(lively music) >> Hello, everyone. Welcome back to the Supercloud presentation here. I'm theCUBE, I'm John Furrier, your host. What a great segment here. We're going to unpack the networking aspect of the cloud, how that translates into what Supercloud architecture and platform deployment scenarios look like. And demystify multi-cloud, hybridcloud. We've got two great experts. Amir Khan, the Co-Founder and CEO of Alkira, Atif Khan, Co-Founder and CTO of Alkira. These guys been around since 2018 with the startup, but before that story, history in the tech industry. I mean, routing early days, multiple waves, multiple cycles. >> Welcome three decades. >> Welcome to Supercloud. >> Thanks. >> Thanks for coming on. >> Thank you so much for having us. >> So, let's get your take on Supercloud because it's been one of those conversations that really galvanized the industry because it kind of highlights almost this next wave, this next side of the street that everyone's going to be on that's going to be successful. The laggards on the legacy seem to be stuck on the old model. SaaS is growing up, it's ISVs, it's ecosystems, hyperscale, full hybrid. And then multi-cloud around the corners cause all this confusion, everyone's hand waving. You know, this is a solution, that solution, where are we? What do you guys see as this supercloud dynamic? >> So where we start from is always focusing on the customer problem. And in 2018 when we identified the problem, we saw that there were multiple clouds with many diverse ways of doing things from the network perspective, and customers were struggling with that. So we delved deeper into that and looked at each one of the cloud architectures completely independent. And there was no common solution and customers were struggling with that from the perspective. They wanted to be in multiple clouds, either through mergers and acquisitions or running an application which may be more cost effective to run in something or maybe optimized for certain reasons to run in a different cloud. But from the networking perspective, everything needed to come together. So that's, we are starting to define it as a supercloud now, but basically, it's a common infrastructure across all clouds. And then integration of high lift services like, you know, security or IPAM services or many other types of services like inter-partner routing and stuff like that. So, Amir, you agree then that multi-cloud is simply a default result of having whatever outcomes, either M&A, some productivity software, maybe Azure. >> Yes. >> Amazon has this and then I've got on-premise application, so it's kinds mishmash. >> So, I would qualify it with hybrid multi-cloud because everything is going to be interconnected. >> John: Got it. >> Whether it's on-premise, remote users or clouds. >> But have CTO perspective, obviously, you got developers, multiple stacks, got AWS, Azure and GCP, other. Not everyone wants to kind of like go all in, but yet they don't want to hedge too much because it's a resource issue. And I got to learn this stack, I got to learn that stack. So then now, you have this default multi-cloud, hybrid multi-cloud, then it's like, okay, what do I do? How do you spread that around? Is it dangerous? What's the the approach technically? What's some of the challenges there? >> Yeah, certainly. John, first, thanks for having us here. So, before I get to that, I'll just add a little bit to what Amir was saying, like how we started, what we were seeing and how it, you know, correlates with the supercloud. So, as you know, before this company, Alkira, we were doing, we did the SD-WAN company, which was Viptela. So there, we started seeing when people started deploying SD-WAN at like a larger scale. We started like, you know, customers coming to us and saying they needed connectivity into the cloud from the SD-WAN. They wanted to extend the SD-WAN fabric to the cloud. So we came up with an architecture, which was like later we started calling them Cloud onRamps, where we built, you know, a transit VPC and put like the virtual instances of SD-WAN appliances extended from there to the cloud. But before we knew, like it started becoming very complicated for the customers because it wasn't just connectivity, it also required, you know, other use cases. You had to instantiate or bring in security appliances in there. You had to secure all of that stuff. There were requirements for, you know, different regions. So you had to bring up the same thing in different regions. Then multiple clouds, what did you do? You had to replicate the same thing in multiple clouds. And now if there was was requirement between clouds, how were you going to do it? You had to route traffic from somewhere, and come up with all those routing controls and stuff. So, it was very complicated. >> Like spaghetti code, but on network. >> The games begin, in fact, one of our customers called it spaghetti mess. And so, that's where like we thought about where was the industry going and which direction the industry was going into? And we came up with the Alkira where what we are doing is building a common infrastructure across multiple clouds, across in, you know, on-prem locations, be it data centers or physical sites, branches sites, et cetera, with integrated security and network networking services inside. And, you know, nowadays, networking is not only about connectivity, you have to secure everything. So, security has to be built in. Redundancy, high availability, disaster recovery. So all of that needs to be built in. So that's like, you know, kind of a definition of like what we thought at that time, what is turning into supercloud now. >> Yeah. It's interesting too, you mentioned, you know, VPCs is not, configuration of loans a hassle. Nevermind the manual mistakes could be made, but as you decide to do something you got to, "Oh, we got to get these other things." A lot of the hyper scales and a lot of the alpha cloud players now, and cloud native folks, they're kind of in that mode of, "Wow, look at what we've built." Now, they're got to maintain, how do I refresh it? Like, how do I keep the talent? So they got this similar chaotic environment where it's like, okay, now they're already already through, so I think they're going to be okay. But then some people want to bypass it completely. So there's a lot of customers that we see out there that fit the makeup of, I'm cloud first, I've lifted and shifted, I move some stuff to the cloud. But I want to bypass all that learnings from all the people that are gone through the past three years. Can I just skip that and go to a multi-cloud or coherent infrastructure? What do you think about that? What's your view? >> So yeah, so if you look at these enterprises, you know, many of them just to find like the talent, which for one cloud as far as the IT staff is concerned, it's hard enough. And now, when you have multiple clouds, it's hard to find people the talent which is, you know, which has expertise across different clouds. So that's where we come into the picture. So our vision was always to simplify all of this stuff. And simplification, it cannot be just simplification because you cannot just automate the workflows of the cloud providers underneath. So you have to, you know, provide