. The reason we get very good performance and price performance is because we have come up with new algorithms which help the SQL process can scale out. Similarly for HeatWave ML, we have come up with new IP, new like algorithms. One example is that we use meta-learn proxy models, right? That's the technique we use for automating the training process, right? So think of this meta-learn proxy models to be like, you know using machine learning for machine learning training. And this is an IP which we developed. And again, we have published the results and the techniques. But having such kind of like techniques is what gives us a better performance. Similarly, another thing which we use is adaptive sampling that you can have a large data set. But we intelligently sample to figure out that how can we train on a small subset without compromising on the accuracy? So, yes, there are many techniques that you have developed specifically for machine learning which is what gives us the better performance, better price performance, and also better scalability. >> What about mySQL autopilot? Is there anything that differs from HeatWave ML that is relevant? >> Okay. Interesting you should ask. So mySQL Autopilot is think of it to be an application using machine learning. So mySQL Autopilot uses machine learning to automate various aspects of the database service. So for instance, if you want to figure out that what's the right partitioning scheme to partition the data in memory? We use machine learning techniques to figure out that what's the right, the best column based on the user's workload to partition the data in memory Or given a workload, if you want to figure out what is the right cluster size to provision? That's something we use mySQL autopilot for. And I want to highlight that we don't aware of any other database service which provides this level of machine learning based automation which customers get with mySQL Autopilot. >> Hmm. Interesting. Okay. Last question for both of you. What are you guys working on next? What can customers expect from this collaboration specifically in this space? Maybe Nipun, you can start and then Kamaran can bring us home. >> Sure. So there are two things we are working on. One is based on the feedback we have gotten from customers, we are going to keep making the machine learning capabilities richer in HeatWave ML. That's one dimension. And the second thing is which Kamaran was alluding to earlier, We are looking at the next generation of like processes coming from AMD. And we will be seeing as to how we can more benefit from these processes whether it's the size of the L3 cache, the memory bandwidth, the network bandwidth, and such or the newer effects. And make sure that we leverage the all the greatness which the new generation of processes will offer. >> It's like an engineering playground. Kumaran, let's give you the final word. >> No, that's great. Now look with the Zen 4 CPU cores, we're also bringing in AVX 512 instruction capability. Now our implementation is a little different. It was in, in Rome and Milan, too where we use a double pump implementation. What that means is, you know, we take two cycles to do these instructions. But the key thing there is we don't lower our speed of the CPU. So there's no noisy neighbor effects. And it's something that OCI and the HeatWave has taken full advantage of. And so like, as we go out in time and we see the Zen 4 core, we can... we see up to 96 CPUs that that's going to work really well. So we're collaborating closely with, with OCI and with the HeatWave team here to make sure that we can take advantage of that. And we're also going to upgrade the memory subsystem to get to 12 channels of DDR 5. So it should be, you know there should be a fairly significant boost in absolute performance. But more important or just as importantly in TCO value for the customers, the end customers who are going to adopt this great service. >> I love their relentless innovation guys. Thanks so much for your time. We're going to have to leave it there. Appreciate it. >> Thank you, David. >> Thank you, David. >> Okay. Thank you for watching this special presentation on theCUBE. Your leader in enterprise and emerging tech coverage.

(upbeat music) >> For those of you who've been following the cloud database space, you know that MySQL HeatWave has been on a technology tear over the last 24 months with Oracle claiming record breaking benchmarks relative to other database platforms. So far, those benchmarks remain industry leading as competitors have chosen not to respond, perhaps because they don't feel the need to, or maybe they don't feel that doing so would serve their interest. Regardless, the HeatWave team at Oracle has been very aggressive about its performance claims, making lots of noise, challenging the competition to respond, publishing their scripts to GitHub. But so far, there are no takers, but customers seem to be picking up on these moves by Oracle and it's likely the performance numbers resonate with them. Now, the other area we want to explore, which we haven't thus far, is the engine behind HeatWave and that is AMD. AMD's epic processors have been the powerhouse on OCI, running MySQL HeatWave since day one. And today we're going to explore how these two technology companies are working together to deliver these performance gains and some compelling TCO metrics. In fact, a recent Wikibon analysis from senior analyst Marc Staimer made some TCO comparisons in OLAP workloads relative to AWS, Snowflake, GCP, and Azure databases, you can find that research on wikibon.com. And with that, let me introduce today's guest, Nipun Agarwal senior vice president of MySQL HeatWave and Kumaran Siva, who's the corporate vice president for strategic business development at AMD. Welcome to theCUBE gentlemen. >> Welcome. Thank you. >> Thank you, Dave. >> Hey Nipun, you and I have talked a lot about this. You've been on theCUBE a number of times talking about MySQL HeatWave. But for viewers who may not have seen those episodes maybe you could give us an overview of HeatWave and how it's different from competitive cloud database offerings. >> Sure. So MySQL HeatWave is a fully managed MySQL database service offering from Oracle. It's a single database, which can be used to run transactional processing, analytics and machine learning workloads. So, in the past, MySQL has been designed and optimized for transaction processing. So customers of MySQL when they had to run, analytics machine learning, would need to extract the data out of MySQL, into some other database or service, to run analytics or machine learning. MySQL HeatWave offers a single database for running all kinds of workloads so customers don't need to extract data into some of the database. In addition to having a single database, MySQL HeatWave is also very performant compared to one up databases and also it is very price competitive. So the advantages are; single database, very performant, and very good price performance. >> Yes. And you've published some pretty impressive price performance numbers against competitors. Maybe you could describe those benchmarks and highlight some of the results, please. >> Sure. So one thing to notice that the performance of any database is going to like vary, the performance advantage is going to vary based on, the size of the data and the specific workloads, so the mileage varies, that's the first thing to know. So what we have done is, we have published multiple benchmarks. So we have benchmarks on PPCH or PPCDS and we have benchmarks on different data sizes because based on the customer's workload, the mileage is going to vary, so we want to give customers a broad range of comparisons so that they can decide for themselves. So in a specific case, where we are running on a 30 terabyte PPCH workload, HeatWave is about 18 times better price performance compared to Redshift. 18 times better compared to Redshift, about 33 times better price performance, compared to Snowflake, and 42 times better price performance compared to Google BigQuery. So, this is on 30 Terabyte PPCH. Now, if the data size is different, or the workload is different, the characteristics may vary slightly but this is just to give a flavor of the kind of performance advantage MySQL HeatWave offers. >> And then my last question before we bring in Kumaran. We've talked about the secret sauce being the tight integration between hardware and software, but would you add anything to that? What is that secret sauce in HeatWave that enables you to achieve these performance results and what does it mean for customers? >> So there are three parts to this. One is HeatWave has been designed with a scale out architecture in mind. So we have invented and implemented new algorithms for skill out query processing for analytics. The second aspect is that HeatWave has been really optimized for cloud, commodity cloud, and that's where AMD comes in. So for instance, many of the partitioning schemes we have for processing HeatWave, we optimize them for the L3 cache of the AMD processor. The thing which is very important to our customers is not just the sheer performance but the price performance, and that's where we have had a very good partnership with AMD because not only does AMD help us provide very good performance, but the price performance, right? And that all these numbers which I was showing, big part of it is because we are running on AMD which provides very good price performance. So that's the second aspect. And the third aspect is, MySQL autopilot, which provides machine learning based automation. So it's really these three things, a combination of new algorithms, design for scale out query processing, optimized for commodity cloud hardware, specifically AMD processors, and third, MySQL auto pilot which gives us this performance advantage. >> Great, thank you. So that's a good segue for AMD and Kumaran. So Kumaran, what is AMD bringing to the table? What are the, like, for instance, relevance specs of the chips that are used in Oracle cloud infrastructure and what makes them unique? >> Yeah, thanks Dave. That's a good question. So, OCI is a great customer of ours. They use what we call the top of stack devices meaning that they have the highest core count and they also are very, very fast cores. So these are currently Zen 3 cores. I think the HeatWave product is right now deployed on Zen 2 but will shortly be also on the Zen 3 core as well. But we provide in the case of OCI 64 cores. So that's the largest devices that we build. What actually happens is, because these large number of CPUs in a single package and therefore increasing the density of the node, you end up with this fantastic TCO equation and the cost per performance, the cost per for deployed services like HeatWave actually ends up being extraordinarily competitive and that's a big part of the contribution that we're bringing in here. >> So Zen 3 is the AMD micro architecture which you introduced, I think in 2017, and it's the basis for EPIC, which is sort of the enterprise grade that you really attacked the enterprise with. Maybe you could elaborate a little bit, double click on how your chips contribute specifically to HeatWave's, price performance results. >> Yeah, absolutely. So in the case of HeatWave, so as Nipun alluded to, we have very large L3 caches, right? So in our very, very top end parts just like the Milan X devices, we can go all the way up to like 768 megabytes of L3 cache. And that gives you just enormous performance and performance gains. And that's part of what we're seeing with HeatWave today and that not that they're currently on the second generation ROM based product, 'cause it's a 7,002 based product line running with the 64 cores. But as time goes on, they'll be adopting the next generation Milan as well. And the other part of it too is, as our chip led architecture has evolved, we know, so from the first generation Naples way back in 2017, we went from having multiple memory domains and a sort of NUMA architecture at the time, today we've really optimized that architecture. We use a common I/O Die that has all of the memory channels attached to it. And what that means is that, these scale out applications like HeatWave, are able to really scale very efficiently as they go from a small domain of CPUs to, for example the entire chip, all 64 cores that scaling, is been a key focus for AMD and being able to design and build architectures that can take advantage of that and then have applications like HeatWave that scale so well on it, has been, a key aim of ours. >> And Gen 3 moving up the Italian countryside. Nipun, you've taken the somewhat unusual step of posting the benchmark parameters, making them public on GitHub. Now, HeatWave is relatively new. So people felt that when Oracle gained ownership of MySQL it would let it wilt on the vine in favor of Oracle database, so you lost some ground and now, you're getting very aggressive with HeatWave. What's the reason for publishing those benchmark parameters on GitHub? >> So, the main reason for us to publish price performance numbers for HeatWave is to communicate to our customers a sense of what are the benefits they're going to get when they use HeatWave. But we want to be very transparent because as I said the performance advantages for the customers may vary, based on the data size, based on the specific workloads. So one of the reasons for us to publish, all these scripts on GitHub is for transparency. So we want customers to take a look at the scripts, know what we have done, and be confident that we stand by the numbers which we are publishing, and they're very welcome, to try these numbers themselves. In fact, we have had customers who have downloaded the scripts from GitHub and run them on our service to kind of validate. The second aspect is in some cases, they may be some deviations from what we are publishing versus what the customer would like to run in the production deployments so it provides an easy way, for customers to take the scripts, modify them in some ways which may suit their real world scenario and run to see what the performance advantages are. So that's the main reason, first, is transparency, so the customers can see what we are doing, because of the comparison, and B, if they want to modify it to suit their needs, and then see what is the performance of HeatWave, they're very welcome to do so. >> So have customers done that? Have they taken the benchmarks? And I mean, if I were a competitor, honestly, I wouldn't get into that food fight because of the impressive performance, but unless I had to, I mean, have customers picked up on that, Nipun? >> Absolutely. In fact, we have had many customers who have benchmarked the performance of MySQL HeatWave, with other services. And the fact that the scripts are available, gives them a very good starting point, and then they've also tweaked those queries in some cases, to see what the Delta would be. And in some cases, customers got back to us saying, hey the performance advantage of HeatWave is actually slightly higher than what was published and what is the reason. And the reason was, when the customers were trying, they were trying on the latest version of the service, and our benchmark results were posted let's say, two months back. So the service had improved in those two to three months and customers actually saw better performance. So yes, absolutely. We have seen customers download the scripts, try them and also modify them to some extent and then do the comparison of HeatWave with other services. >> Interesting. Maybe a question for both of you how is the competition responding to this? They haven't said, "Hey, we're going to come up "with our own benchmarks." Which is very common, you oftentimes see that. Although, for instance, Snowflake hasn't responded to data bricks, so that's not their game, but if the customers are actually, putting a lot of faith in the benchmarks and actually using that for buying decisions, then it's inevitable. But how have you seen the competition respond to the MySQL HeatWave and AMD combo? >> So maybe I can take the first track from the database service standpoint. When customers have more choice, it is invariably advantages for the customer because then the competition is going to react, right? So the way we have seen the reaction is that we do believe, that the other database services are going to take a closer eye to the price performance, right? Because if you're offering such good price performance, the vendors are already looking at it. And, you know, instances where they have offered let's say discount to the customers, to kind of at least like close the gap to some extent. And the second thing would be in terms of the capability. So like one of the things which I should have mentioned even early on, is that not only does MySQL HeatWave on AMD, provide very good price performance, say on like a small cluster, but it's all the way up to a cluster size of 64 nodes, which has about 1000 cores. So the point is, that HeatWave performs very well, both on a small system, as well as a huge scale out. And this is again, one of those things which is a differentiation compared to other services so we expect that even other database services will have to improve their offerings to provide the same good scale factor, which customers are now starting to expectancy, with MySQL HeatWave. >> Kumaran, anything you'd add to that? I mean, you guys are an arms dealer, you love all your OEMs, but at the same time, you've got chip competitors, Silicon competitors. How do you see the competitive-- >> I'd say the broader answer and the big picture for AMD, we're very maniacally focused on our customers, right? And OCI and Oracle are huge and important customers for us, and this particular use cases is extremely interesting both in that it takes advantage, very well of our architecture and it pulls out some of the value that AMD bring. I think from a big picture standpoint, our aim is to execute, to build to bring out generations of CPUs, kind of, you know, do what we say and say, sorry, say what we do and do what we say. And from that point of view, we're hitting, the schedules that we say, and being able to bring out the latest technology and bring it in a TCO value proposition that generationally keeps OCI and HeatWave ahead. That's the crux of our partnership here. >> Yeah, the execution's been obvious for the last several years. Kumaran, staying with you, how would you characterize the collaboration between, the AMD engineers and the HeatWave engineering team? How do you guys work together? >> No, I'd say we're in a very, very deep collaboration. So, there's a few aspects where, we've actually been working together very closely on the code and being able to optimize for both the large L3 cache that AMD has, and so to be able to take advantage of that. And then also, to be able to take advantage of the scaling. So going between, you know, our architecture is chip like based, so we have these, the CPU cores on, we call 'em CCDs and the inter CCD communication, there's opportunities to optimize an application level and that's something we've been engaged with. In the broader engagement, we are going back now for multiple generations with OCI, and there's a lot of input that now, kind of resonates in the product line itself. And so we value this very close collaboration with HeatWave and OCI. >> Yeah, and the cadence, Nip, and you and I have talked about this quite a bit. The cadence has been quite rapid. It's like this constant cycle every couple of months I turn around, is something new on HeatWave. But for question again, for both of you, what new things do you think that organizations, customers, are going to be able to do with MySQL HeatWave if you could look out next 12 to 18 months, is there anything you can share at this time about future collaborations? >> Right, look, 12 to 18 months is a long time. There's going to be a lot of innovation, a lot of new capabilities coming out on in MySQL HeatWave. But even based on what we are currently offering, and the trend we are seeing is that customers are bringing, more classes of workloads. So we started off with OLTP for MySQL, then it went to analytics. Then we increased it to mixed workloads, and now we offer like machine learning as alike. So one is we are seeing, more and more classes of workloads come to MySQL HeatWave. And the second is a scale, that kind of data volumes people are using HeatWave for, to process these mixed workloads, analytics machine learning OLTP, that's increasing. Now, along the way we are making it simpler to use, we are making it more cost effective use. So for instance, last time, when we talked about, we had introduced this real time elasticity and that's something which is a very, very popular feature because customers want the ability to be able to scale out, or scale down very efficiently. That's something we provided. We provided support for compression. So all of these capabilities are making it more efficient for customers to run a larger part of their workloads on MySQL HeatWave, and we will continue to make it richer in the next 12 to 18 months. >> Thank you. Kumaran, anything you'd add to that, we'll give you the last word as we got to wrap it. >> No, absolutely. So, you know, next 12 to 18 months we will have our Zen 4 CPUs out. So this could potentially go into the next generation of the OCI infrastructure. This would be with the Genoa and then Bergamo CPUs taking us to 96 and 128 cores with 12 channels at DDR five. This capability, you know, when applied to an application like HeatWave, you can see that it'll open up another order of magnitude potentially of use cases, right? And we're excited to see what customers can do do with that. It certainly will make, kind of the, this service, and the cloud in general, that this cloud migration, I think even more attractive. So we're pretty excited to see how things evolve in this period of time. >> Yeah, the innovations are coming together. Guys, thanks so much, we got to leave it there really appreciate your time. >> Thank you. >> All right, and thank you for watching this special Cube conversation, this is Dave Vellante, and we'll see you next time. (soft calm music)

