 from our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE Conversation. Over and welcome to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We are here at Arpid Joshua Pura, GM of Networking Edge IoT for the Linux Foundation. Arpid, great to see you again. Welcome back to theCUBE. Thanks for joining us. Thank you. So obviously we love the Linux Foundation and following all the events we've chatted in the past about networking. Compute storage and networking just doesn't seem to go away with cloud and on-premise hybrid cloud, multi-cloud, but open-source software continues to surpass expectations, growth, geographies outside the United States and North America. Just overall, just greatness in software. Everything's an abstraction layer now. You got Kubernetes, cloud native, so many good things going on with software. So congratulations. Well, thank you. No, I think we're excited too. So you guys got a big event coming up in China OSS open-source summit plus CUBECon. A lot of exciting things. I want to talk about that in a second, but I want to get your take on a couple of key things. IoT Edge, Edge and IoT, deep learning in AI and networking. I want to kind of drill down with you. Tell us what's the updates on the projects around Linux Foundation? The exciting ones. I mean, we know cloud native CNC is on a tier, more logos, more members keeps growing. Cloud natives clearly has a lot of opportunity, but the classic in the stack, certainly networking and storage are still kicking butt. Yeah, so let me start off by Edge. And the fundamental assumption here is that what happened in the cloud and core is going to move to the Edge. And it's going to be 50, 100, 200 times larger in terms of opportunity, applications, spending, et cetera. And so what LF did was we announced a very exciting project called Linux Foundation Edge as an umbrella earlier in January. And it was announced with over 60 founding members, right? It's the largest founding member announcements we have had in quite some time. And the reason for that is very simple. The project aims at unifying the fragmented Edge and IoT market. So today, Edge is completely fragmented. If you talk to clouds, they have a view of Edge, right? Azure, Amazon, Baidu, Tencent, you name it. If you talk to the enterprise, they have a view of what Edge needs to be. If you talk to the telcos, they are bringing the telecom stack close to the Edge. And then if you talk to the IoT vendors, they have a perception of Edge. So each of them are solving the Edge problems differently. What LF Edge is doing is it is unifying a framework and set of frameworks that allow you to create a common life cycle management framework for Edge compute. Now the best part of it is it's built on five exciting technologies. So people ask, why now? So there are five technologies that are converging at the same time. 5G, low latency. Net NFV, network function virtualization, so on-demand, AI, right? So predictive analytics for machine learning. Container and microservices app development. So you can really write apps really fast. And then hardware development, TPU, GPU, NPU, lots of exciting different sides and shapes. All five converging, put it close to the apps and you have a whole new market. I mean, this is first of all complicated in the sense of and cluttered, fragmented, shifting ground. So it's an opportunity. It's an opportunity. I get that, fragmented. You got the clouds, you got the enterprises and you got the telcos all kind of doing their own thing. Multiple technologies exploding. 5G, Wi-Fi 6, a bunch of other things you laid out all happening. But also you have all those suppliers, right? And so you have different manufacturers. At different layers. So it's very multiple dimensions to the complexity. Correct. What are you guys seeing in terms of as a solution? What's motivating the founding members when you say unifying, what specifically does that mean? What that means is the entire ecosystem from those markets are coming together to solve common problems. And I always sort of joke around, but it's true. The common problems are really the plumbing, right? It's the common life cycle management. How do you start, stop, boot, load, log, things like that. How do you abstract? Now in the edge, you got 400, 500 interfaces that comes into an IoT or an Edge device. ZigBee, Bluetooth, you got protocols like MQTT, things that are legacy and new, right? Then you have connectivity to the clouds, devices of various forms and shapes. So there's a lot of end by end problems as we call it. So the cloud players. For LF Edge, for example, Tencent and Baidu and the cloud leaders are coming together and say, let's solve it once. The industrial IoT player like in our dynamic, OSI soft, they're coming in saying let's solve it once. The telcos, AT&T, NTT, they're saying, let's solve it once, right? And let's solve this problem in open source because we all don't need to do it and we'll differentiate on top. So, and then of course the classic system vendors that support these markets are all joining hands. And then talk about the business pressure real quick. I know, obviously you look at say Alibaba for instance and the folks who mentioned Tencent and China, they're perfecting the edge. You've got videos, the Edge, all kinds of Edge devices. Correct. So there's business pressures as well. The business pressure is very simple. The innovation has to speed up. The cost has to go down and new apps are coming up. So extra revenue, right? So because of these five technologies I mentioned, you got the top killer apps in Edge or anything that is kind of video but not YouTube, right? So anything that the video comes from 360 venues or drones, things like that. Plus anything that moves but that's not a phone, right? So things like connected cars, vehicles, right? All of those are Edge applications. So in LF Edge we are defining Edge as an application that requires 20 milliseconds or less latency. I can't wait for someone to define software defined Edge or it's probably is defined. A great example, I interviewed an R&D engineer at VM where yesterday in San Francisco is at the radio event and we're just riffing on 5G and talking about software at the Edge and one of the advances that's coming is splicing the