[Music] the history of exadata in the platform is really unique and from my vantage point it started earlier this century as a skunk works inside of oracle called project sage back when grid computing was the next big thing oracle saw that betting on standard hardware would put it on an industry curve that would rapidly evolve and i remember the oracle hp database machine which was announced at oracle open world almost 15 years ago and then exadata kept evolving after the sun acquisition it became a platform that had tightly integrated hardware and software and today exadata it keeps evolving almost like a chameleon to address more workloads and reach new performance levels last april for example oracle announced the availability of exadata x9m in oci oracle cloud infrastructure and introduced the ability to run the autonomous database service or the exa data database service you know oracle often talks about they call it stock exchange performance level kind of no description needed and sort of related capabilities the company as we know is fond of putting out benchmarks and comparisons with previous generations of product and sometimes competitive products that underscore the progress that's being made with exadata such as 87 percent more iops with metrics for latency measured in microseconds mics instead of milliseconds and many other numbers that are industry-leading and compelling especially for mission-critical workloads one thing that hasn't been as well publicized is that exadata on oci is using amd's epyc processors in the database service epyc is not eastern pacific yacht club for all your sailing buffs rather it stands for extreme performance yield computing the enterprise grade version of amd's zen architecture which has been a linchpin of amd's success in terms of penetrating enterprise markets and to focus on the innovations that amd and oracle are bringing to market we have with us today juan loyza who's executive vice president of mission critical technologies at oracle and mark papermaster who's the cto and evp of technology and engineering at amd juan welcome back to the show mark great to have you on thecube and your first appearance thanks for coming on yep happy to be here thank you all right juan let's start with you you've been on thecube a number of times as i said and you've talked about how exadata is a top platform for oracle database we've covered that extensively what's different and unique from your point of view about exadata cloud infrastructure x9m on oci yeah so as you know exadata it's designed top down to be the best possible platform for database uh it has a lot of unique capabilities like we make extensive use of rdma smart storage we take advantage of you know everything we can in the leading uh hardware platforms and x9m is our next generation platform and it does exactly that we're always wanting to be to get all the best that we can from the available hardware that our partners like amd produce and so that's what x9 in it is it's faster more capacity lower latency more ios pushing the limits of the hardware technology so we don't want to be the limit the software the database software should not be the limit it should be uh the actual physical limits of the hardware and that that's what x9m is all about why won amd chips in x9m uh yeah so we're we're uh introducing uh amd chips we think they provide outstanding performance uh both for oltp and for analytic workloads and it's really that simple we just think that performance is outstanding in the product yeah mark your career is quite amazing i've been around long enough to remember the transition to cmos from emitter coupled logic in the mainframe era back when you were at ibm that was an epic technology call at the time i was of course steeped as an analyst at idc in the pc era and like like many witnessed the tectonic shift that apple's ipod and iphone caused and the timing of you joining amd is quite important in my view because it coincided with the year that pc volumes peaked and marked the beginning of what i call a stagflation period for x86 i could riff on history for hours but let's focus on the oracle relationship mark what are the relevant capabilities and key specs of the amd chips that are used in exadata x9m on oracle's cloud well thanks and and uh it's really uh the basis of i think the great partnership that we have with oracle on exadata x9m and that is that the amd technology uses our third generation of zen processors zen was you know architected to really bring high performance you know back to x86 a very very strong road map that we've executed you know on schedule to our commitments and this third generation does all of that it uses a seven nanometer cpu that is a you know core that was designed to really bring uh throughput uh bring you know really high uh efficiency uh to computing uh and just deliver raw capabilities and so uh for uh exadata x9m uh it's really leveraging all of that it's it's a uh implemented in up to 64 cores per socket it's got uh you know really anywhere from 128 to 168 pcie gen 4 io connectivity so you can you can really attach uh you know all of the uh the necessary uh infrastructure and and uh storage uh that's needed uh for exadata performance and also memory you have to feed the beast for those analytics and for the oltp that juan was talking about and so it does have eight lanes of memory for high performance ddr4 so it's really as a balanced processor and it's implemented in a way to really optimize uh high performance that that is our whole focus of uh amd it's where we've you know reset the company focus on years ago and uh again uh you know great to see uh you know the the super smart uh you know database team at oracle really a partner with us understand those capabilities and it's been just great to partner with them to uh you know to you know enable oracle to really leverage the capabilities of the zen processor yeah it's been a pretty amazing 10 or 11 years for both companies but mark how specifically are you working with oracle at the engineering and product level you know and what does that mean for your joint customers in terms of what they can expect from the collaboration well here's where the collaboration really comes to play you think about a processor and you know i'll say you know when one's team first looked at it there's general benchmarks and the benchmarks are impressive but they're general benchmarks and you know and they showed you know the i'll say the you know the base processing capability but the partnership comes to bear uh when it when it means optimizing for the workloads that exadata x9m is really delivering to the end customers and that's where we dive down and and as we uh learn from the oracle team we learned to understand where bottlenecks could be uh where is there tuning that we could in fact in fact really boost the performance above i'll say that baseline that you get in the generic benchmarks and that's what the teams have done so for instance you look at you know optimizing latency to rdma you look at just throughput optimizing throughput on otp and database processing when you go through the workloads and you take the traces and you break it down and you find the areas that are bottlenecking and then you can adjust we have you know thousands of parameters that can be adjusted for a given workload and that's again that's the beauty of the partnership so we have the expertise on the cpu engineering uh you know oracle exudated team knows innately what the customers need to get the most out of their platform and when the teams came together we actually achieved anywhere from 20 percent to 50 gains on specific workloads it's really exciting to see so okay so so i want to follow up on that is that different from the competition how are you driving customer value you mentioned some you know some some percentage improvements are you measuring primarily with with latency how do you look at that well uh you know we are differentiated with the uh in the number of factors we bring a higher core density we bring the highest core density certainly in x86 and and moreover what we've led the industry is how to scale those cores we have a very high performance fabric that connects those together so as as a customer needs more cores again we scale anywhere from 8 to 64 cores but what the trick is uh that is you add more cores you want the scale the scale to be as close to linear as possible and so that's a differentiation we have and we enable that again with that balanced computer of cpu io and memory that we design but the key is you know we pride ourselves at amd of being able to partner in a very deep fashion with our customers we listen very well i think that's uh what we've had the opportunity uh to do with uh juan and his team we appreciate that and and that is how we got the kind of performance benefits that i described earlier it's working together almost like one team and in bringing that best possible capability to the end customers great thank you for that one i want to come back to you can both the exadata database service and the autonomous database service can they take advantage of exadata cloud x9m capabilities that are in that platform yeah absolutely um you know autonomous is basically our self-driving version of the oracle database but fundamentally it is the same uh database course so both of them will take advantage of the tremendous performance that we're getting now you know when when mark takes about 64 cores that's for chip we have two chips you know it's a two socket server so it's 128 128-way processor and then from our point of view there's two threads so from the database point there's 200 it's a 256-way processor and so there's a lot of raw performance there and we've done a lot of work with the amd team to make sure that we deliver that to our customers for all the different kinds of workload including otp analytics but also including for our autonomous database so yes absolutely allah takes advantage of it now juan you know i can't let you go without asking about the competition i've written extensively about the big four hyperscale clouds specifically aws azure google and alibaba and i know that don't hate me sometimes it angers some of my friends at oracle ibm too that i don't include you in that list but but i see oracle specifically is different and really the cloud for the most demanding applications and and top performance databases and not the commodity cloud which of course that angers all my friends at those four companies so i'm ticking everybody off so how does exadata cloud infrastructure x9m compare to the likes of aws azure google and other database cloud services in terms of oltp and analytics value performance cost however you want to frame it yeah so our architecture is fundamentally different uh we've architected our database for the scale out environment so for example we've moved intelligence in the storage uh we've put uh remote direct memory access we put persistent memory into our product so we've done a lot of architectural changes that they haven't and you're starting to see a little bit of that like if you look at some of the things that amazon and google are doing they're starting to realize that hey if you're gonna achieve good results you really need to push some database uh processing into the storage so so they're taking baby steps toward that you know you know roughly 15 years after we we've had a product and again at some point they're gonna realize you really need rdma you really need you know more uh direct access to those capabilities so so they're slowly getting there but you know we're well ahead and what you know the way this is delivered is you know better availability better performance lower latency higher iops so and this is why our customers love our product and you know if you if you look at the global fortune 100 over 90 percent of them are running exit data today and even in the in our cloud uh you know over 60 of the global 100 are running exadata in the oracle cloud because of all the differentiated uh benefits that they get uh from the product uh so yeah we're we're well ahead in the in the database space mark last question for you is how do you see this relationship evolving in the future can you share a little road map for the audience you bet well first off you know given the deep partnership that we've had on exudate x9m uh it it's really allowed us to inform our future design so uh in our current uh third generation epic epyc is uh that is really uh what we call our epic server offerings and it's a 7003 third gen in and exudate x9m so what about fourth gen well fourth gen is well underway uh you know it and uh and uh you know ready to you know for the for the future but it incorporates learning uh that we've done in partnership with with oracle uh it's gonna have even more through capabilities it's gonna have expanded memory capabilities because there's a cxl connect express link that'll expand even more memory opportunities and i could go on so you know that's the beauty of a deep partnership as it enables us to really take that learning going forward it pays forward and we're very excited to to fold all of that into our future generations and provide even a better capabilities to one and his team moving forward yeah you guys have been obviously very forthcoming you have to be with with with zen and epic juan anything you'd like to add as closing comments yeah i would say that in the processor market there's been a real acceleration in innovation in the last few years um there was you know a big move 10 15 years ago when multi-core processors came out and then you know we were on that for a while and then things started staggering but in the last two or three years and amd has been leading this um there's been a dramatic uh acceleration in innovation in this space so it's very exciting to be part of this and and customers are getting a big benefit from this all right chance hey thanks for coming back in the cube today really appreciate your time thanks glad to be here all right thank you for watching this exclusive cube conversation this is dave vellante from thecube and we'll see you next time [Music]

>> From theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> In the words of my colleague CTO David Nicholson, Broadcom buys old cars, not to restore them to their original luster and beauty. Nope. They buy classic cars to extract the platinum that's inside the catalytic converter and monetize that. Broadcom's planned 61 billion acquisition of VMware will mark yet another new era and chapter for the virtualization pioneer, a mere seven months after finally getting spun out as an independent company by Dell. For VMware, this means a dramatically different operating model with financial performance and shareholder value creation as the dominant and perhaps the sole agenda item. For customers, it will mean a more focused portfolio, less aspirational vision pitches, and most certainly higher prices. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we'll share data, opinions and customer insights about this blockbuster deal and forecast the future of VMware, Broadcom and the broader ecosystem. Let's first look at the key deal points, it's been well covered in the press. But just for the record, $61 billion in a 50/50 cash and stock deal, resulting in a blended price of $138 per share, which is a 44% premium to the unaffected price, i.e. prior to the news breaking. Broadcom will assume 8 billion of VMware debt and promises that the acquisition will be immediately accretive and will generate 8.5 billion in EBITDA by year three. That's more than 4 billion in EBITDA relative to VMware's current performance today. In a classic Broadcom M&A approach, the company promises to dilever debt and maintain investment grade ratings. They will rebrand their software business as VMware, which will now comprise about 50% of revenues. There's a 40 day go shop and importantly, Broadcom promises to continue to return 60% of its free cash flow to shareholders in the form of dividends and buybacks. Okay, with that out of the way, we're going to get to the money slide literally in a moment that Broadcom shared on its investor call. Broadcom has more than 20 business units. It's CEO Hock Tan makes it really easy for his business unit managers to understand. Rule number one, you agreed to an operating plan with targets for revenue, growth, EBITDA, et cetera, hit your numbers consistently and we're good. You'll be very well compensated and life will be wonderful for you and your family. Miss the number, and we're going to have a frank and uncomfortable bottom line discussion. You'll four, perhaps five quarters to turn your business around, if you don't, we'll kill it or sell it if we can. Rule number two, refer to rule number one. Hello, VMware, here's the money slide. I'll interpret the bullet points on the left for clarity. Your fiscal year 2022 EBITDA was 4.7 billion. By year three, it will be 8.5 billion. And we Broadcom have four knobs to turn with you, VMware to help you get there. First knob, if it ain't recurring revenue with rubber stamp renewals, we're going to convert that revenue or kill it. Knob number two, we're going to focus R&D in the most profitable areas of the business. AKA expect the R&D budget to be cut. Number three, we're going to spend less on sales and marketing by focusing on existing customers. We're not going to lose money today and try to make it up many years down the road. And number four, we run Broadcom with 1% GNA. You will too. Any questions? Good. Now, just to give you a little sense of how Broadcom runs its business and how well run a company it is, let's do a little simple comparison with this financial snapshot. All we're doing here is taking the most recent quarterly earnings reports from Broadcom and VMware respectively. We take the quarterly revenue and multiply by four X to get the revenue run rate and then we calculate the ratios off of the most recent quarters revenue. It's worth spending some time on this to get a sense of how profitable the Broadcom business actually is and what the spreadsheet gurus at Broadcom are seeing with respect to the possibilities for VMware. So combined, we're talking about a 40 plus billion dollar company. Broadcom is growing at more than 20% per year. Whereas VMware's latest quarter showed a very disappointing 3% growth. Broadcom is mostly a hardware company, but its gross margin is in the high seventies. As a software company of course VMware has higher gross margins, but FYI, Broadcom's software business, the remains of Symantec and what they purchased as CA has 90% gross margin. But the I popper is operating margin. This is all non gap. So it excludes things like stock based compensation, but Broadcom had 61% operating margin last quarter. This is insanely off the charts compared to VMware's 25%. Oracle's non gap operating margin is 47% and Oracle is an incredibly profitable company. Now the red box is where the cuts are going to take place. Broadcom doesn't spend much on marketing. It doesn't have to. It's SG&A is 3% of revenue versus 18% for VMware and R&D spend is almost certainly going to get cut. The other eye popper is free cash flow as a percentage of revenue at 51% for Broadcom and 29% for VMware. 51%. That's incredible. And that my dear friends is why Broadcom a company with just under 30 billion in revenue has a market cap of 230 billion. Let's dig into the VMware portfolio a bit more and identify the possible areas that will be placed under the microscope by Hock Tan and his managers. The data from ETR's latest survey shows the net score or spending momentum across VMware's portfolio in this chart, net score essentially measures the net percent of customers that are spending more on a specific product or vendor. The yellow bar is the most recent survey and compares the April 22 survey data to April 21 and January of 22. Everything is down in the yellow from January, not surprising given the economic outlook and the change in spending patterns that we've reported. VMware Cloud on AWS remains the product in the ETR survey with the most momentum. It's the only offering in the portfolio with spending momentum above the 40% line, a level that we consider highly elevated. Unified Endpoint Management looks more than respectable, but that business is a rock fight with Microsoft. VMware Cloud is things like VMware Cloud foundation, VCF and VMware's cross cloud offerings. NSX came from the Nicira acquisition. Tanzu is not yet pervasive and one wonders if VMware is making any money there. Server is ESX and vSphere and is the bread and butter. That is where Broadcom is going to focus. It's going to look at VSAN and NSX, which is software probably profitable. And of course the other products and see if the investments are paying off, if they are Broadcom will keep, if they are not, you can bet your socks, they will be sold off or killed. Carbon Black is at the far right. VMware paid $2.1 billion for Carbon Black. And it's the lowest performer on this list in terms of net score or spending momentum. And that doesn't mean it's not profitable. It just doesn't have the momentum you'd like to see, so you can bet that is going to get scrutiny. Remember VMware's growth has been under pressure for the last several years. So it's been buying companies, dozens of them. It bought AirWatch, bought Heptio, Carbon Black, Nicira, SaltStack, Datrium, Versedo, Bitnami, and on and on and on. Many of these were to pick up engineering teams. Some of them were to drive new revenue. Now this is definitely going to be scrutinized by Broadcom. So that helps explain why Michael Dell would sell VMware. And where does VMware go from here? It's got great core product. It's an iconic name. It's got an awesome ecosystem, fantastic distribution channel, but its growth is slowing. It's got limited developer chops in a world that developers and cloud native is all the rage. It's got a far flung R&D agenda going at war with a lot of different places. And it's increasingly fighting this multi front war with cloud companies, companies like Cisco, IBM Red Hat, et cetera. VMware's kind of becoming a heavy lift. It's a perfect acquisition target for Broadcom and why the street loves this deal. And we titled this Breaking Analysis taming the VMware beast because VMware is a beast. It's ubiquitous. It's an epic software platform. EMC couldn't control it. Dell used it as a piggy bank, but really didn't change its operating model. Broadcom 100% will. Now one of the things that we get excited about is the future of systems architectures. We published a breaking analysis about a year ago, talking about AWS's secret weapon with Nitro and it's Annapurna custom Silicon efforts. Remember it acquired Annapurna for a measly $350 million. And we talked about how there's a new architecture and a new price performance curve emerging in the enterprise, driven by AWS and being followed by Microsoft, Google, Alibaba, a trend toward custom Silicon with the arm based Nitro and which is AWS's hypervisor and Nick strategy, enabling processor diversity with things like Graviton and Trainium and other diverse processors, really diversifying away from x86 and how this leads to much faster product cycles, faster tape out, lower costs. And our premise was that everyone in the data center is going to competes, is going to need a Nitro to be competitive long term. And customers are going to gravitate toward the most economically favorable platform. And as we describe the landscape with this chart, we've updated this for this Breaking Analysis and we'll come back to nitro in a moment. This is a two dimensional graphic with net score or spending momentum on the vertical axis and overlap formally known as market share or presence within the survey, pervasiveness that's on the horizontal axis. And we plot various companies and products and we've inserted VMware's net score breakdown. The granularity in those colored bars on the bottom right. Net score is essentially the green minus the red and a couple points on that. VMware in the latest survey has 6% new adoption. That's that lime green. It's interesting. The question Broadcom is going to ask is, how much does it cost you to acquire that 6% new. 32% of VMware customers in the survey are increasing spending, meaning they're increasing spending by 6% or more. That's the forest green. And the question Broadcom will dig into is what percent of that increased spend (chuckles) you're capturing is profitable spend? Whatever isn't profitable is going to be cut. Now that 52% gray area flat spending that is ripe for the Broadcom picking, that is the fat middle, and those customers are locked and loaded for future rent extraction via perpetual renewals and price increases. Only 8% of customers are spending less, that's the pinkish color and only 3% are defecting, that's the bright red. So very, very sticky profile. Perfect for Broadcom. Now the rest of the chart lays out some of the other competitor names and we've plotted many of the VMware products so you can see where they fit. They're all pretty respectable on the vertical axis, that's spending momentum. But what Broadcom wants is that core ESX vSphere base where we've superimposed the Broadcom logo. Broadcom doesn't care so much about spending momentum. It cares about profitability potential and then momentum. AWS and Azure, they're setting the pace in this business, in the upper right corner. Cisco very huge presence in the data center, as does Intel, they're not in the ETR survey, but we've superimposed them. Now, Intel of course, is in a dog fight within Nvidia, the Arm ecosystem, AMD, don't forget China. You see a Google cloud platform is in there. Oracle is also on the chart as well, somewhat lower on the vertical axis, but it doesn't have that spending momentum, but it has a big presence. And it owns a cloud as we've talked about many times and it's highly differentiated. It's got a strategy that allows it to differentiate from the pack. It's very financially driven. It knows how to extract lifetime value. Safra Catz operates in many ways, similar to what we're seeing from Hock Tan and company, different from a portfolio standpoint. Oracle's got the full stack, et cetera. So it's a different strategy. But very, very financially savvy. You could see IBM and IBM Red Hat in the mix and then Dell and HP. I want to come back to that momentarily to talk about where value is flowing. And then we plotted Nutanix, which with Acropolis could suck up some V tax avoidance business. Now notice Symantec and CA, relatively speaking in the ETR survey, they have horrible spending momentum. As we said, Broadcom doesn't care. Hock Tan is not going for growth at the expense of profitability. So we fully expect VMware to come down on the vertical axis over time and go up on the profit scale. Of course, ETR doesn't measure the profitability here. Now back to Nitro, VMware has this thing called Project Monterey. It's essentially their version of Nitro and will serve as their future architecture diversifying off x86 and accommodating alternative processors. And a much more efficient performance, price in energy consumption curve. Now, one of the things that we've advocated for, we said this about Dell and others, including VMware to take a page out of AWS and start developing custom Silicon to better integrate hardware and software and accelerate multi-cloud or what we call supercloud. That layer above the cloud, not just running on individual clouds. So this is all about efficiency and simplicity to own this space. And we've challenged organizations to do that because otherwise we feel like the cloud guys are just going to have consistently better costs, not necessarily price, but better cost structures, but it begs the question. What happens to Project Monterey? Hock Tan and Broadcom, they don't invest in something that is unproven and doesn't throw off free cash flow. If it's not going to pay off for years to come, they're probably not going to invest in it. And yet Project Monterey could help secure VMware's future in not only the data center, but at the edge and compete more effectively with cloud economics. So we think either Project Monterey is toast or the VMware team will knock on the door of one of Broadcom's 20 plus business units and say, guys, what if we work together with you to develop a version of Monterey that we can use and sell to everyone, it'd be the arms dealer to everyone and be competitive with the cloud and other players out there and create the de facto standard for data center performance and supercloud. I mean, it's not outrageously expensive to develop custom Silicon. Tesla is doing it for example. And Broadcom obviously is capable of doing it. It's got good relationships with semiconductor fabs. But I think this is going to be a tough sell to Broadcom, unless VMware can hide this in plain site and make it profitable fast, like AWS most likely has with Nitro and Graviton. Then Project Monterey and our pipe dream of alternatives to Nitro in the data center could happen but if it can't, it's going to be toast. Or maybe Intel or Nvidia will take it over or maybe the Monterey team will spin out a VMware and do a Pensando like deal and demonstrate the viability of this concept and then Broadcom will buy it back in 10 years. Here's a double click on that previous data that we put in tabular form. It's how the data on that previous slide was plotted. I just want to give you the background data here. So net score spending momentum is the sorted on the left. So it's sorted by net score in the left hand chart, that was the y-axis in the previous data set and then shared and or presence in the data set is the right hand chart. In other words, it's sorted on the right hand chart, right hand table. That right most column is shared and you can see it's sorted top to bottom, and that was the x-axis on the previous chart. The point is not many on the left hand side are above the 40% line. VMware Cloud on AWS is, it's expensive, so it's probably profitable and it's probably a keeper. We'll see about the rest of VMware's portfolio. Like what happens to Tanzu for example. On the right, we drew a red line, just arbitrarily at those companies and products with more than a hundred mentions in the survey, everything but Tanzu from VMware makes that cut. Again, this is no indication of profitability here, and that's what's going to matter to Broadcom. Now let's take a moment to address the question of Broadcom as a software company. What the heck do they know about software, right. Well, they're not dumb over there and they know how to run a business, but there is a strategic rationale to this move beyond just doing portfolios and extracting rents and cutting R&D, et cetera, et cetera. Why, for example, isn't Broadcom going after coming back to Dell or HPE, it could pick up for a lot less than VMware, and they got way more revenue than VMware. Well, it's obvious, software's more profitable of course, and Broadcom wants to move up the stack, but there's a trend going on, which Broadcom is very much in touch with. First, it sells to Dell and HPE and Cisco and all the OEM. so it's not going to disrupt that. But this chart shows that the value is flowing away from traditional servers and storage and networking to two places, merchant Silicon, which itself is morphing. Broadcom... We focus on the left hand side of this chart. Broadcom correctly believes that the world is shifting from a CPU centric center of gravity to a connectivity centric world. We've talked about this on theCUBE a lot. You should listen to Broadcom COO Charlie Kawwas speak about this. It's all that supporting infrastructure around the CPU where value is flowing, including of course, alternative GPUs and XPUs, and NPUs et cetera, that are sucking the value out of the traditional x86 architecture, offloading some of the security and networking and storage functions that traditionally have been done in x86 which are part of the waste right now in the data center. This is that shifting dynamic of Moore's law. Moore's law, not keeping pace. It's slowing down. It's slower relative to some of the combinatorial factors. When you add up in all the CPU and GPU and NPU and accelerators, et cetera. So we've talked about this a lot in Breaking Analysis episodes. So the value is shifting left within that middle circle. And it's shifting left within that left circle toward components, other than CPU, many of which Broadcom supplies. And then you go back to the middle, value is shifting from that middle section, that traditional data center up into hyperscale clouds, and then to the right toward infrastructure software to manage all that equipment in the data center and across clouds. And look Broadcom is an arms dealer. They simply sell to everyone, locking up key vectors of the value chain, cutting costs and raising prices. It's a pretty straightforward strategy, but not for the fate of heart. And Broadcom has become pretty good at it. Let's close with the customer feedback. I spoke with ETRs Eric Bradley this morning. He and I both reached out to VMware customers that we know and got their input. And here's a little snapshot of what they said. I'll just read this. Broadcom will be looking to invest in the core and divest of any underperforming assets, right on. It's just what we were saying. This doesn't bode well for future innovation, this is a CTO at a large travel company. Next comment, we're a Carbon Black customer. VMware didn't seem to interfere with Carbon Black, but now that we're concerned about short term disruption to their tech roadmap and long term, are they going to split and be sold off like Symantec was, this is a CISO at a large hospitality organization. Third comment, I got directly from a VMware practitioner, an IT director at a manufacturing firm. This individual said, moving off VMware would be very difficult for us. We have over 500 applications running on VMware, and it's really easy to manage. We're not going to move those into the cloud and we're worried Broadcom will raise prices and just extract rents. Last comment, we'll share as, Broadcom sees the cloud data center and IoT is their next revenue source. The VMware acquisition provides them immediate virtualization capabilities to support a lightweight IoT offering. Big concern for customers is what technology they will invest in and innovate, and which will be stripped off and sold. Interesting. I asked David Floyer to give me a back of napkin estimate for the following question. I said, David, if you're running mission critical applications on VMware, how much would it increase your operating cost moving those applications into the cloud? Or how much would it save? And he said, Dave, VMware's really easy to run. It can run any application pretty much anywhere, and you don't need an army of people to manage it. All your processes are tied to VMware, you're locked and loaded. Move that into the cloud and your operating cost would double by his estimates. Well, there you have it. Broadcom will pinpoint the optimal profit maximization strategy and raise prices to the point where customers say, you know what, we're still better off staying with VMware. And sadly, for many practitioners there aren't a lot of choices. You could move to the cloud and increase your cost for a lot of your applications. You could do it yourself with say Zen or OpenStack. Good luck with that. You could tap Nutanix. That will definitely work for some applications, but are you going to move your entire estate, your application portfolio to Nutanix? It's not likely. So you're going to pay more for VMware and that's the price you're going to pay for two decades of better IT. So our advice is get out ahead of this, do an application portfolio assessment. If you can move apps to the cloud for less, and you haven't yet, do it, start immediately. Definitely give Nutanix a call, but going to have to be selective as to what you actually can move, forget porting to OpenStack, or do it yourself Hypervisor, don't even go there. And start building new cloud native apps where it makes sense and let the VMware stuff go into manage decline. Let certain apps just die through attrition, shift your development resources to innovation in the cloud and build a brick wall around the stable apps with VMware. As Paul Maritz, the former CEO of VMware said, "We are building the software mainframe". Now marketing guys got a hold of that and said, Paul, stop saying that, but it's true. And with Broadcom's help that day we'll soon be here. That's it for today. Thanks to Stephanie Chan who helps research our topics for Breaking Analysis. Alex Myerson does the production and he also manages the Breaking Analysis podcast. Kristen Martin and Cheryl Knight help get the word out on social and thanks to Rob Hof, who was our editor in chief at siliconangle.com. Remember, these episodes are all available as podcast, wherever you listen, just search Breaking Analysis podcast. Check out ETRs website at etr.ai for all the survey action. We publish a full report every week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com. You can DM me at DVellante or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (upbeat music)

(upbeat music) >> Okay, we're back. I'm John Furrier with theCUBE and we're going to go deeper into a deep dive into unified cloud networking solution from Pluribus and NVIDIA. And we'll examine some of the use cases with Alessandro Barbieri, VP of product management at Pluribus Networks and Pete Lumbis, the director of technical marketing and video remotely. Guys thanks for coming on, appreciate it. >> Yeah thanks a lot. >> I'm happy to be here. >> So a deep dive, let's get into the what and how. Alessandro, we heard earlier about the Pluribus and NVIDIA partnership and the solution you're working together in. What is it? >> Yeah, first let's talk about the what. What are we really integrating with the NVIDIA BlueField the DPU technology? Pluribus has been shipping in volume in multiple mission critical networks, this Netvisor ONE network operating systems. It runs today on merchant silicon switches and effectively it's standard based open network operating system for data center. And the novelty about this operating system is that it integrates distributed the control plane to automate effect with SDN overlay. This automation is completely open and interoperable and extensible to other type of clouds. It's not enclosed. And this is actually what we're now porting to the NVIDIA DPU. >> Awesome, so how does it integrate into NVIDIA hardware and specifically how is Pluribus integrating its software with the NVIDIA hardware? >> Yeah, I think we leverage some of the interesting properties of the BlueField DPU hardware which allows actually to integrate our network operating system in a manner which is completely isolated and independent from the guest operating system. So the first byproduct of this approach is that whatever we do at the network level on the DPU card is completely agnostic to the hypervisor layer or OS layer running on the host. Even more, we can also independently manage this network node this switch on a NIC effectively, managed completely independently from the host. You don't have to go through the network operating system running on X86 to control this network node. So you truly have the experience effectively top of rack for virtual machine or a top of rack for Kubernetes spots, where if you allow me with analogy, instead of connecting a server NIC directly to a switchboard, now we are connecting a VM virtual interface to a virtual interface on the switch on an niche. And also as part of this integration, we put a lot of effort, a lot of emphasis in accelerating the entire data plan for networking and security. So we are taking advantage of the NVIDIA DOCA API to program the accelerators. And these you accomplish two things with that. Number one, you have much better performance. They're running the same network services on an X86 CPU. And second, this gives you the ability to free up I would say around 20, 25% of the server capacity to be devoted either to additional workloads to run your cloud applications or perhaps you can actually shrink the power footprint and compute footprint of your data center by 20% if you want to run the same number of compute workloads. So great efficiencies in the overall approach. >> And this is completely independent of the server CPU, right? >> Absolutely, there is zero code from Pluribus running on the X86. And this is why we think this enables a very clean demarcation between compute and network. >> So Pete, I got to get you in here. We heard that the DPU enable cleaner separation of DevOps and NetOps. Can you explain why that's important because everyone's talking DevSecOps, right? Now, you've got NetSecOps. This separation, why is this clean separation important? >> Yeah, I think, it's a pragmatic solution in my opinion. We wish the world was all kind of rainbows and unicorns, but it's a little messier than that. I think a lot of the DevOps stuff and that mentality and philosophy. There's a natural fit there. You have applications running on servers. So you're talking about developers with those applications integrating with the operators of those servers. Well, the network has always been this other thing and the network operators have always had a very different approach to things than compute operators. And I think that we in the networking industry have gotten closer together but there's still a gap, there's still some distance. And I think that distance isn't going to be closed and so, again, it comes down to pragmatism. And I think one of my favorite phrases is look, good fences make good neighbors. And that's what this is. >> Yeah, and it's a great point 'cause DevOps has become kind of the calling car for cloud, right? But DevOps is a simply infrastructures code and infrastructure is networking, right? So if infrastructure is code you're talking about that part of the stack under the covers, under the hood if you will. This is super important distinction and this is where the innovation is. Can you elaborate on how you see that because this is really where the action is right now? >> Yeah, exactly. And I think that's where one from the policy, the security, the zero trust aspect of this, right? If you get it wrong on that network side, all of a sudden you can totally open up those capabilities. And so security's part of that. But the other part is thinking about this at scale, right? So we're taking one top of rack switch and adding up to 48 servers per rack. And so that ability to automate, orchestrate and manage its scale becomes absolutely critical. >> Alessandro, this is really the why we're talking about here and this is scale. And again, getting it right. If you don't get it right, you're going to be really kind of up you know what? So this is a huge deal. Networking matters, security matters, automation matters, DevOps, NetOps, all coming together clean separation. Help us understand how this joint solution with NVIDIA fits into the Pluribus unified cloud networking vision because this is what people are talking about and working on right now. >> Yeah, absolutely. So I think here with this solution we're attacking two major problems in cloud networking. One, is operation of cloud networking and the second, is distributing security services in the cloud infrastructure. First, let me talk about first what are we really unifying? If we're unifying something, something must be at least fragmented or disjointed. And what is disjointed is actually the network in the cloud. If you look wholistically how networking is deployed in the cloud, you have your physical fabric infrastructure, right? Your switches and routers. You build your IP clause, fabric leaf and spine topologies. This is actually a well understood problem I would say. There are multiple vendors with let's say similar technologies, very well standardized, very well understood and almost a commodity I would say building an IP fabric these days, but this is not the place where you deploy most of your services in the cloud particularly from a security standpoint. Those services are actually now moved into the compute layer where cloud builders have to instrument a separate network virtualization layer where they deploy segmentation and security closer to the workloads. And this is where the complication arise. This high value part of the cloud network is where you have a plethora of options that they don't talk to each other and they're very dependent on the kind of hypervisor or compute solution you choose. For example, the networking API between an ESXi environment or an Hyper-V or a Zen are completely disjointed. You have multiple orchestration layers. And then when you throw in also Kubernetes in this type of architecture, you are introducing yet another level of networking. And when Kubernetes runs on top of VMs which is a prevalent approach, you actually are stuck in multiple networks on the compute layer that they eventually ran on the physical fabric infrastructure. Those are all ships in the knights effectively, right? They operate as completely disjointed and we're trying to tackle this problem first with the notion of a unified fabric which is independent from any workloads whether this fabric spans on a switch which can be connected to bare metal workload or can span all the way inside the DPU where you have your multi hypervisor compute environment. It's one API, one common network control plane and one common set of segmentation services for the network. That's problem number one. >> It's interesting I hear you talking and I hear one network among different operating models. Reminds me of the old serverless days. There's still servers but they call it serverless. Is there going to be a term network-less because at the end of the day it should be one network, not multiple operating models. This is a problem that you guys are working on, is that right? I'm just joking serverless and network-less, but the idea is it should be one thing. >> Yeah, effectively what we're trying to do is we're trying to recompose this fragmentation in terms of network cooperation across physical networking and server networking. Server networking is where the majority of the problems are because as much as you have standardized the ways of building physical networks and cloud fabrics with IP protocols and internet, you don't have that sort of operational efficiency at the server layer. And this is what we're trying to attack first with this technology. The second aspect we're trying to attack is how we distribute security services throughout the infrastructure more efficiently whether it's micro-segmentation is a stateful firewall services or even encryption. Those are all capabilities enabled by the BlueField DPU technology. And we can actually integrate those capabilities directly into the network fabric limiting dramatically at least for east west traffic the sprawl of security appliances whether virtual or physical. That is typically the way people today segment and secure the traffic in the cloud. >> Awesome. Pete, all kidding aside about network-less and serverless kind of fun play on words there, the network is one thing it's basically distributed computing, right? So I'd love to get your thoughts about this distributed security with zero trust as the driver for this architecture you guys are doing. Can you share in more detail the depth of why DPU based approach is better than alternatives? >> Yeah, I think what's beautiful and kind of what the DPU brings that's new to this model is completely isolated compute environment inside. So it's the, yo dog, I heard you like a server so I put a server inside your server. And so we provide ARM CPUs, memory and network accelerators inside and that is completely isolated from the host. The actual X86 host just thinks it has a regular niche in there, but you actually have this full control plane thing. It's just like taking your top of rack switch and shoving it inside of your compute node. And so you have not only this separation within the data plane, but you have this complete control plane separation so you have this element that the network team can now control and manage, but we're taking all of the functions we used to do at the top of rack switch and we're distributing them now. And as time has gone on we've struggled to put more and more and more into that network edge. And the reality is the network edge is the compute layer, not the top of rack switch layer. And so that provides this phenomenal enforcement point for security and policy. And I think outside of today's solutions around virtual firewalls, the other option is centralized appliances. And even if you can get one that can scale large enough, the question is, can you afford it? And so what we end up doing is we kind of hope that NVIDIA's good enough or we hope that the VXLAN tunnel's good enough. And we can't actually apply more advanced techniques there because we can't financially afford that appliance to see all of the traffic. And now that we have a distributed model with this accelerator, we could do it. >> So what's in it for the customer real quick and I think this is an interesting point you mentioned policy. Everyone in networking knows policy is just a great thing. And as you hear it being talked about up the stack as well when you start getting to orchestrating microservices and whatnot all that good stuff going on there, containers and whatnot and modern applications. What's the benefit to the customers with this approach because what I heard was more scale, more edge, deployment flexibility relative to security policies and application enablement? What's the customer get out of this architecture? What's the enablement? >> It comes down to taking again the capabilities that we're in that top of rack switch and distributing them down. So that makes simplicity smaller, blast radius' for failures smaller failure domains, maintenance on the networks and the systems become easier. Your ability to integrate across workloads becomes infinitely easier. And again, we always want to kind of separate each one of those layers so just as in say a VXLAN network, my leaf in spine don't have to be tightly coupled together. I can now do this at a different layer and so you can run a DPU with any networking in the core there. And so you get this extreme flexibility. You can start small, you can scale large. To me the possibilities are endless. >> It's a great security control plan. Really flexibility is key and also being situationally aware of any kind of threats or new vectors or whatever's happening in the network. Alessandro, this is huge upside, right? You've already identified some successes with some customers on your early field trials. What are they doing and why are they attracted to the solution? >> Yeah, I think the response from customer has been the most encouraging and exciting for us to sort of continue and work and develop this product. And we have actually learned a lot in the process. We talked to tier two, tier three cloud providers. We talked to SP, Soft Telco type of networks as well as inter large enterprise customers. In one particular case one, let me call out a couple of examples here just to give you a flavor. There is a cloud provider in Asia who is actually managing a cloud where they're offering services based on multiple hypervisors. They are native services based on Zen, but they also on ramp into the cloud workloads based on ESXi and KVM depending on what the customer picks from the menu. And they have the problem of now orchestrating through their orchestrate or integrating with Zen center, with vSphere, with OpenStack to coordinate this multiple environments. And in the process to provide security, they actually deploy virtual appliances everywhere which has a lot of cost complication and eats up into the server CPU. The promise that they saw in this technology, they call it actually game changing is actually to remove all this complexity, having a single network and distribute the micro segmentation service directly into the fabric. And overall they're hoping to get out it tremendous OPEX benefit and overall operational simplification for the cloud infrastructure. That's one important use case. Another global enterprise customer is running both ESXi and Hyper-V environment and they don't have a solution to do micro segmentation consistently across hypervisors. So again, micro segmentation is a huge driver security. Looks like it's a recurring theme talking to most of these customers. And in the Telco space, we're working with few Telco customers on the CFT program where the main goal is actually to harmonize network cooperation. They typically handle all the VNFs with their own homegrown DPDK stack. This is overly complex. It is frankly also slow and inefficient. And then they have a physical network to manage. The idea of having again one network to coordinate the provisioning of cloud services between the Telco VNFs and the rest of the infrastructure is extremely powerful on top of the offloading capability opted by the BlueField DPUs. Those are just some examples. >> That was a great use case. A lot more potential I see that with the unified cloud networking, great stuff, Pete, shout out to you 'cause at NVIDIA we've been following your success us for a long time and continuing to innovate as cloud scales and Pluribus with unified networking kind of bring it to the next level. Great stuff, great to have you guys on and again, software keeps driving the innovation and again, networking is just a part of it and it's the key solution. So I got to ask both of you to wrap this up. How can cloud operators who are interested in this new architecture and solution learn more because this is an architectural shift? People are working on this problem, they're try to think about multiple clouds, they're try to think about unification around the network and giving more security, more flexibility to their teams. How can people learn more? >> Yeah, so Alessandro and I have a talk at the upcoming NVIDIA GTC conference. So it's the week of March 21st through 24th. You can go and register for free nvidia.com/gtc. You can also watch recorded sessions if you end up watching this on YouTube a little bit after the fact. And we're going to dive a little bit more into the specifics and the details and what we're providing in the solution. >> Alessandro, how can we people learn more? >> Yeah, absolutely. People can go to the Pluribus website, www.pluribusnetworks.com/eft and they can fill up the form and they will contact Pluribus to either know more or to know more and actually to sign up for the actual early field trial program which starts at the end of April. >> Okay, well, we'll leave it there. Thank you both for joining, appreciate it. Up next you're going to hear an independent analyst perspective and review some of the research from the enterprise strategy group ESG. I'm John Furrier with theCUBE, thanks for watching. (upbeat music)