frequency so that you could put software in the radios at the antennas so you can essentially provision in real time. And that's a telco use case, right? So our projects at the LF Edge are Edge X Foundry, a Crano, Edge Virtualization Engine, Open Glossary, Home Edge, right? There's five and growing and all of these software projects can allow you to put Edge blueprints. And blueprints are really reference solutions for smart cities, manufacturing, telcos, industrial gateways, et cetera, et cetera. So it's going to be a fertile ground for entrepreneurship too if you think about it. Because just the radio software that spices the radio spectrum is going to essentially maybe enable a service provider market in towers, right on my own lap. I can own the tower and rent it out, one radio. So business model innovation is also an opportunity, not just the business pressure to have an Edge, but. So technology, business, and market pressures, all three are colliding. Yeah, perfect storm. So Edge is very exciting for us and we had some new announcements come out in May and more exciting news to come out in June as well. And so go back into Linus Foundation. So if I want to learn more. LFedge.org. That's kind of the CNCF of Edge, if you will, right? Yeah, it's an umbrella with all the projects and that's equivalent to the CNCF, right? And of course it's a huge growth. So it's got momentum, 64 founding members. And now we are at 70 founding members and growing. And how long has it been around? It's the umbrella has been around for about five months. Some of the projects have been around for a couple of years as they incubate. Well, let us know when the events start kicking in, we'll get the cube down and we'll cover it. It's super exciting. Again, multiple dimensions, innovation. All right, next topic. One of my favorites is AI and deep learning. AI is great. If you don't have data, you can't really make it work. Deep learning requires data. So this is a data conversation. What's going on in the Linus Foundation around AI and deep learning? Yeah, so we have a foundation called LF Deep Learning, as you know. It was launched last year. And since then we have significantly moved it forward by adding more members. And obviously the key here is adding more projects, right? So our goal in the LF Deep Learning Foundation is to bring the community of data scientists, researchers, entrepreneurs, academia and end users to collaborate and create frameworks and platforms that don't require a PhD to use. So a lot of data ingestion, managing data. So not a lot of coding, more data analysts, and more applications. It's more, I would say platforms for use, right? So frameworks that you can actually use to get business outcomes, right? So projects include Accumals, which is a machine learning framework that allows you, and a marketplace which allows you to sort of use a lot of use cases that can be commonly put. And this is across all verticals, but I'll give you a telecom example. For example, there is a use case, which is drones inspecting base stations and doing analytics for maintenance, right? That can be fed into a marketplace used by other operators worldwide. You don't have to repeat that and you don't need to understand the details of machine learning algorithms, right? So we are trying to do that. There are projects that have been contributed from Tencent, Baidu, Uber, et cetera. Angel, Elastic Deep Learning, Pyro. It's a huge investment. And everybody wins with this contribution because data is one of those things where if this is available, it just gets smarter. And so if you look at deep learning and machine learning, right? I mean, obviously there's the classic definition. I won't go into that. But from our perspective, we look at data and how you can share the data. And so from an LF perspective, we have something called a CDLA license. So think of an Apache for data. How do you share data? Because it's a big issue. And we have solved that problem. Then you can say, hey, there is all these machine learning, you know, TensorFlow and others, right? How can you use it and have plug-ins to this framework, right? Then there's the infrastructure. Where do you run these machine learning? Like if you run it on edge, you can run predictive maintenance before a machine breaks down. If you run it in the core, you can do a lot more, right? So we've done that level of integration. So you're treating data like code. You can bring data to the table, apply some licensing, best practices like Apache. And then, yes, and then integrate it with the machine learning, deep learning models and create platforms and frameworks, right? Whether it's for cloud services, whether it's for sharing across clouds, elastic search, et cetera. And Amazon does that internally. They vertically integrate SageMaker, for instance. That's exactly right. And this is the open source version of it. Got it. Oh, that's awesome. So, and how does someone get involved here? I'll see you develop. I love this. Deep learning is the place to go under Linux Foundation similar to LFH and CNCF. So it's not just developers. It's also people who have data who might want to expose it in. Data scientists, data, databases, algorithmists, machine learning, and obviously a whole bunch of startups. A new kind of developer, data developer. And exactly, and a lot of verticals like the security vertical, telecom vertical, enterprise verticals, finance, et cetera. You know, I've always said, you and I talked about this before. I always rant on theCUBE about this. I believe that there's going to be a data development environment where data is code, kind of like what DevOps did with. It's the new currency. It's the new currency. All right, so final area I want to chat with you before we get into the OSS China thing. Networking. Yeah. Near and dear to your heart. Near and dear to my heart. Networking's hot now, because if you bring IoT, Edge, AI, networking, you got to move things around. Move things around, right? They still need networking. So we're in the second year of the LFH networking journey. And we are really excited at the progress that has happened, right? So projects like ONAP, Open Daylight, Tungsten Fabric, OPNFV, FDIO, right? I mean, these are now, I wouldn't say household names, but