>>Everyone wants a cloud operating model. Since the introduction of the modern cloud. Last decade, the entire technology landscape has changed. We've learned a lot from the hyperscalers, especially from AWS. Now, one thing is certain in the technology business. It's so competitive. Then if a faster, better, cheaper idea comes along, the industry will move quickly to adopt it. They'll add their unique value and then they'll bring solutions to the market. And that's precisely what's happening throughout the technology industry because of cloud. And one of the best examples is Amazon's nitro. That's AWS has custom built hypervisor that delivers on the promise of more efficiently using resources and expanding things like processor, optionality for customers. It's a secret weapon for Amazon. As, as we, as we wrote last year, every infrastructure company needs something like nitro to compete. Why do we say this? Well, Wiki Bon our research arm estimates that nearly 30% of CPU cores in the data center are wasted. >>They're doing work that they weren't designed to do well, specifically offloading networking, storage, and security tasks. So if you can eliminate that waste, you can recapture dollars that drop right to the bottom line. That's why every company needs a nitro like solution. As a result of these developments, customers are rethinking networks and how they utilize precious compute resources. They can't, or won't put everything into the public cloud for many reasons. That's one of the tailwinds for tier two cloud service providers and why they're growing so fast. They give options to customers that don't want to keep investing in building out their own data centers, and they don't want to migrate all their workloads to the public cloud. So these providers and on-prem customers, they want to be more like hyperscalers, right? They want to be more agile and they do that. They're distributing, networking and security functions and pushing them closer to the applications. >>Now, at the same time, they're unifying their view of the network. So it can be less fragmented, manage more efficiently with more automation and better visibility. How are they doing this? Well, that's what we're going to talk about today. Welcome to changing the game for cloud networking made possible by pluribus networks. My name is Dave Vellante and today on this special cube presentation, John furrier, and I are going to explore these issues in detail. We'll dig into new solutions being created by pluribus and Nvidia to specifically address offloading, wasted resources, accelerating performance, isolating data, and making networks more secure all while unifying the network experience. We're going to start on the west coast and our Palo Alto studios, where John will talk to Mike of pluribus and AMI, but Donnie of Nvidia, then we'll bring on Alessandra Bobby airy of pluribus and Pete Lummus from Nvidia to take a deeper dive into the technology. And then we're gonna bring it back here to our east coast studio and get the independent analyst perspective from Bob Liberte of the enterprise strategy group. We hope you enjoy the program. Okay, let's do this over to John >>Okay. Let's kick things off. We're here at my cafe. One of the TMO and pluribus networks and NAMI by Dani VP of networking, marketing, and developer ecosystem at Nvidia. Great to have you welcome folks. >>Thank you. Thanks. >>So let's get into the, the problem situation with cloud unified network. What problems are out there? What challenges do cloud operators have Mike let's get into it. >>Yeah, it really, you know, the challenges we're looking at are for non hyperscalers that's enterprises, governments, um, tier two service providers, cloud service providers, and the first mandate for them is to become as agile as a hyperscaler. So they need to be able to deploy services and security policies. And second, they need to be able to abstract the complexity of the network and define things in software while it's accelerated in hardware. Um, really ultimately they need a single operating model everywhere. And then the second thing is they need to distribute networking and security services out to the edge of the host. Um, we're seeing a growth in cyber attacks. Um, it's, it's not slowing down. It's only getting worse and, you know, solving for this security problem across clouds is absolutely critical. And the way to do it is to move security out to the host. >>Okay. With that goal in mind, what's the pluribus vision. How does this tie together? >>Yeah. So, um, basically what we see is, uh, that this demands a new architecture and that new architecture has four tenants. The first tenant is unified and simplified cloud networks. If you look at cloud networks today, there's, there's sort of like discreet bespoke cloud networks, you know, per hypervisor, per private cloud edge cloud public cloud. Each of the public clouds have different networks that needs to be unified. You know, if we want these folks to be able to be agile, they need to be able to issue a single command or instantiate a security policy across all those locations with one command and not have to go to each one. The second is like I mentioned, distributed security, um, distributed security without compromise, extended out to the host is absolutely critical. So micro-segmentation and distributed firewalls, but it doesn't stop there. They also need pervasive visibility. >>You know, it's, it's, it's sort of like with security, you really can't see you can't protect what you can't see. So you need visibility everywhere. The problem is visibility to date has been very expensive. Folks have had to basically build a separate overlay network of taps, packet brokers, tap aggregation infrastructure that really needs to be built into this unified network I'm talking about. And the last thing is automation. All of this needs to be SDN enabled. So this is related to my comment about abstraction abstract, the complexity of all of these discreet networks, physic whatever's down there in the physical layer. Yeah. I don't want to see it. I want to abstract it. I wanted to find things in software, but I do want to leverage the power of hardware to accelerate that. So that's the fourth tenant is SDN automation. >>Mike, we've been talking on the cube a lot about this architectural shift and customers are looking at this. This is a big part of everyone who's looking at cloud operations next gen, how do we get there? How do customers get this vision realized? >>That's a great question. And I appreciate the tee up. I mean, we're, we're here today for that reason. We're introducing two things today. Um, the first is a unified cloud networking vision, and that is a vision of where pluribus is headed with our partners like Nvidia longterm. Um, and that is about, uh, deploying a common operating model, SDN enabled SDN, automated hardware, accelerated across all clouds. Um, and whether that's underlying overlay switch or server, um, hype, any hypervisor infrastructure containers, any workload doesn't matter. So that's ultimately where we want to get. And that's what we talked about earlier. Um, the first step in that vision is what we call the unified cloud fabric. And this is the next generation of our adaptive cloud fabric. Um, and what's nice about this is we're not starting from scratch. We have a, a, an award-winning adaptive cloud fabric product that is deployed globally. Um, and in particular, uh, we're very proud of the fact that it's deployed in over a hundred tier one mobile operators as the network fabric for their 4g and 5g virtualized cores. We know how to build carrier grade, uh, networking infrastructure, what we're doing now, um, to realize this next generation unified cloud fabric is we're extending from the switch to this Nvidia Bluefield to DPU. We know there's a, >>Hold that up real quick. That's a good, that's a good prop. That's the blue field and video. >>It's the Nvidia Bluefield two DPU data processing unit. And, um, uh, you know, what we're doing, uh, fundamentally is extending our SDN automated fabric, the unified cloud fabric out to the host, but it does take processing power. So we knew that we didn't want to do, we didn't want to implement that running on the CPU, which is what some other companies do because it consumes revenue generating CPU's from the application. So a DPU is a perfect way to implement this. And we knew that Nvidia was the leader with this blue field too. And so that is the first that's, that's the first step in the getting into realizing this vision. >>I mean, Nvidia has always been powering some great workloads of GPU. Now you've got DPU networking and then video is here. What is the relationship with clothes? How did that come together? Tell us the story. >>Yeah. So, you know, we've been working with pluribus for quite some time. I think the last several months was really when it came to fruition and, uh, what pluribus is trying to build and what Nvidia has. So we have, you know, this concept of a Bluefield data processing unit, which if you think about it, conceptually does really three things, offload, accelerate an isolate. So offload your workloads from your CPU to your data processing unit infrastructure workloads that is, uh, accelerate. So there's a bunch of acceleration engines. So you can run infrastructure workloads much faster than you would otherwise, and then isolation. So you have this nice security isolation between the data processing unit and your other CPU environment. And so you can run completely isolated workloads directly on the data processing unit. So we introduced this, you know, a couple of years ago, and with pluribus, you know, we've been talking to the pluribus team for quite some months now. >>And I think really the combination of what pluribus is trying to build and what they've developed around this unified cloud fabric, uh, is fits really nicely with the DPU and running that on the DPU and extending it really from your physical switch, all the way to your host environment, specifically on the data processing unit. So if you think about what's happening as you add data processing units to your environment. So every server we believe over time is going to have data processing units. So now you'll have to manage that complexity from the physical network layer to the host layer. And so what pluribus is really trying to do is extending the network fabric from the host, from the switch to the host, and really have that single pane of glass for network operators to be able to configure provision, manage all of the complexity of the network environment. >>So that's really how the partnership truly started. And so it started really with extending the network fabric, and now we're also working with them on security. So, you know, if you sort of take that concept of isolation and security isolation, what pluribus has within their fabric is the concept of micro-segmentation. And so now you can take that extended to the data processing unit and really have, um, isolated micro-segmentation workloads, whether it's bare metal cloud native environments, whether it's virtualized environments, whether it's public cloud, private cloud hybrid cloud. So it really is a magical partnership between the two companies with their unified cloud fabric running on, on the DPU. >>You know, what I love about this conversation is it reminds me of when you have these changing markets, the product gets pulled out of the market and, and you guys step up and create these new solutions. And I think this is a great example. So I have to ask you, how do you guys differentiate what sets this apart for customers with what's in it for the customer? >>Yeah. So I mentioned, you know, three things in terms of the value of what the Bluefield brings, right? There's offloading, accelerating, isolating, that's sort of the key core tenants of Bluefield. Um, so that, you know, if you sort of think about what, um, what Bluefields, what we've done, you know, in terms of the differentiation, we're really a robust platform for innovation. So we introduced Bluefield to, uh, last year, we're introducing Bluefield three, which is our next generation of Bluefields, you know, we'll have five X, the arm compute capacity. It will have 400 gig line rate acceleration, four X better crypto acceleration. So it will be remarkably better than the previous generation. And we'll continue to innovate and add, uh, chips to our portfolio every, every 18 months to two years. Um, so that's sort of one of the key areas of differentiation. The other is the, if you look at Nvidia and, and you know, what we're sort of known for is really known for our AI artificial intelligence and our artificial intelligence software, as well as our GPU. >>So you look at artificial intelligence and the combination of artificial intelligence plus data processing. This really creates the, you know, faster, more efficient, secure AI systems from the core of your data center, all the way out to the edge. And so with Nvidia, we really have these converged accelerators where we've combined the GPU, which does all your AI processing with your data processing with the DPU. So we have this convergence really nice convergence of that area. And I would say the third area is really around our developer environment. So, you know, one of the key, one of our key motivations at Nvidia is really to have our partner ecosystem, embrace our technology and build solutions around our technology. So if you look at what we've done with the DPU, with credit and an SDK, which is an open SDK called Doka, and it's an open SDK for our partners to really build and develop solutions using Bluefield and using all these accelerated libraries that we expose through Doka. And so part of our differentiation is really building this open ecosystem for our partners to take advantage and build solutions around our technology. >>You know, what's exciting is when I hear you talk, it's like you realize that there's no one general purpose network anymore. Everyone has their own super environment Supercloud or these new capabilities. They can really craft their own, I'd say, custom environment at scale with easy tools. Right. And it's all kind of, again, this is the new architecture Mike, you were talking about, how does customers run this effectively? Cost-effectively and how do people migrate? >>Yeah, I, I think that is the key question, right? So we've got this beautiful architecture. You, you know, Amazon nitro is a, is a good example of, of a smart NIC architecture that has been successfully deployed, but enterprises and serve tier two service providers and tier one service providers and governments are not Amazon, right? So they need to migrate there and they need this architecture to be cost-effective. And, and that's, that's super key. I mean, the reality is deep user moving fast, but they're not going to be, um, deployed everywhere on day one. Some servers will have DPS right away, some servers will have use and a year or two. And then there are devices that may never have DPS, right. IOT gateways, or legacy servers, even mainframes. Um, so that's the beauty of a solution that creates a fabric across both the switch and the DPU, right. >>Um, and by leveraging the Nvidia Bluefield DPU, what we really like about it is it's open. Um, and that drives, uh, cost efficiencies. And then, um, uh, you know, with this, with this, our architectural approach effectively, you get a unified solution across switch and DPU workload independent doesn't matter what hypervisor it is, integrated visibility, integrated security, and that can, uh, create tremendous cost efficiencies and, and really extract a lot of the expense from, from a capital perspective out of the network, as well as from an operational perspective, because now I have an SDN automated solution where I'm literally issuing a command to deploy a network service or to create or deploy our security policy and is deployed everywhere, automatically saving the oppor, the network operations team and the security operations team time. >>All right. So let me rewind that because that's super important. Get the unified cloud architecture, I'm the customer guy, but it's implemented, what's the value again, take, take me through the value to me. I have a unified environment. What's the value. >>Yeah. So I mean, the value is effectively, um, that, so there's a few pieces of value. The first piece of value is, um, I'm creating this clean D mark. I'm taking networking to the host. And like I mentioned, we're not running it on the CPU. So in implementations that run networking on the CPU, there's some conflict between the dev ops team who owned the server and the NetApps team who own the network because they're installing software on the, on the CPU stealing cycles from what should be revenue generating. Uh CPU's. So now by, by terminating the networking on the DPU, we click create this real clean DMARC. So the dev ops folks are happy because they don't necessarily have the skills to manage network and they don't necessarily want to spend the time managing networking. They've got their network counterparts who are also happy the NetApps team, because they want to control the networking. >>And now we've got this clean DMARC where the DevOps folks get the services they need and the NetApp folks get the control and agility they need. So that's a huge value. Um, the next piece of value is distributed security. This is essential. I mentioned earlier, you know, put pushing out micro-segmentation and distributed firewall, basically at the application level, right, where I create these small, small segments on an by application basis. So if a bad actor does penetrate the perimeter firewall, they're contained once they get inside. Cause the worst thing is a bad actor, penetrates a perimeter firewall and can go wherever they want and wreak havoc. Right? And so that's why this, this is so essential. Um, and the next benefit obviously is this unified networking operating model, right? Having, uh, uh, uh, an operating model across switch and server underlay and overlay, workload agnostic, making the life of the NetApps teams much easier so they can focus their time on really strategy instead of spending an afternoon, deploying a single villain, for example. >>Awesome. And I think also from my standpoint, I mean, perimeter security is pretty much, I mean, they're out there, it gets the firewall still out there exists, but pretty much they're being breached all the time, the perimeter. So you have to have this new security model. And I think the other thing that you mentioned, the separation between dev ops is cool because the infrastructure is code is about making the developers be agile and build security in from day one. So this policy aspect is, is huge. Um, new control points. I think you guys have a new architecture that enables the security to be handled more flexible. >>Right. >>That seems to be the killer feature here, >>Right? Yeah. If you look at the data processing unit, I think one of the great things about sort of this new architecture, it's really the foundation for zero trust it's. So like you talked about the perimeter is getting breached. And so now each and every compute node has to be protected. And I think that's sort of what you see with the partnership between pluribus and Nvidia is the DPU is really the foundation of zero trust. And pluribus is really building on that vision with, uh, allowing sort of micro-segmentation and being able to protect each and every compute node as well as the underlying network. >>This is super exciting. This is an illustration of how the market's evolving architectures are being reshaped and refactored for cloud scale and all this new goodness with data. So I gotta ask how you guys go into market together. Michael, start with you. What's the relationship look like in the go to market with an Nvidia? >>Sure. Um, I mean, we're, you know, we're super excited about the partnership, obviously we're here together. Um, we think we've got a really good solution for the market, so we're jointly marketing it. Um, uh, you know, obviously we appreciate that Nvidia is open. Um, that's, that's sort of in our DNA, we're about open networking. They've got other ISV who are gonna run on Bluefield too. We're probably going to run on other DPS in the, in the future, but right now, um, we're, we feel like we're partnered with the number one, uh, provider of DPS in the world and, uh, super excited about, uh, making a splash with it. >>I'm in get the hot product. >>Yeah. So Bluefield too, as I mentioned was GA last year, we're introducing, uh, well, we now also have the converged accelerator. So I talked about artificial intelligence or artificial intelligence with the Bluefield DPU, all of that put together on a converged accelerator. The nice thing there is you can either run those workloads. So if you have an artificial intelligence workload and an infrastructure workload, you can warn them separately on the same platform or you can actually use, uh, you can actually run artificial intelligence applications on the Bluefield itself. So that's what the converged accelerator really brings to the table. Uh, so that's available now. Then we have Bluefield three, which will be available late this year. And I talked about sort of, you know, uh, how much better that next generation of Bluefield is in comparison to Bluefield two. So we will see Bluefield three shipping later on this year, and then our software stack, which I talked about, which is called Doka we're on our second version are Doka one dot two. >>We're releasing Doka one dot three, uh, in about two months from now. And so that's really our open ecosystem framework. So allow you to program the Bluefields. So we have all of our acceleration libraries, um, security libraries, that's all packed into this STK called Doka. And it really gives that simplicity to our partners to be able to develop on top of Bluefield. So as we add new generations of Bluefield, you know, next, next year, we'll have, you know, another version and so on and so forth Doka is really that unified unified layer that allows, um, Bluefield to be both forwards compatible and backwards compatible. So partners only really have to think about writing to that SDK once, and then it automatically works with future generations of Bluefields. So that's sort of the nice thing around, um, around Doka. And then in terms of our go to market model, we're working with every, every major OEM. So, uh, later on this year, you'll see, you know, major server manufacturers, uh, releasing Bluefield enabled servers. So, um, more to come >>Awesome, save money, make it easier, more capabilities, more workload power. This is the future of, of cloud operations. >>Yeah. And, and, and, uh, one thing I'll add is, um, we are, um, we have a number of customers as you'll hear in the next segment, um, that are already signed up and we'll be working with us for our, uh, early field trial starting late April early may. Um, we are accepting registrations. You can go to www.pluribusnetworks.com/e F T a. If you're interested in signing up for, um, uh, being part of our field trial and providing feedback on the product, >>Awesome innovation and network. Thanks so much for sharing the news. Really appreciate it. Thanks so much. Okay. In a moment, we'll be back to look deeper in the product, the integration security zero trust use cases. You're watching the cube, the leader in enterprise tech coverage, >>Cloud networking is complex and fragmented slowing down your business. How can you simplify and unify your cloud networks to increase agility and business velocity? >>Pluribus unified cloud networking provides a unified simplify and agile network fabric across all clouds. It brings the simplicity of a public cloud operation model to private clouds, dramatically reducing complexity and improving agility, availability, and security. Now enterprises and service providers can increase their business philosophy and delight customers in the distributed multi-cloud era. We achieve this with a new approach to cloud networking, pluribus unified cloud fabric. This open vendor, independent network fabric, unifies, networking, and security across distributed clouds. The first step is extending the fabric to servers equipped with data processing units, unifying the fabric across switches and servers, and it doesn't stop there. The fabric is unified across underlay and overlay networks and across all workloads and virtualization environments. The unified cloud fabric is optimized for seamless migration to this new distributed architecture, leveraging the power of the DPU for application level micro-segmentation distributed fireball and encryption while still supporting those