business enterprise names. And we've seen pretty much all the telecom providers, almost 70% of the subscribers covered, enabled by the service providers now participating. Vendors are completely behind it. So we're moving into a phase, which is really the deployment phase. And we are starting to see, not just pox, but real deployments happen in some of the major carriers now. Very excited, you know, Dublin. ONAP's Dublin release is coming up. OPNFV just released the Hunter release. Lots of exciting work in FIDO to sort of connect multiple projects together. So we're looking at it. The big news there is the launch of what's called OVP. Or, you know, it's a compliance and verification program that cuts down the deployment time of a VNF by half. You know, it's interesting, Stu and I always talk about the Stu Miniman and I talk about the CUBE co-host with me about networking. You know, virtualization came out. I was like, oh, networking's going to change. Actually, help networking. Now you're seeing programmable networks come out. You see Cisco doing a lot of things, Juniper as well. And you got containers and Kubernetes right around the corner. So again, this is not going to change the need. It's going to change the desire and need of networking. It's going to change what networking is. How do you describe that to people? Someone said, yeah, but tell me what's going on with networking, you know? Virtualization got through that wave. Now I've got the container, Kubernetes service mesh wave. How does networking change? Yeah, so it's a four-step process, right? The first step, as you rightly said, virtualization, right? Moved into VMs. Then came disaggregation, which led, enabled by the technology SDN, as we all know, right? Then came orchestration, right? Which was last year. And that was enabled by projects like ONAP and automation. So now all of the networks are automated, fully running, self-healing, you know, feedback, close control, all that stuff. And networks have to be automated before 5G and IoT and all of these things hit, because you're no longer talking about phones. You're talking about things that get connected, right? So that's where we are today. And that journey continues for another two years. And beyond, right? But very heavy focus on deployment. And while that's happening, we're looking at, you know, the hybrid version of, you know, VMs and containers running in the network. How do you make that happen? How do you, you know, how do you translate one from the other? So, you know, VNF, CNFs, everything going at the same time. You know, what's exciting is with the software abstractions emerging, the hard problems are starting to emerge. Because as it gets more complicated and by end problems, as you said, there's a lot of new costs and complexities. For instance, the big conversation at the edge is, you don't want to move data around. No, so you want to move compute to the edge. You can, yeah. There's still a networking problem. You still got edge. So edge, AI, deep learning, networking, they're all tied together, right? And this is where Linux foundation by developing these projects in umbrellas, but then allowing working groups to collaborate between these projects is a very simple governance mechanism we use. So for example, we have edge working groups in Kubernetes that work with LFH. We have hyper ledger signaling, SIGs that work for telecom. So LFN and hyper ledger, right? Then we have automotive grade Linux that have connected cars working on the edge. Massive collaboration, but that's how it works. Yeah, you connect the dots, but you don't kind of force any kind of semantic or syntax into what people can do. Each project is autonomous and independent, but related. Yeah, it's smart. You guys have a good view. I'm a big fan of what you guys do. Okay, let's talk about the open source summit and KubeCon happening in China, a week of the 24th of June. Correct. What's going on there is a lot of stuff going on beyond cloud native and Linux. What are some of the hot areas in China that you guys are going to be talking about? I know you're going over. Yeah, so we're really excited to be there. And this is again, life beyond Linux and cloud native. There's a whole dimension of projects there. Everything from the edge and the excitement of IoT, cloud edge, we have keynotes from Tencent and VMware and all the Chinese, China Mobile and others that are all focusing on the explosive growth of open source in China, right? Yeah, and they have a lot of use cases. They've been very aggressive on mobility. Very aggressive on mobility data, right? And they have been a big contributor to open source, right? So all of that is going to happen there. A lot of tracks on AI and deep learning as a lot more algorithms come out of the Tencent and the Baidu and the Alibaba's of the world. So we have tracks there. We have huge tracks on networking because 5G and implementation of own app and network automation is all part of the umbrella. So we're looking at a cross section of projects in open source summit and KubeCon, right? All integrated in Shanghai. And a lot of use cases are developing certainly in the edge in China. Correct. Cross-pollination has been addressed in China. So they've kind of solved some of those problems. Yeah, and I think the good news is as a global community, which is open source, whether it's Europe, Asia, China, India, Japan, the developers are coming together very nicely through a common governance which crosses boundaries and building on use cases that are relevant to their community. And what's great about what you guys have done with Linux Foundation is that you're not taking positions on geographies because let the clouds do that because clouds have. Clouds have geographies, edge may have geography, they have regions. The software, software. Software, software. All right, thanks for coming in. Great insight, love talking about networking. The deep learning congratulations and obviously the IoT edge is hot. Thank you very much. A good trip with China. Thanks for coming in. Thank you, thank you. I'm John Furrier here for Kube Conversation with the Linux Foundation, big event in China. OSS Open Source Summit and KubeCon in Shanghai. Week of January, June 24th. Kube Conversation, thanks for watching.