servers and devices that are not equipped with a DPU. Ultimately the unified cloud fabric extends seamlessly across distributed clouds, including central regional at edge private clouds and public clouds. The unified cloud fabric is a comprehensive network solution. That includes everything you need for clouds, networking built in SDN automation, distributed security without compromises, pervasive wire speed, visibility and application insight available on your choice of open networking switches and DP use all at the lowest total cost of ownership. The end result is a dramatically simplified unified cloud networking architecture that unifies your distributed clouds and frees your business to move at cloud speed, >>To learn more, visit www.pluribusnetworks.com. >>Okay. We're back I'm John ferry with the cube, and we're going to go deeper into a deep dive into unified cloud networking solution from Clovis and Nvidia. And we'll examine some of the use cases with Alessandra Burberry, VP of product management and pullovers networks and Pete Bloomberg who's director of technical marketing and video remotely guys. Thanks for coming on. Appreciate it. >>Yeah. >>So deep dive, let's get into the what and how Alexandra we heard earlier about the pluribus Nvidia partnership and the solution you're working together on what is it? >>Yeah. First let's talk about the water. What are we really integrating with the Nvidia Bluefield, the DPO technology, uh, plugable says, um, uh, there's been shipping, uh, in, uh, in volume, uh, in multiple mission critical networks. So this advisor one network operating systems, it runs today on a merchant silicone switches and effectively it's a standard open network operating system for data center. Um, and the novelty about this system that integrates a distributed control plane for, at water made effective in SDN overlay. This automation is a completely open and interoperable and extensible to other type of clouds is not enclosed them. And this is actually what we're now porting to the Nvidia DPO. >>Awesome. So how does it integrate into Nvidia hardware and specifically how has pluribus integrating its software with the Nvidia hardware? >>Yeah, I think, uh, we leverage some of the interesting properties of the Bluefield, the DPO hardware, which allows actually to integrate, uh, um, uh, our software, our network operating system in a manner which is completely isolated and independent from the guest operating system. So the first byproduct of this approach is that whatever we do at the network level on the DPU card that is completely agnostic to the hypervisor layer or OSTP layer running on, uh, on the host even more, um, uh, we can also independently manage this network, know that the switch on a Neek effectively, um, uh, managed completely independently from the host. You don't have to go through the network operating system, running on x86 to control this network node. So you throw yet the experience effectively of a top of rack for virtual machine or a top of rack for, uh, Kubernetes bots, where instead of, uh, um, if you allow me with the analogy instead of connecting a server knee directly to a switchboard, now you're connecting a VM virtual interface to a virtual interface on the switch on an ache. >>And, uh, also as part of this integration, we, uh, put a lot of effort, a lot of emphasis in, uh, accelerating the entire, uh, data plane for networking and security. So we are taking advantage of the DACA, uh, Nvidia DACA API to program the accelerators. And these accomplished two things with that. Number one, uh, you, uh, have much greater performance, much better performance. They're running the same network services on an x86 CPU. And second, this gives you the ability to free up, I would say around 20, 25% of the server capacity to be devoted either to, uh, additional workloads to run your cloud applications, or perhaps you can actually shrink the power footprint and compute footprint of your data center by 20%, if you want to run the same number of compute workloads. So great efficiencies in the overall approach, >>And this is completely independent of the server CPU, right? >>Absolutely. There is zero code from running on the x86, and this is what we think this enables a very clean demarcation between computer and network. >>So Pete, I gotta get, I gotta get you in here. We heard that, uh, the DPU is enabled cleaner separation of dev ops and net ops. Can you explain why that's important because everyone's talking DevSecOps right now, you've got net ops, net, net sec ops, this separation. Why is this clean separation important? >>Yeah, I think it's a, you know, it's a pragmatic solution in my opinion. Um, you know, we wish the world was all kind of rainbows and unicorns, but it's a little, a little messier than that. And I think a lot of the dev ops stuff and that, uh, mentality and philosophy, there's a natural fit there. Right? You have applications running on servers. So you're talking about developers with those applications integrating with the operators of those servers. Well, the network has always been this other thing and the network operators have always had a very different approach to things than compute operators. And, you know, I think that we, we in the networking industry have gotten closer together, but there's still a gap there's still some distance. And I think in that distance, isn't going to be closed. And so, you know, again, it comes down to pragmatism and I think, you know, one of my favorite phrases is look good fences, make good neighbors. And that's what this is. >>Yeah. That's a great point because dev ops has become kind of the calling card for cloud, right. But dev ops is as simply infrastructure as code and infrastructure is networking, right? So if infrastructure is code, you know, you're talking about, you know, that part of the stack under the covers under the hood, if you will, this is super important distinction. And this is where the innovation is. Can you elaborate on how you see that? Because this is really where the action is right now. >>Yeah, exactly. And I think that's where, um, one from, from the policy, the security that the zero trust aspect of this, right? If you get it wrong on that network side, all of a sudden you, you can totally open up that those capabilities. And so security is part of that. But the other part is thinking about this at scale, right? So we're taking one top of rack switch and adding, you know, up to 48 servers per rack. And so that ability to automate, orchestrate and manage at scale becomes absolutely critical. >>I'll Sandra, this is really the why we're talking about here, and this is scale. And again, getting it right. If you don't get it right, you're going to be really kind of up, you know what you know, so this is a huge deal. Networking matters, security matters, automation matters, dev ops, net ops, all coming together, clean separation, um, help us understand how this joint solution with Nvidia fits into the pluribus unified cloud networking vision, because this is what people are talking about and working on right now. >>Yeah, absolutely. So I think here with this solution, we're attacking two major problems in cloud networking. One is, uh, operation of, uh, cloud networking. And the second is a distributing security services in the cloud infrastructure. First, let me talk about the first water. We really unifying. If we're unifying something, something must be at least fragmented or this jointed and the, what is this joint that is actually the network in the cloud. If you look holistically, how networking is deployed in the cloud, you have your physical fabric infrastructure, right? Your switches and routers, you'll build your IP clause fabric leaf in spine typologies. This is actually a well understood the problem. I, I would say, um, there are multiple vendors, uh, uh, with, uh, um, uh, let's say similar technologies, um, very well standardized, whether you will understood, um, and almost a commodity, I would say building an IP fabric these days, but this is not the place where you deploy most of your services in the cloud, particularly from a security standpoint, two services are actually now moved into the compute layer where you actually were called builders, have to instrument the, a separate, uh, network virtualization layer, where they deploy segmentation and security closer to the workloads. >>And this is where the complication arise. These high value part of the cloud network is where you have a plethora of options that they don't talk to each other. And they are very dependent on the kind of hypervisor or compute solution you choose. Um, for example, the networking API to be between an GSXI environment or an hyper V or a Zen are completely disjointed. You have multiple orchestration layers. And when, and then when you throw in also Kubernetes in this, in this, in this type of architecture, uh, you're introducing yet another level of networking. And when Kubernetes runs on top of VMs, which is a prevalent approach, you actually just stacking up multiple networks on the compute layer that they eventually run on the physical fabric infrastructure. Those are all ships in the nights effectively, right? They operate as completely disjointed. And we're trying to attack this problem first with the notion of a unified fabric, which is independent from any workloads, whether it's this fabric spans on a switch, which can be con connected to a bare metal workload, or can span all the way inside the DPU, uh, where, um, you have, uh, your multi hypervisor compute environment. >>It's one API, one common network control plane, and one common set of segmentation services for the network. That's probably the number one, >>You know, it's interesting you, man, I hear you talking, I hear one network month, different operating models reminds me of the old serverless days. You know, there's still servers, but they call it serverless. Is there going to be a term network list? Because at the end of the day, it should be one network, not multiple operating models. This, this is a problem that you guys are working on. Is that right? I mean, I'm not, I'm just joking server listen network list, but the idea is it should be one thing. >>Yeah, it's effectively. What we're trying to do is we are trying to recompose this fragmentation in terms of network operation, across physical networking and server networking server networking is where the majority of the problems are because of the, uh, as much as you have standardized the ways of building, uh, physical networks and cloud fabrics with IP protocols and internet, you don't have that kind of, uh, uh, sort of, uh, um, um, uh, operational efficiency, uh, at the server layer. And, uh, this is what we're trying to attack first. The, with this technology, the second aspect we're trying to attack is are we distribute the security services throughout the infrastructure, more efficiently, whether it's micro-segmentation is a stateful firewall services, or even encryption. Those are all capabilities enabled by the blue field, uh, uh, the Butte technology and, uh, uh, we can actually integrate those capabilities directly into the nettle Fabrica, uh, limiting dramatically, at least for east-west traffic, the sprawl of, uh, security appliances, whether virtual or physical, that is typically the way the people today, uh, segment and secure the traffic in the cloud. >>Awesome. Pete, all kidding aside about network lists and serverless kind of fun, fun play on words there, the network is one thing it's basically distributed computing, right? So I love to get your thoughts about this distributed security with zero trust as the driver for this architecture you guys are doing. Can you share in more detail the depth of why DPU based approach is better than alternatives? >>Yeah, I think what's, what's beautiful and kind of what the DPU brings. That's new to this model is a completely isolated compute environment inside. So, you know, it's the, uh, yo dog, I heard you like a server, so I put a server inside your server. Uh, and so we provide, uh, you know, armed CPU's memory and network accelerators inside, and that is completely isolated from the host. So the server, the, the actual x86 host just thinks it has a regular Nick in there, but you actually have this full control plane thing. It's just like taking your top of rack switch and shoving it inside of your compute node. And so you have not only the separation, um, within the data plane, but you have this complete control plane separation. So you have this element that the network team can now control and manage, but we're taking all of the functions we used to do at the top of rack switch, and we're just shooting them now. >>And, you know, as time has gone on we've, we've struggled to put more and more and more into that network edge. And the reality is the network edge is the compute layer, not the top of rack switch layer. And so that provides this phenomenal enforcement point for security and policy. And I think outside of today's solutions around virtual firewalls, um, the other option is centralized appliances. And even if you can get one that can scale large enough, the question is, can you afford it? And so what we end up doing is we kind of hope that of aliens good enough, or we hope that if the excellent tunnel is good enough and we can actually apply more advanced techniques there because we can't physically, you know, financially afford that appliance to see all of the traffic. And now that we have a distributed model with this accelerator, we could do it. >>So what's the what's in it for the customer. I real quick, cause I think this is interesting point. You mentioned policy, everyone in networking knows policy is just a great thing and it adds, you hear it being talked about up the stack as well. When you start getting to orchestrating microservices and whatnot, all that good stuff going on there, containers and whatnot and modern applications. What's the benefit to the customers with this approach? Because what I heard was more scale, more edge deployment, flexibility, relative to security policies and application enablement. I mean, is that what what's the customer get out of this architecture? What's the enablement. >>It comes down to, uh, taking again the capabilities that were in that top of rack switch and asserting them down. So that makes simplicity smaller blast radiuses for failure, smaller failure domains, maintenance on the networks, and the systems become easier. Your ability to integrate across workloads becomes infinitely easier. Um, and again, you know, we always want to kind of separate each one of those layers. So just as in say, a VX land network, my leaf and spine don't have to be tightly coupled together. I can now do this at a different layer. And so you can run a DPU with any networking in the core there. And so you get this extreme flexibility. You can start small, you can scale large. Um, you know, to me, the, the possibilities are endless. Yes, >>It's a great security control plan. Really flexibility is key. And, and also being situationally aware of any kind of threats or new vectors or whatever's happening in the network. Alessandra, this is huge upside, right? You've already identified some successes with some customers on your early field trials. What are they doing and why are they attracted to the solution? >>Yeah, I think the response from customers has been, uh, the most, uh, encouraging and, uh, exciting, uh, for, uh, for us to, uh, to sort of continue and work and develop this product. And we have actually learned a lot in the process. Um, we talked to tier two tier three cloud providers. Uh, we talked to, uh, SP um, software Tyco type of networks, uh, as well as a large enterprise customers, um, in, uh, one particular case. Um, uh, one, uh, I think, um, let me, let me call out a couple of examples here, just to give you a flavor. Uh, there is a service provider, a cloud provider, uh, in Asia who is actually managing a cloud, uh, where they are offering services based on multiple hypervisors. They are native services based on Zen, but they also are on ramp into the cloud, uh, workloads based on, uh, ESI and, uh, uh, and KVM, depending on what the customer picks from the piece on the menu. >>And they have the problem of now orchestrating through their orchestrate or integrating with the Zen center with vSphere, uh, with, uh, open stack to coordinate these multiple environments and in the process to provide security, they actually deploy virtual appliances everywhere, which has a lot of costs, complication, and eats up into the server CPU. The problem is that they saw in this technology, they call it actually game changing is actually to remove all this complexity of in a single network and distribute the micro-segmentation service directly into the fabric. And overall, they're hoping to get out of it, uh, uh, tremendous, uh, um, opics, uh, benefit and overall, um, uh, operational simplification for the cloud infrastructure. That's one potent a use case. Uh, another, uh, large enterprise customer global enterprise customer, uh, is running, uh, both ESI and hyper V in that environment. And they don't have a solution to do micro-segmentation consistently across hypervisors. >>So again, micro-segmentation is a huge driver security looks like it's a recurring theme, uh, talking to most of these customers and in the Tyco space, um, uh, we're working with a few types of customers on the CFT program, uh, where the main goal is actually to our Monet's network operation. They typically handle all the VNF search with their own homegrown DPDK stack. This is overly complex. It is frankly also as low and inefficient, and then they have a physical network to manage the, the idea of having again, one network, uh, to coordinate the provision in our cloud services between the, the take of VNF, uh, and, uh, the rest of the infrastructure, uh, is extremely powerful on top of the offloading capability of the, by the bluefin DPOs. Those are just some examples. >>That was a great use case, a lot more potential. I see that with the unified cloud networking, great stuff, feed, shout out to you guys at Nvidia had been following your success for a long time and continuing to innovate as cloud scales and pluribus here with the unified networking, kind of bring it to the next level. Great stuff. Great to have you guys on. And again, software keeps driving the innovation again, networking is just a part of it, and it's the key solution. So I got to ask both of you to wrap this up. How can cloud operators who are interested in, in this, uh, new architecture and solution, uh, learn more because this is an architectural shift. People are working on this problem. They're trying to think about multiple clouds of trying to think about unification around the network and giving more security, more flexibility, uh, to their teams. How can people learn more? >>Yeah, so, uh, all Sandra and I have a talk at the upcoming Nvidia GTC conference. Um, so that's the week of March 21st through 24th. Um, you can go and register for free and video.com/at GTC. Um, you can also watch recorded sessions if you ended up watching us on YouTube a little bit after the fact. Um, and we're going to dive a little bit more into the specifics and the details and what we're providing in the solution. >>Alexandra, how can people learn more? >>Yeah, absolutely. People can go to the pluribus, a website, www boost networks.com/eft, and they can fill up the form and, uh, they will contact durables to either know more or to know more and actually to sign up for the actual early field trial program, which starts at the end of April. >>Okay. Well, we'll leave it there. Thanks. You both for joining. Appreciate it up next. You're going to hear an independent analyst perspective and review some of the research from the enterprise strategy group ESG. I'm John ferry with the >>Cube. Thanks for watching. >>Okay. We've heard from the folks at networks and Nvidia about their effort to transform cloud networking and unify bespoke infrastructure. Now let's get the perspective from an independent analyst and to do so. We welcome in ESG, senior analysts, Bob LA Liberte, Bob. Good to see you. Thanks for coming into our east coast studios. >>Oh, thanks for having me. It's great to be >>Here. Yeah. So this, this idea of unified cloud networking approach, how serious is it? What's what's driving it. >>Yeah, there's certainly a lot of drivers behind it, but probably the first and foremost is the fact that application environments are becoming a lot more distributed, right? So the, it pendulum tends to swing back and forth. And we're definitely on one that's swinging from consolidated to distributed. And so applications are being deployed in multiple private data centers, multiple public cloud locations, edge locations. And as a result of that, what you're seeing is a lot of complexity. So organizations are having to deal with this highly disparate environment. They have to secure it. They have to ensure connectivity to it and all that's driving up complexity. In fact, when we asked in one of our last surveys and last year about network complexity, more than half 54% came out and said, Hey, our network environment is now either more or significantly more complex than it used to be. >>And as a result of that, what you're seeing is it's really impacting agility. So everyone's moving to these modern application environments, distributing them across areas so they can improve agility yet it's creating more complexity. So a little bit counter to the fact and, you know, really counter to their overarching digital transformation initiatives. From what we've seen, you know, nine out of 10 organizations today are either beginning in process or have a mature digital transformation process or initiative, but their top goals, when you look at them, it probably shouldn't be a surprise. The number one goal is driving operational efficiency. So it makes sense. I've distributed my environment to create agility, but I've created a lot of complexity. So now I need these tools that are going to help me drive operational efficiency, drive better experience. >>I mean, I love how you bring in the data yesterday. Does a great job with that. Uh, questions is, is it about just unifying existing networks or is there sort of a need to rethink kind of a do-over network, how networks are built? >>Yeah, that's a, that's a really good point because certainly unifying networks helps right. Driving any kind of operational efficiency helps. But in this particular case, because we've made the transition to new application architectures and the impact that's having as well, it's really about changing and bringing in new frameworks and new network architectures to accommodate those new application architectures. And by that, what I'm talking about is the fact that these new modern application architectures, microservices, containers are driving a lot more east west traffic. So in the old days, it used to be easier in north south coming out of the server, one application per server, things like that. Right now you've got hundreds, if not thousands of microservices communicating with each other users communicating to them. So there's a lot more traffic and a lot of it's taking place within the servers themselves. The other issue that you starting to see as well from that security perspective, when we were all consolidated, we had those perimeter based legacy, you know, castle and moat security architectures, but that doesn't work anymore when the applications aren't in the castle, right. >>When everything's spread out that that no longer happens. So we're absolutely seeing, um, organizations trying to, trying to make a shift. And, and I think much, like if you think about the shift that we're seeing with all the remote workers and the sassy framework to enable a secure framework there, this it's almost the same thing. We're seeing this distributed services framework come up to support the applications better within the data centers, within the cloud data centers, so that you can drive that security closer to those applications and make sure they're, they're fully protected. Uh, and that's really driving a lot of the, you know, the zero trust stuff you hear, right? So never trust, always verify, making sure that everything is, is, is really secure micro-segmentation is another big area. So ensuring that these applications, when they're connected to each other, they're, they're fully segmented out. And that's again, because if someone does get a breach, if they are in your data center, you want to limit the blast radius, you want to limit the amount of damage that's done. So that by doing that, it really makes it a lot harder for them to see everything that's in there. >>You know, you mentioned zero trust. It used to be a buzzword, and now it's like become a mandate. And I love the mode analogy. You know, you build a moat to protect the queen and the castle, the Queens left the castles, it's just distributed. So how should we think about this, this pluribus and Nvidia solution. There's a spectrum, help us understand that you've got appliances, you've got pure software solutions. You've got what pluribus is doing with Nvidia, help us understand that. >>Yeah, absolutely. I think as organizations recognize the need to distribute their services to closer to the applications, they're trying different models. So from a legacy approach, you know, from a security perspective, they've got these centralized firewalls that they're deploying within their data centers. The hard part for that is if you want all this traffic to be secured, you're actually sending it out of the server up through the rack, usually to in different location in the data center and back. So with the need for agility, with the need for performance, right, that adds a lot of latency. Plus when you start needing to scale, that means adding more and more network connections, more and more appliances. So it can get very costly as well as impacting the performance. The other way that organizations are seeking to solve this problem is by taking the software itself and deploying it on the servers. Okay. So that's a, it's a great approach, right? It brings it really close to the applications, but the things you start running into there, there's a couple of things. One is that you start seeing that the DevOps team start taking on that networking and security responsibility, which they >>Don't want to >>Do, they don't want to do right. And the operations teams loses a little bit of visibility into that. Um, plus when you load the software onto the server, you're taking up precious CPU cycles. So if you're really wanting your applications to perform at an optimized state, having additional software on there, isn't going to, isn't going to do it. So, you know, when we think about all those types of things, right, and certainly the other side effects of that is the impact of the performance, but there's also a cost. So if you have to buy more servers because your CPU's are being utilized, right, and you have hundreds or thousands of servers, right, those costs are going to add up. So what, what Nvidia and pluribus have done by working together is to be able to take some of those services and be able to deploy them onto a smart Nick, right? >>To be able to deploy the DPU based smart SMARTNICK into the servers themselves. And then pluribus has come in and said, we're going to unify create that unified fabric across the networking space, into those networking services all the way down to the server. So the benefits of having that are pretty clear in that you're offloading that capability from the server. So your CPU's are optimized. You're saving a lot of money. You're not having to go outside of the server and go to a different rack somewhere else in the data center. So your performance is going to be optimized as well. You're not going to incur any latency hit for every trip round trip to the, to the firewall and back. So I think all those things are really important. Plus the fact that you're going to see from a, an organizational aspect, we talked about the dev ops and net ops teams. The network operations teams now can work with the security teams to establish the security policies and the networking policies. So that they've dev ops teams. Don't have to worry about that. So essentially they just create the guardrails and let the dev op team run. Cause that's what they want. They want that agility and speed. >>Yeah. Your point about CPU cycles is key. I mean, it's estimated that 25 to 30% of CPU cycles in the data center are wasted. The cores are wasted doing storage offload or, or networking or security offload. And, you know, I've said many times everybody needs a nitro like Amazon nugget, but you can't go, you can only buy Amazon nitro if you go into AWS. Right. Everybody needs a nitro. So is that how we should think about this? >>Yeah. That's a great analogy to think about this. Um, and I think I would take it a step further because it's, it's almost the opposite end of the spectrum because pluribus and video are doing this in a very open way. And so pluribus has always been a proponent of open networking. And so what they're trying to do is extend that now to these distributed services. So leverage working with Nvidia, who's also open as well, being able to bring that to bear so that organizations can not only take advantage of these distributed services, but also that unified networking fabric, that unified cloud fabric across that environment from the server across the switches, the other key piece of what pluribus is doing, because they've been doing this for a while now, and they've been doing it with the older application environments and the older server environments, they're able to provide that unified networking experience across a host of different types of servers and platforms. So you can have not only the modern application supported, but also the legacy environments, um, you know, bare metal. You could go any type of virtualization, you can run containers, et cetera. So a wide gambit of different technologies hosting those applications supported by a unified cloud fabric from pluribus. >>So what does that mean for the customer? I don't have to rip and replace my whole infrastructure, right? >>Yeah. Well, think what it does for, again, from that operational efficiency, when you're going from a legacy environment to that modern environment, it helps with the migration helps you accelerate that migration because you're not switching different management systems to accomplish that. You've got the same unified networking fabric that you've been working with to enable you to run your legacy as well as transfer over to those modern applications. Okay. >>So your people are comfortable with the skillsets, et cetera. All right. I'll give you the last word. Give us the bottom line here. >>So yeah, I think obviously with all the modern applications that are coming out, the distributed application environments, it's really posing a lot of risk on these organizations to be able to get not only security, but also visibility into those environments. And so organizations have to find solutions. As I said, at the beginning, they're looking to drive operational efficiency. So getting operational efficiency from a unified cloud networking solution, that it goes from the server across the servers to multiple different environments, right in different cloud environments is certainly going to help organizations drive that operational efficiency. It's going to help them save money for visibility, for security and even open networking. So a great opportunity for organizations, especially large enterprises, cloud providers who are trying to build that hyperscaler like environment. You mentioned the nitro card, right? This is a great way to do it with an open solution. >>Bob, thanks so much for, for coming in and sharing your insights. Appreciate it. >>You're welcome. Thanks. >>Thanks for watching the program today. Remember all these videos are available on demand@thekey.net. You can check out all the news from today@siliconangle.com and of course, pluribus networks.com many thanks diplomas for making this program possible and sponsoring the cube. This is Dave Volante. Thanks for watching. Be well, we'll see you next time.

>>mhm. Okay, we're back. I'm John. Fully with the Cuban. We're going to go deeper into a deep dive into unified cloud networking solution from Pluribus and NVIDIA. And we'll examine some of the use cases with Alexandra Barberry, VP of product Management and Pluribus Networks. And Pete Lambasts, the director of technical market and video. Remotely guys, thanks for coming on. Appreciate it. >>I think >>so. Deep dive. Let's get into the what and how Alexandra, we heard earlier about the pluribus and video partnership in the solution you're working together on. What is it? >>Yeah. First, let's talk about the what? What are we really integrating with the NVIDIA Bluefield deep You Technology pluribus says, uh, has been shipping, uh, in volume in multiple mission critical networks. So this adviser, one network operating systems it runs today on merchant silicon switches and effectively, it's a standard based open network computing system for data centre. Um, and the novelty about this operating system is that it integrates a distributed the control plane for Atwater made effective in STN overlay. This automation is completely open and interoperable, and extensible to other type of clouds is nothing closed and this is actually what we're now porting to the NVIDIA GPU. >>Awesome. So how does it integrate into video hardware? And specifically, how is plural is integrating its software within video hardware? >>Yeah, I think we leverage some of the interesting properties of the blue field the GPU hardware, which allows actually to integrate, um, our soft our network operating system in a manner which is completely isolated and independent from the guest operating system. So the first byproduct of this approach is that whatever we do at the network level on the GPU card is completely agnostic to the hyper visor layer or OS layer running on on the host even more. Um, uh, we can also independently manage this network. Note this switch on a nick effectively, uh, managed completely independently from the host. You don't have to go through the network operating system running on X 86 to control this network node. So you truly have the experience effectively of a top of rack for virtual machine or a top of rack for kubernetes spots. Where instead of, uh, um, if you allow me with analogy instead of connecting a server nique directly to a switchboard now you're connecting a VM virtual interface to a virtual interface on the switch on a nick. And also as part of this integration, we, uh, put a lot of effort, a lot of emphasis in accelerating the entire day to play in for networking and security. So we are taking advantage of the DACA, uh, video DACA api to programme the accelerators and this your accomplished two things with that number one, you, uh, have much greater performance, much better performance than running the same network services on an X 86 CPU. And second, this gives you the ability to free up. I would say around 2025% of the server capacity to be devoted either to additional war close to run your cloud applications. Or perhaps you can actually shrink the power footprint and compute footprint of your data centre by 20% if you want to run. The same number of computer work was so great efficiencies in the overall approach. >>And this is completely independent of the server CPU, right? >>Absolutely. There is zero quote from pluribus running on the X 86 this is what why we think this enables a very clean demarcation between computer and network. >>So, Pete, I gotta get I gotta get you in here. We heard that the GPUS enable cleaner separation of devops and net ops. Can you explain why that's important? Because everybody's talking. Def SEC ops, right now you've got Net ops. Net net SEC ops, this separation. Why is this clean separation important? >>Yeah, I think it's, uh, you know, it's a pragmatic solution, in my opinion, Um, you know, we wish the world was all kind of rainbows and unicorns, but it's a little a little messier than that. And I think a lot of the devops stuff in that, uh, mentality and philosophy. There's a natural fit there, right? You have applications running on servers. So you're talking about developers with those applications integrating with the operators of those servers? Well, the network has always been this other thing, and the network operators have always had a very different approach to things than compute operators. And, you know, I think that we we in the networking industry have gotten closer together. But there's still a gap. There's still some distance, and I think in that distance isn't going to be closed and So again it comes down to pragmatism. And I think, you know, one of my favourite phrases is look, good fences make good neighbours. And that's what this is. Yeah, >>it's a great point because devops has become kind of the calling card for cloud. Right? But devops is a simply infrastructure as code infrastructure is networking, right? So if infrastructure as code, you know, you're talking about, you know, that part of the stack under the covers under the hood, if you will. This is super important distinction. And this is where the innovation is. Can you elaborate on how you see that? Because this is really where the action is right now. >>Yeah, exactly. And I think that's where one from from the policy, the security, the zero trust aspect of this right. If you get it wrong on that network side, all of a sudden, you you can totally open up that those capabilities and so security is part of that. But the other part is thinking about this at scale, right. So we're taking one top of rack switch and adding, you know, up to 48 servers per rack, and so that ability to automate orchestrate and manage its scale becomes absolutely critical. >>Alexandra, this is really the why we're talking about here. And this is scale and again getting it right. If you don't get it right, you're gonna be really kind of up. You know what you know. So this is a huge deal. Networking matters. Security matters. Automation matters. DEVOPS. Net ops all coming together. Clean separation. Help us understand how this joint solution within video gets into the pluribus unified cloud networking vision. Because this is what people are talking about and working on right now. >>Yeah, absolutely. So I think here with this solution, we're talking to major problems in cloud networking. One is the operation of cloud networking, and the second is distributing security services in the cloud infrastructure. First, let me talk about first. What are we really unifying? If you really find something, something must be at least fragmented or disjointed. And what is this? Joint is actually the network in the cloud. If you look holistically how networking is deployed in the cloud, you have your physical fabric infrastructure, right? Your switches and routers. You build your I P clause fabric leaf and spine to apologies. this is actually well understood the problem. I would say, um, there are multiple vendors with a similar technologies. Very well, standardised. Very well understood. Um, and almost a commodity, I would say building an I P fabric these days. But this is not the place where you deploy most of your services in the cloud, particularly from a security standpoint. Those services are actually now moved into the compute layer where you actually were called. Builders have to instrument a separate network virtualisation layer, where they deploy segmentation and security closer to the workloads. And this is where the complication arise. This high value part of the cloud network is where you have a plethora of options, that they don't talk to each other, and they are very dependent on the kind of hyper visor or compute solution you choose. Um, for example, the networking API between an SX I environment or and hyper V or a Zen are completely disjointed. You have multiple orchestration layers and when and then when you throw in Also kubernetes in this In this in this type of architecture, uh, you're introducing yet another level of networking, and when you burn it, it runs on top of the M s, which is a prevalent approach. You actually just stuck in multiple networks on the compute layer that they eventually run on the physical fabric infrastructure. Those are all ships in the night effectively, right? They operate as completely disjointed. And we're trying to attack this problem first with the notion of a unified fabric, which is independent from any work clothes. Uh, whether it's this fabric spans on a switch which can become connected to a bare metal workload or can spend all the way inside the deep You where you have your multi hypervisors computer environment. It's one a P I one common network control plane and one common set of segmentation services for the network. That's probably number one. >>You know, it's interesting you I hear you talking. I hear one network different operating models reminds me the old server list days. You know there's still servers, but they called server list. Is there going to be a term network list? Because at the end of the, it should be one network, not multiple operating models. This this is like a problem that you guys are working on. Is that right? I mean, I'm not I'm just joking. Server, Listen, network list. But the idea is it should be one thing. >>Yeah, it's effectively. What we're trying to do is we're trying to recompose this fragmentation in terms of network operations across physical networking and server networking. Server networking is where the majority of the problems are because of the as much as you have standardised the ways of building, uh, physical networks and cloud fabrics with high people articles on the Internet. And you don't have that kind of, uh, sort of, uh, operational efficiency at the server layer. And this is what we're trying to attack first with this technology. The second aspect we're trying to attack is how we distribute the security services throughout the infrastructure more efficiently. Whether it's micro segmentation is a state, full firewall services or even encryption, those are all capabilities enabled by the blue field deep you technology and, uh, we can actually integrate those capabilities directly into the network fabric. Limiting dramatically, at least for is to have traffic, the sprawl of security appliances with a virtual or physical that is typically the way people today segment and secured the traffic in the >>cloud. All kidding aside about network. Listen, Civil is kind of fun. Fun play on words There the network is one thing is basically distributed computing, right? So I love to get your thoughts about this Distributed security with zero trust as the driver for this architecture you guys are doing. Can you share in more detail the depth of why DPU based approach is better than alternatives? >>Yeah, I think. What's what's beautiful and kind of what the deep you brings that's new to this model is completely isolated. Compute environment inside. So you know, it's the yo dog. I heard you like a server, So I put a server inside your server. Uh, and so we provide, you know, arm CPUs, memory and network accelerators inside, and that is completely isolated from the host. So the server, the the actual X 86 host just thinks it has a regular nick in there. But you actually have this full control plane thing. It's just like taking your top of rack, switch and shovel. Get inside of your compute node. And so you have not only the separation, um, within the data plane, but you have this complete control plane separation. So you have this element that the network team can now control and manage. But we're taking all of the functions we used to do at the top of rack Switch, and we distribute them now. And, you know, as time has gone on, we've we've struggled to put more and more and more into that network edge. And the reality is the network edge is the compute layer, not the top of rack switch layer. And so that provides this phenomenal enforcement point for security and policy. And I think outside of today's solutions around virtual firewalls, um, the other option is centralised appliances. And even if you can get one that can scale large enough, the question is, can you afford it? And so what we end up doing is we kind of hope that if aliens good enough or we hope that if you excellent tunnel is good enough, and we can actually apply more advanced techniques there because we can't physically, financially afford that appliance to see all of the traffic, and now that we have a distributed model with this accelerator, we could do it. >>So what's the what's in it for the customer real quick. I think this is an interesting point. You mentioned policy. Everyone in networking those policies just a great thing. And it has. You hear it being talked about up the stack as well. When you start getting to orchestrate microservices and what not all that good stuff going on their containers and whatnot and modern applications. What's the benefit to the customers with this approach? Because what I heard was more scale, more edge deployment, flexibility relative to security policies and application. Enablement. I mean, is that what what's the customer get out of this architecture? What's the enablement? >>It comes down to taking again the capabilities that were that top of rack switch and distracting them down. So that makes simplicity smaller. Blast Radius is for failure, smaller failure domains, maintenance on the networks and the systems become easier. Your ability to integrate across workloads becomes infinitely easier. Um, and again, you know, we always want to kind of separate each one of those layers. So, just as in, say, a Vieques land network, my leaf and spine don't have to be tightly coupled together. I can now do this at a different layer and so you can run a deep You with any networking in the core there. And so you get this extreme flexibility, you can start small. You can scale large. Um, you know, to me that the possibilities are endless. >>It's a great security control Playing really flexibility is key, and and also being situationally aware of any kind of threats or new vectors or whatever is happening in the network. Alexandra, this is huge Upside, right? You've already identified some, uh, successes with some customers on your early field trials. What are they doing? And why are they attracted? The solution? >>Yeah, I think the response from customer has been the most encouraging and exciting for for us to, uh, to sort of continuing work and develop this product. And we have actually learned a lot in the process. Um, we talked to three or two or three cloud providers. We talked to s P um, sort of telco type of networks, uh, as well as enter large enterprise customers. Um, in one particular case, um uh, one, I think. Let me let me call out a couple of examples here just to give you a flavour. There is a service provider, a cloud provider in Asia who is actually managing a cloud where they are offering services based on multiple hypervisors their native services based on Zen. But they also, um, ramp into the cloud workloads based on SX I and N K P M. Depending on what the customer picks from the piece from the menu. And they have the problem of now orchestrating through the orchestrate or integrating with Zen Centre with this fear with open stock to coordinate this multiple environments and in the process to provide security, they actually deploy virtual appliances everywhere, which has a lot of cost complication, and it's up into the service of you the promise that they saw in this technology they call it. Actually, game changing is actually to remove all this complexity, even a single network, and distribute the micro segmentation service directly into the fabric. And overall, they're hoping to get out of it. Tremendous OPEC's benefit and overall operational simplification for the cloud infrastructure. That's one important use case, um, another large enterprise customer, a global enterprise customer is running both Essex I and I purvey in their environment, and they don't have a solution to do micro segmentation consistently across Hypervisors. So again, micro segmentation is a huge driver. Security looks like it's a recurring theme talking to most of these customers and in the telco space. Um, uh, we're working with a few telco customers on the CFT programme, uh, where the main goal is actually to Arman Eyes Network operation. They typically handle all the V NFC with their own homegrown DPD K stock. This is overly complex. It is, frankly, also slow and inefficient. And then they have a physical network to manage the idea of having again one network to coordinate the provisioning of cloud services between the take of the NFC. Uh, the rest of the infrastructure is extremely powerful on top of the offloading capability. After by the blue fill the pews. Those are just some examples. >>There's a great use case, a lot more potential. I see that with the unified cloud networking. Great stuff shout out to you guys that NVIDIA, you've been following your success for a long time and continuing to innovate his cloud scales and pluribus here with unified networking. Kind of bringing the next level great stuff. Great to have you guys on and again, software keeps, uh, driving the innovation again. Networking is just part of it, and it's the key solution. So I got to ask both of you to wrap this up. How can cloud operators who are interested in in this new architecture and solution learn more? Because this is an architectural ship. People are working on this problem. They're trying to think about multiple clouds are trying to think about unification around the network and giving more security more flexibility to their teams. How can people learn more? >>And so, uh, Alexandra and I have a talk at the upcoming NVIDIA GTC conference, so it's the week of March 21st through 24th. Um, you can go and register for free and video dot com slash gtc. Um, you can also watch recorded sessions if you end up watching this on YouTube a little bit after the fact, Um, and we're going to dive a little bit more into the specifics and the details and what we're providing a solution >>as Alexandra. How can people learn more? >>Yeah, so that people can go to the pluribus website www pluribus networks dot com slash e. F t and they can fill up the form and, uh, they will contact Pluribus to either no more or to know more and actually to sign up for the actual early field trial programme. Which starts at the end of it. >>Okay, well, we'll leave it there. Thank you both for joining. Appreciate it up. Next, you're going to hear an independent analyst perspective and review some of the research from the Enterprise Strategy Group E s G. I'm John Ferry with the Cube. Thanks for watching. Mhm. Mhm.

>>Welcome back to the cubes coverage of ADA reinvent 2021 here, the cube coverage. I'm Judd for a, your host we're on the ground with two sets on the floor, real event. Of course, it's hybrid. It's online as well. You can check it out there. All the on-demand replays are there. We're here with Sandy Carter, worldwide vice president, public sector partners and programs. And we've got Fred Swanick, her founder, and chief curator of the room. We're talking about getting the best talent programming and in the cloud, doing great things, innovation all happening, Sandy. Great to see you. Thanks for coming on the cube, but appreciate it. Thanks for halfway to see. Okay. So tell us about the room. What is the room what's going on? >>Um, well, I mentioned in the room is to help the world's most extraordinary do us to fulfill their potential. So, um, it's a community of exceptional talent that we are building throughout the world, um, and connecting this talent to each other and connecting them to the organizations that are looking for people who can really move the needle for those organizations. >>So what kind of results are you guys seeing right now? Give us some stats. >>Well, it's a, it's a relatively new concept. So we're about 5,000 members so far, um, from 77 different countries. Um, and this is, you know, we're talking about sort of the top two to 3% of talent in different fields. Um, and, um, as we go forward, you know, we're really looking, seeing this as an opportunity to curate, um, exceptional talent. Um, and it feels like software engineering, data science, UX, UI design, cloud computing, um, and, uh, it really helped to, um, identify diverse talent as well from pockets that have typically been untapped for technology. Okay. >>I want to ask you kind of, what's the, how you read the tea leaves. How do I spot the talent, but first talk about the relationship with Amazon. What's the program together? How you guys working together? It's a great mission. I mean, we need more people anyway, coding everywhere, globally. What's the AWS connection. >>So Fred and I met and, uh, he had this, I mean the brilliant concept of the room. And so, uh, obviously you need to run that on the cloud. And so he's got organizations he's working at connecting them through the room and kind of that piece that he was needing was the technology. So we stepped in to help him with the technology piece because he's got all the subject matter expertise to train 3 million Africans, um, coming up on tech, we also were able to provide him some of the classwork as well for the cloud computing models. So some of those certs and things that we want to get out into the marketplace as well, we're also helping Fred with that as well. So >>I mean, want to, just to add onto that, you know, one of the things that's unique about the room is that we're trying to really build a long-term relationship with talent. So imagine joining the room as a 20 year old and being part of it until you're 60. So you're going to have a lot of that. You collect on someone as they progress through different stages of their career and the ability for us to leverage that data, um, and continuously learn about someone's, you know, skills and values and use, um, predictive algorithms to be able to match them to the right opportunities at the right time of their lives. And this is where the machine learning comes in and the, you know, the data lake that we're building to build to really store this massive data that we're going to be building on the top talent to the world. >>You know, that's a really good point. It's a list that's like big trend in tech where it's, it's still it's over the life's life of the horizon of the person. And it's also blends community, exactly nurturing, identifying, and assisting. But at the same day, not just giving people the answer, they got to grow on their own, but some people grow differently. So again, progressions are nonlinear sometimes and creativity can come out of nowhere. Got it. Uh, which brings me up to my number one question, because this always was on my mind is how do you spot talent? What's the secret sauce? >>Well, there is no real secret source because every person is unique. So what we look for are people who have an extra dose of five things, courage, passion, resilience, imagination, and good values, right? And this is what we're looking for. And you will someone who is unusually driven to achieve great things. Um, so of course, you know, you look at it from a combination of their, their training, you know, what they, what they've learned, but also what they've actually done in the workplace and feedback that you get from previous employers and data that we collect through our own interactions with this person. Um, and so we screened them through, you know, with the town that we had, didn't fly, we take them through really rigorous selection process. So, um, it takes, uh, for example, people go through an online assessments and then they go through an in-person interview and then we'll take them through a one to three month bootcamp to really identify, you know, people who are exceptional and of course get data from different sources about the person as well. >>Sandy, how do you see this collaboration helping, uh, your other clients? I mean, obviously talent, cross pollinates, um, learnings, what's your, you see this level of >>It has, uh, you know, AWS grows, obviously we're going to need more talent, especially in Africa because we're growing so rapidly there and there's going to be so much talent available in Africa here in just a few short years. Most of the tech talent will be in Africa. I think that that's really essential, but also as looking after my partners, I had Fred today on the keynote explaining to all my partners around the world, 55,000 streaming folks, how they can also leverage the room to fill some of their roles as well. Because if you think about it, you know, we heard from Presidio there's 3 million open cyber security roles. Um, you know, we're training 20 of mine million cloud folks because we have a gap. We see a gap around the world. And part of my responsibility with partners is making sure that they can get access to the right skills. And we're counting on the room and what Fred has produced to produce some of those great skills. You have AI, AML and dev ops. Tell us some of the areas you haven't. >>You know, we're looking at, uh, business intelligence, data science, um, full-stack software engineering, cybersecurity, um, you know, IOT talent. So fields that, um, the world needs a lot more talented. And I think today, a lot of technology, um, talent is moving from one place to another and what we need is new supply. And so what the room is doing is not only a community of top 10, but we're actually producing and training a lot more new talent. And that was going to hopefully, uh, remove a key bottleneck that a lot of companies are facing today as they try to undergo the digital trends. >>Well, maybe you can add some hosts on there. We need some cube hosts, come on, always looking for more talent on the set. You could be there. >>Yeah. The other interesting thing, John, Fred and I on stage today, he was talking about how easy to the first narrative written for easy to was written by a gentleman out of South Africa. So think about that right. ECE to talent. And he was talking about Ian Musk is based, you know, south African, right? So think about all the great talent that exists. There. There you go. There you go. So how do you get access to that talent? And that's why we're so excited to partner with Fred. Not only is he wicked impressive when a time's most influential people, but his mission, his life purpose has really been to develop this great talent. And for us, that gets us really excited because we, yeah, >>I think there's plenty of opportunities to around new business models in the U S for instance, um, my friends started upstart, which they were betting on people almost like a stock market. You know, almost like currency will fund you and you pay us back. And there's all kinds of gamification techniques that you can start to weave into the system. Exactly. As you get the flywheel going, exactly, you can look at it holistically and say, Hey, how do we get more people in and harvest the value of knowledge? >>That's exactly. I mean, one of the elements of the technology platform that we developed to the Amazon with AWS is the room intelligence platform. And in there is something called legacy points. So every time you, as a member of the room, give someone else an opportunity. You invest in their venture, you hire them, you mentor them, you get points and you can leverage those points for some really cool experiences, right? So you want to game-ify um, this community that is, uh, you know, essentially crowdsourcing opportunities. And you're not only getting things from the room, but you're also giving to others to enable everyone to grow. >>Yeah, what's the coolest thing you've seen. And this is a great initiative. First of all, it's a great model. I think it's, this is the future. Cause I'm a big believer that communities groups, as we get into this hybrid world is going to open up the virtualization. What the virtual world has shown us is virtualization, which is a cloud technology when Amazon started with Zen, which is virtualization technology, but virtualization, conceptually is replicating things. So if you think hybrid world, you can blend the connect people together. So now you have this social construct, this connective tissue between relationships, and it's always evolving, you know, this and you've been involved in community from, from, from the early days when you have that social evolution, it's not software as a mechanism. It's a human thing. Exactly. It's organism, it evolves. And so if you can get the software to think like that and the group to drive the behavior, it's not community software. >>Exactly. I mean, we say that the room is not an online community. It's really an offline community powered by technology. So our vision is to actually have physical rooms in different cities around the world, whether it's talent gathers, but imagine showing up at a, at a room space and we've got the technology to know what your interests are. We know that you're working on a new venture and there's this, there's a venture capitalists in that area, investing that venture, we can connect you right then that space powered by the, >>And then you can have watch parties. For instance, there's an event going on in us. You can do some watch parties and time shifted and then re replicated online and create a localization, but yet have that connection in >>Present. Exactly, exactly. Exactly. So what are the >>Learnings, what's your big learning share with the audience? What you've learned, because this is really kind of on the front edge of the new kind of innovation we're seeing, being enabled with software. >>I mean, one thing we're learning is that, uh, talent is truly, uh, evenly distribute around the world, but what is not as opportunity. And so, um, there's some truly exceptional talent that is hidden and on tap today. And if we can, you know, and, and today with the COVID pandemic companies or around the world, a lot more open to hiring more talent. So there's a huge opportunity to access new talent from, from sources that haven't been tapped before. Well, but also learnings the power of blending, the online and offline world. So, um, you know, the room is, as I mentioned, brings people together, normally in line, but also offline. And so when you're able to meet talent and actually see someone's personality and get a sense of the culture fit the 360 degree for your foot, some of that, you can't just get on a LinkedIn. Yes. That I built it to make a decision, to hire someone who is much better. And finally, we're also learning about the importance of long-term relationships. One of my motives in the room is relationships not transactions where, um, you actually get to meet someone in an environment where they're not pretending in an interview and you get to really see who they are and build relationships with them before you need to hide them. And these are some really unique ways that we think we can redefine how talent finds opportunity in the 21st. So >>You can put a cube in every room, we pick >>You up because, >>And the cube, what we do here is that when people collaborate, whether they're doing an interview together, riffing and sharing content is creating knowledge, but that shared experience creates a bonding. So when you have that kind of mindset and this room concept where it's not just resume, get a job, see you later, it's learning, having peers and colleagues and people around you, and then seeing them in a journey, multiple laps around the track of humans >>And going through a career, not just a job. >>Yes, exactly. And then, and then celebrating the ups and downs in learning. It's not always roses, as you know, it's always pain before you accelerate. >>Exactly. And you never quite arrive at your destination. You're always growing, and this is where technology can really play. >>Okay. So super exciting. Where's this go next, Sandy. And next couple of minutes left in. >>So, um, one of the things that we've envisioned, so this is not done yet, but, um, Fred and I imagined like, what if you could have an Alexa set up and you could say, Hey, you know, Alexa, what should be my next job? Or how should I go train? Or I'm really interested in being on a Ted talk. What could I do having an Alexa skill might be a really cool thing to do. And with the great funding that Fred Scott and you should talk about the $400 million to that, he's already raised $400 million. I mean, there, I think the sky's the limit on platforms. Like >>That's a nice chunk of change. There it is. We've got some fat financing as they say, >>But, well, it's a big mission. So to request significant resources, >>Who's backing you guys. What's the, who's the, where's the money coming from? >>It's coming from, um, the MasterCard foundation. They, our biggest funder, um, as well as, um, some philanthropists, um, and essentially these are people who truly see the potential, uh, to unlock, um, opportunity for millions of people global >>For Glen, a global scale. The vision has global >>Executive starting in Africa, but truly global. Our vision is eventually to have a community of about 10 to 20 million of the most extraordinary doers in the world, in this community, and to connect them to opportunity >>Angela and diverse John. I mean, this is the other thing that gets me excited because innovation comes from diversity of thought and given the community, we'll have so many diverse individuals in it that are going to get trained and mentored to create something that is amazing for their career as well. That really gets me excited too, as well as Amazon website, >>Smart people, and yet identifying the fresh voices and the fresh minds that come with it, all that that comes together, >>The social capital that they need to really accelerate their impact. >>Then you read the room and then you get wherever you need. Thanks so much. Congratulations on your great mission. Love the room. Um, you need to be the in Cuban, every room, you gotta get those fresh voices out there. See any graduates on a great project, super exciting. And SageMaker, AI's all part of, it's all kind of, it's a cool wave. It's fun. Can I join? Can I play? I tell you I need a room. >>I think he's top talent. >>Thanks so much for coming. I really appreciate your insight. Great stuff here, bringing you all the action and knowledge and insight here at re-invent with the cube two sets on the floor. It's a hybrid event. We're in person in Las Vegas for a real event. I'm John ferry with the cube, the leader in global tech coverage. Thanks for watching.

>>from around the globe. It's the >>cube >>With digital coverage of IBM think 2021 brought to you by IBM. Welcome back to the cube coverage of IBM Think 2021. I'm john for the host of the cube here for virtual event Cameron Siva who's here with corporate vice president with a M. D. Uh CVP and business development. Great to see you. Thanks for coming on the cube. >>Nice to be. It's an honor to be here. >>You know, love A. M. D. Love the growth, love the processors. Epic 7000 and three series was just launched. Its out in the field. Give us a quick overview of the of the of the processor, how it's doing and how it's going to help us in the data center and the edge >>for sure. No this is uh this is an exciting time for A. M. D. This is probably one of the most exciting times uh to be honest and in my 2020 plus years of uh working in sex industry, I think I've never been this excited about a new product as I am about the the third generation ethic processor that were just announced. Um So the Epic 7003, what we're calling it a series processor. It's just a fantastic product. We not only have the fastest server processor in the world with the AMG Epic 7763 but we also have the fastest CPU core so that the process of being the complete package to complete socket and then we also the fastest poor in the world with the the Epic um 72 F three for frequency. So that one runs run super fast on each core. And then we also have 64 cores in the CPU. So it's it's addressing both kind of what we call scale up and scale out. So it's overall overall just just an enormous, enormous product line that that I think um you know, we'll be we'll be amazing within within IBM IBM cloud. Um The processor itself includes 256 megabytes of L three cache, um you know, cash is super important for a variety of workloads in the large cache size. We have shown our we've seen scale in particular cloud applications, but across the board, um you know, database, uh java all sorts of things. This processor is also based on the Zen three core, which is basically 19% more instructions per cycle relative to ours, N two. So that was the prior generation, the second generation Epic Force, which is called Rome. So this this new CPU is actually quite a bit more capable. It runs also at a higher frequency with both the 64 4 and the frequency optimized device. Um and finally, we have um what we call all in features. So rather than kind of segment our product line and charge you for every little, you know, little thing you turn on or off. We actually have all in features includes, you know, really importantly security, which is becoming a big, big team and something that we're partnering with IBM very closely on um and then also things like 628 lanes of pc I E gen four, um are your faces that grew up to four terabytes so you can do these big large uh large um in memory databases. The pc I interfaces gives you lots and lots of storage capability so all in all super products um and we're super excited to be working with IBM honest. >>Well let's get into some of the details on this impact because obviously it's not just one place where these processes are going to live. You're seeing a distributed surface area core to edge um, cloud and hybrid is now in play. It's pretty much standard now. Multi cloud on the horizon. Company's gonna start realizing, okay, I gotta put this to work and I want to get more insights out of the data and civilian applications that are evolving on this. But you guys have seen some growth in the cloud with the Epic processors, what can customers expect and why our cloud providers choosing Epic processors, >>you know, a big part of this is actually the fact that I that am be um delivers upon our roadmap. So we, we kind of do what we say and say what we do and we delivered on time. Um so we actually announced I think was back in august of 2019, their second generation, Epic part and then now in March, we are now in the third generation. Very much on schedule. Very much um, intern expectations and meeting the performance that we had told the industry and told our customers that we're going to meet back then. So it's a really super important pieces that our customers are now learning to expect performance, jenin, Jenin and on time from A. M. D, which is, which is uh, I think really a big part of our success. The second thing is, I think, you know, we are, we are a leader in terms of the core density that we provide and cloud in particular really values high density. So the 64 cores is absolutely unique today in the industry and that it has the ability to be offered both in uh bare metal. Um, as we have been deployed in uh, in IBM cloud and also in virtualized type environment. So it has that ability to spend a lot of different use cases. Um and you can, you know, you can run each core uh really fast, But then also have the scale out and then be able to take advantage of all 64 cores. Each core has two threads up to 128 threads per socket. It's a super powerful uh CPU and it has a lot of value for um for the for the cloud cloud provider, they're actually about over 400 total instances by the way of A. M. D processors out there. And that's all the flavors, of course, not just that they're generation, but still it's it's starting to really proliferate. We're trying to see uh M d I think all across the cloud, >>more cores, more threads all goodness. I gotta ask you, you know, I interviewed Arvin the ceo of IBM before he was Ceo at a conference and you know, he's always been, I know him, he's always loved cloud, right? So, um, but he sees a little bit differently than just being like copying the clouds. He sees it as we see it unfolding here, I think Hybrid. Um, and so I can almost see the playbook evolving. You know, Red has an operating system, Cloud and Edge is a distributed system, it's got that vibe of a system architecture, almost got processors everywhere. Could you give us a sense of the over an overview of the work you're doing with IBM Cloud and what a M. D s role is there? And I'm curious, could you share for the folks watching too? >>For sure. For sure. By the way, IBM cloud is a fantastic partner to work with. So, so, first off you talked about about the hybrid, hybrid cloud is a really important thing for us and that's um that's an area that we are definitely focused in on. Uh but in terms of our specific joint partnerships and we do have an announcement last year. Um so it's it's it's somewhat public, but we are working together on Ai where IBM is a is an undisputed leader with Watson and some of the technologies that you guys bring there. So we're bringing together, you know, it's kind of this real hard work goodness with IBM problems and know how on the AI side. In addition, IBM is also known for um you know, really enterprise grade, yeah, security and working with some of the key sectors that need and value, reliability, security, availability, um in those areas. Uh and so I think that partnership, we have quite a bit of uh quite a strong relationship and partnership around working together on security and doing confidential computer. >>Tell us more about the confidential computing. This is a joint development agreement, is a joint venture joint development agreement. Give us more detail on this. Tell us more about this announcement with IBM cloud, an AMG confidential computing. >>So that's right. So so what uh you know, there's some key pillars to this. One of this is being able to to work together, define open standards, open architecture. Um so jointly with an IBM and also pulling in something assets in terms of red hat to be able to work together and pull together a confidential computer that can so some some key ideas here, we can work with work within a hybrid cloud. We can work within the IBM cloud and to be able to provide you with, provide, provide our joint customers are and customers with uh with unprecedented security and reliability uh in the cloud, >>what's the future of processors, I mean, what should people think when they expect to see innovation? Um Certainly data centers are evolving with core core features to work with hybrid operating model in the cloud. People are getting that edge relationship basically the data centers a large edge, but now you've got the other edges, we got industrial edges, you got consumers, people wearables, you're gonna have more and more devices big and small. Um what's the what's the road map look like? How do you describe the future of a. M. D. In in the IBM world? >>I think I think R I B M M D partnership is bright, future is bright for sure, and I think there's there's a lot of key pieces there. Uh you know, I think IBM brings a lot of value in terms of being able to take on those up earlier, upper uh layers of software and that and the full stack um so IBM strength has really been, you know, as a systems company and as a software company. Right, So combining that with the Andes Silicon, uh divided and see few devices really really is is it's a great combination, I see, you know, I see um growth in uh you know, obviously in in deploying kind of this, this scale out model where we have these very large uh large core count Cpus I see that trend continuing for sure. Uh you know, I think that that is gonna, that is sort of the way of the future that you want cloud data applications that can scale across multi multiple cores within the socket and then across clusters of Cpus with within the data center um and IBM is in a really good position to take advantage of that to go to, to to drive that within the cloud. That income combination with IBM s presence on prem uh and so that's that's where the hybrid hybrid cloud value proposition comes in um and so we actually see ourselves uh you know, playing in both sides, so we do have a very strong presence now and increasingly so on premises as well. And we we partner we were very interested in working with IBM on the on on premises uh with some of some of the key customers and then offering that hybrid connectivity onto, onto the the IBM cloud as well. >>I B M and M. D. Great partnership, great for clarifying and and sharing that insight come, I appreciate it. Thanks for for coming on the cube, I do want to ask you while I got you here. Um kind of a curveball question if you don't mind. As you see hybrid cloud developing one of the big trends is this ecosystem play right? So you're seeing connections between IBM and their and their partners being much more integrated. So cloud has been a big KPI kind of model. You connect people through a. P. I. S. There's a big trend that we're seeing and we're seeing this really in our reporting on silicon angle the rise of a cloud service provider within these ecosystems where hey, I could build on top of IBM cloud and build a great business. Um and as I do that, I might want to look at an architecture like an AMG, how does that fit into to your view as a doing business development over at A. M. D. I mean because because people are building on top of these ecosystems are building their own clouds on top of cloud, you're seeing data. Cloud, just seeing these kinds of clouds, specialty clouds. So I mean we could have a cute cloud on top of IBM maybe someday. So, so I might want to build out a whole, I might be a cloud. So that's more processors needed for you. So how do you see this enablement? Because IBM is going to want to do that, it's kind of like, I'm kind of connecting the dots here in real time, but what's your, what's your take on that? What's your reaction? >>I think, I think that's I think that's right and I think m d isn't, it isn't a pretty good position with IBM to be able to, to enable that. Um we do have some very significant osD partnerships, a lot of which that are leveraged into IBM um such as Red hat of course, but also like VM ware and Nutanix. Um this provide these always V partners provide kind of the base level infrastructure that we can then build upon and then have that have that A P I. And be able to build build um uh the the multi cloud environments that you're talking about. Um and I think that, I think that's right. I think that is that is one of the uh you know, kind of future trends that that we will see uh you know, services that are offered on top of IBM cloud that take advantage of the the capabilities of the platform that come with it. Um and you know, the bare metal offerings that that IBM offer on their cloud is also quite unique um and hyper very performance. Um and so this actually gives um I think uh the the kind of uh call the medic cloud that unique ability to kind of go in and take advantage of the M. D. Hardware at a performance level and at a um uh to take advantage of that infrastructure better than they could in another cloud environments. I think that's that's that's actually very key and very uh one of the one of the features of the IBM problems that differentiates it >>so much headroom there corns really appreciate you sharing that. I think it's a great opportunity. As I say, if you're you want to build and compete. Finally, there's no with the white space with no competition or be better than the competition. So as they say in business, thank you for coming on sharing. Great great future ahead for all builders out there. Thanks for coming on the cube. >>Thanks thank you very much. >>Okay. IBM think cube coverage here. I'm john for your host. Thanks for watching. Mm

>>from around the globe. It's the >>cube with >>Digital coverage of IBM think 2021 brought to you by IBM. Welcome back to the cube coverage of IBM Think 2021. I'm john for the host of the cube here for virtual event Cameron Siva who's here with corporate vice president with a M. D. Uh CVP and business development. Great to see you. Thanks for coming on the cube. >>Nice to be. It's an honor to be here. >>You know, love A. M. D. Love the growth, loved the processors. Epic 7000 and three series was just launched its out in the field. Give us a quick overview of the of the of the processor, how it's doing and how it's going to help us in the data center on the edge >>for sure. No this is uh this is an exciting time for A. M. D. This is probably one of the most exciting times uh to be honest and in my 2020 plus years of uh working in sex industry, I think I've never been this excited about a new product as I am about the the third generation Epic processor that we just announced. Um So the Epic 7003, what we're calling it a serious processor. It's just a fantastic product. We not only have the fastest server processor in the world with the AMG Epic 7763 but we also have the fastest CPU core so that the process of being the complete package, the complete socket and then we also the fastest poor in the world with the the Epic um 72 F three for frequency. So that one runs run super fast on each core. And then we also have 64 cores in the CPU. So it's it's addressing both kind of what we call scale up and scale out. So it's overall overall just just an enormous, enormous product line that that I think um you know, we'll be we'll be amazing within within IBM IBM cloud. Um The processor itself includes 256 megabytes of L three cache. Um you know, cash is super important for a variety of workloads in the large cat size. We have shown our we've seen scale in particular cloud applications, but across the board, um you know, database, uh java whole sorts of things. This processor is also based on the Zen three core, which is basically 19% more instructions per cycle relative to ours, N two. So that was the prior generation, the second generation Epic Force, which is called Rome. So this this new CPU is actually quite a bit more capable. It runs also at a higher frequency with both the 64 4 and the frequency optimized device. Um and finally, we have um we call all in features so rather than kind of segment our product line and charge you for every little, you know, little thing you turn on or off. We actually have all in features includes, you know, really importantly security, which is becoming a big, big team and something that we're partnering with IBM very closely on um and then also things like 628 lanes of pc I E gen four, um are your faces that grew up to four terabytes so you can do these big large uh large um in memory databases, the Pc I interfaces gives you lots and lots of storage capability. So all in all super products um and we're super excited to be working with IBM honest. >>Well, let's get into some of the details on this impact because obviously it's not just one place where these processes are gonna live. You're seeing a distributed surface area core to edge um cloud and hybrid is now in play. It's pretty much standard now. Multi cloud on the horizon. Company's gonna start realizing, okay, I gotta put this to work and I want to get more insights out of the data and civilian applications that are evolving on this. But you guys have seen some growth in the cloud with the Epic processors, what can customers expect and why our cloud providers choosing Epic processors, >>you know, a big part of this is actually the fact that I that am d um delivers upon our roadmap. So we we kind of do what we say and say what we do and we delivered on time. Um so we actually announced I think was back in august of 2019, their second generation. That big part and then now in March, we are now in the third generation, very much on schedule, very much um intent, expectations and meeting the performance that we had told the industry and told our customers that we're going to meet back then. So it's a really super important pieces that our customers are now learning to expect performance, jenin, jenin and on time from A. M. D, which is, which is uh, I think really a big part of our success. The second thing is, I think, you know, we are, we are a leader in terms of the core density that we provide and cloud in particular really values high density. So the 64 cores is absolutely unique today in the industry and that it has the ability to be offered both in uh, bare metal, um, as we have been deployed in uh, in IBM Club and also in virtualized type environment. So it has that ability to spend a lot of different use cases. Um And you can, you know, you can run each core really fast, But then also have the scale out and then be able to take advantage of all 64 cores. Each core has two threads up to 128 threads per socket. It's a super powerful uh CPU and it has a lot of value for um for the with a cloud cloud provider, they're actually about over 400 total instances by the way of A. M. D. Processors out there. And that's all the flavors, of course, not just that they're generation, but still it's it's starting to really proliferate. We're trying to see uh M d I think all across the cloud, >>more cores, more threads all goodness. I gotta ask you, you know, I interviewed Arvin the Ceo of IBM before he was Ceo at a conference and you know, he's always been I know him, he's always loved cloud, right? So, um but he sees a little bit differently than just being like copying the clouds. He sees it as we see it unfolding here. I think Hybrid. Um and so I can almost see the playbook evolving. You know, Red has an operating system. Cloud and Edge is a distributed system. It's got that vibe of a system architecture, you got processors everywhere. Could you give us a sense of the over an overview of the work you're doing with IBM Cloud and what a M. D s role is there? And I'm curious could you share for the folks watching too? >>For sure. For sure. By the way, IBM cloud is a fantastic partner to work with. So, so, first off you talked about about the hybrid, hybrid cloud is a really important thing for us and that's um that's an area that we are definitely focused in on, uh but in terms of our specific joint partnerships and we did an announcement last year, so it's it's it's somewhat public, but we are working together on ai where IBM is a is an undisputed leader with Watson and some of the technologies that you guys bring there. So we're bringing together, you know, it's kind of this real hard work goodness with IBM s progress and know how on the AI side. In addition, IBM is also known for um you know, really enterprise grade, yeah, security and working with some of the key sectors that need and value, reliability, security, availability um in those areas. Uh and so I think that partnership, we have quite a bit of uh quite a strong relationship and partnership around working together on security and doing confidential computer. >>Tell us more about the confidential computing. This is a joint development agreement, is a joint venture joint development agreement. Give us more detail on this. Tell us more about this announcement with IBM cloud, an AMG confidential computing. >>So that's right. So so what uh, you know, there's some key pillars to this. One of us is being able to to work together, define open standards, open architecture. Um so jointly with an IBM and also pulling in some of the assets in terms of red hat to be able to work together and pull together a confidential computer that can so some some key ideas here, we can work with, work within a hybrid cloud. We can work within the IBM cloud and to be able to provide you with, provide, provide our joint customers are and customers with with with unprecedented security and reliability uh in the cloud, >>what's the future of processors? I mean, what should people think when they expect to see innovation? Um Certainly data centers are evolving with core core features to work with hybrid operating model in the cloud. People are getting that edge relationship basically the data centers a large edge, but now you've got the other edges, we got industrial edges, you got consumers, people wearables. You're gonna have more and more devices big and small. Um What's the what's the road map look like? How do you describe the future of a. M. D. In in the IBM world? >>I think I think R I B M M. D partnership is bright, future is bright for sure, and I think there's there's a lot of key pieces there. Uh you know, I think IBM brings a lot of value in terms of being able to take on those up earlier, upper uh layers of software and that and the full stack um so IBM strength has really been, you know, as a systems company and as a software company. Right? So combining that with the Andes silicon, uh divide and see few devices really really is is it's a great combination. I see, you know, I see um growth in uh you know, obviously in in deploying kind of this, this scale out model where we have these very large uh large core count cpus, I see that trend continuing for sure. Uh you know, I think that that is gonna that is sort of the way of the future that you want cloud data applications that can scale across multi multiple cores within the socket and then across clusters of Cpus with within the data center. Um and IBM is in a really good position to take advantage of that to go to to to drive that within the cloud. That income combination with IBM s presence on prem. Uh and so that's that's where the hybrid hybrid cloud value proposition comes in. Um and so we actually see ourselves uh you know, playing in both sides. So we do have a very strong presence now and increasingly so on premises as well. And we we partner we were very interested in working with IBM on the on on premises uh with some of some of the key customers and then offering that hybrid connectivity onto, onto the the IBM cloud as >>well. I B M and M. D. Great partnership, great for clarifying and and sharing that insight come. I appreciate it. Thanks for for coming on the cube. I do want to ask you while I got you here. Um kind of a curveball question if you don't mind. You know, as you see hybrid cloud developing one of the big trends is this ecosystem play, right? So you're seeing connections between IBM and their and their partners being much more integrated. So cloud has been a big KPI kind of model. You connect people through a. P. I. S. There's a big trend that we're seeing and we're seeing this really in our reporting on silicon angle the rise of a cloud service provider within these ecosystems where hey, I could build on top of IBM cloud and build a great business. Um and as I do that, I might want to look at an architecture like an AMG, how does that fit into to your view as a doing business development over at AMG because because people are building on top of these ecosystems are building their own clouds on top of clouds, just seeing data cloud, just seeing these kinds of clouds, specialty clouds. So we could have a cute cloud on on top of IBM maybe someday. So, so I might want to build out a whole, I might be a cloud, so that's more processors needed for you. So how do you see this enablement? Because IBM is going to want to do that, it's kind of like, I'm kind of connecting the dots here in real time, but what's your, what's your take on that? What's your reaction? >>I think, I think that's I think that's right and I think m d isn't it isn't a pretty good position with IBM to be able to to enable that. Um we do have some very significant OsD partnerships, a lot of which that are leveraged into IBM um such as red hat of course, but also like VM ware and Nutanix. Um this provide these OS V partners provide kind of the base level infrastructure that we can then build upon and then have that have that A P. I. And be able to build, build um uh the the multi cloud environments that you're talking about. Um and I think that I think that's right, I think that is that is one of the uh you know, kind of future trends that that we will see uh you know, services that are offered on top of IBM cloud that take advantage of the the capabilities of the platform that come with it. Um and you know, the bare metal offerings that that IBM offer on their cloud is also quite unique um and hyper very performance. Um and so this actually gives um I think uh the the kind of uh I've been called a meta cloud, that unique ability to kind of go in and take advantage of the M. D. Hardware at a performance level and at a um uh to take advantage of that infrastructure better than they could in another crowd environments. I think that's that's that's actually very key and very uh one of the, one of the features of the IBM problems that differentiates it >>so much headroom there corns really appreciate you sharing that. I think it's a great opportunity. As I say, if you're you want to build and compete. Finally, there's no with the white space, with no competition or be better than the competition. So as they say in business, thank you for coming on sharing. Great, great future ahead for all builders out there. Thanks for coming on the cube. >>Thanks thank you very >>much. Okay. IBM think cube coverage here. I'm john for your host. Thanks for watching. Mm mm

(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a NetApp storage, right? So workloads are going to shift from the business applications, and these business applications, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SaaS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud then come compute, Kubernetes. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)