your full data plane on top of it, fed full control plane, management plane, policy and management on top of it. And coming back to like your question, so these nowadays, those people who are working on networking, you know, before it used to be like CLI. You used to learn about Cisco CLI or Juniper CLI, and you used to work on it. Nowadays, it's very different. So automation, programmability, all of that stuff is the key. So now, you know, Ops guys, the DevOps guys, so these are the people who are in high demand. >> So what do you think about the folks out there that are saying, okay, you got a lot of fragmentation. I got the stacks, I got a lot of stove pipes, if you will, out there on the stack. I got to learn this from Azure. Can you guys have with your product abstract the way that's so developers don't need to know the ins and outs of stack's, almost like a gateway, if you will, the old days. But like I'm a developer or team develop, why should I have to learn the management layer of Azure? >> That's exactly what we started, you know, out with to solve. So it's, what we have built is a platform and the platform sits inside the cloud. And customers are able to build their own network or a virtual network on top using that platform. So the platform has its own data plane, own control plane and management plane with a policy layer on top of it. So now, it's the platform which is sitting in different clouds, but from a customer's point of view, it's one way of doing networking. One way of instantiating or bringing in services or security services in the middle. Whether those are our security services or whether those are like services from our partners, like Palo Alto or Checkpoint or Cisco. >> So you guys brought the SD-WAN mojo and refactored it for the cloud it sounds like. >> No. >> No? (chuckles) >> We cannot said. >> All right, explain. >> It's way more than that. >> I mean, SD-WAN was wan. I mean, you're talking about wide area networks, talking about connected, so explain the difference. >> SD-WAN was primarily done for one major reason. MPLS was expensive, very strong SLAs, but very low speed. Internet, on the other hand, you sat at home and you could access your applications much faster. No SLA, very low cost, right? So we wanted to marry the two together so you could have a purely private infrastructure and a public infrastructure and secure both of them by creating a common secure fabric across all those environments. And then seamlessly tying it into your internal branch and data center and cloud network. So, it merely brought you to the edge of the cloud. It didn't do anything inside the cloud. Now, the major problem resides inside the clouds where you have to optimize the clouds themselves. Take a step back. How were the clouds built? Basically, the cloud providers went to the Ciscos and Junipers and the rest of the world, built the network in the data centers or across wide area infrastructure, and brought it all together and tried to create a virtualized layer on top of that. But there were many limitations of this underlying infrastructure that they had built. So number of routes per region, how inter region connectivity worked, or how many routes you could carry to the VPCs of V nets? That all those were becoming no common policy across, you know, these environments, no segmentation across these environments, right? So the networking constructs that the enterprise customers were used to as enterprise class carry class capabilities, they did not exist in the cloud. So what did the customer do? They ended up stitching it together all manually. And that's why Atif was alluding to earlier that it became a spaghetti mess for the customers. And then what happens is, as a result, day two operations, you know, troubleshooting, everything becomes a nightmare. So what do you do? You have to build an infrastructure inside the cloud. Cloud has enough raw capabilities to build the solutions inside there. Netflix's of the world. And many different companies have been born in the cloud and evolved from there. So why could we not take the raw capabilities of the clouds and build a network cloud or a supercloud on top of these clouds to optimize the whole infrastructure and seamlessly connecting it into the on-premise and remote user locations, right? So that's your, you know, hybrid multi-cloud solution. >> Well, great call out on the SD-WAN in common versus cloud. 'Cause I think this is important because you're building a network layer in the cloud that spans out so the customers don't have to get into the, there's a gap in the system that I'm used to, my operating environment, of having lockdown security and network. >> So yeah. So what you do is you use the raw capabilities like bandwidth or virtual machines, or you know, containers, or, you know, different types of serverless capabilities. And you bring it all together in a way to solve the networking problems, thereby creating a supercloud, which is an abstraction layer which hides all the complexity of the underlying clouds from the customer, right? And it provides a common infrastructure across all environments to that customer, right? That's the beauty of it. And it does it in a way that it looks like, if they have the networking knowledge, they can apply it to this new environment and carry it forward. One way of doing security across all clouds and hybrid environments. One way of doing routing. One way of doing large-scale network address translation. One way of doing IPAM services. So people are tired of doing individual things and individual clouds and on-premise locations, right? So now they're getting something common. >> You guys brought that, you brought all that to bear and flexible for the customer to essentially self-serve their network cloud. >> Yes, yeah. Is that the wave? >> And nowadays, from business perspective, agility is the key, right? You have to move at the pace of the business. If you don't, you are losing. >> So, would it be safe to say that you guys have a network supercloud? >> Absolutely, yeah. >> We, pretty much, yeah. Absolutely. >> What does that mean to our customer? What's in it for them? What's the benefit to the customer? I got a network supercloud, it connects, provides SLA, all the capabilities I need. What do they get? What's the end point for them? What's the end? >> Atif, maybe you can talk some examples. >> The IT infrastructure is all like distributed now, right? So you have applications running in data centers. You have applications running in one cloud. Other cloud, public clouds, enterprises are depending on so many SaaS applications. So now, these are, you can call these endpoints. So a supercloud or a network cloud, from our perspective, it's a cloud in the middle or a network in the middle, which provides connectivity from any endpoint to any endpoint. So, you are able to connect to the supercloud or network cloud in one way no matter where you are. So now, whichever cloud you are in, whichever cloud you need to connect to. And also, it's not just connecting to the cloud. So you need to do a lot of stuff, a lot of networking inside the cloud also. So now, as Amir was saying, every cloud has its own from a networking, you know, the concept perspective or the construct, they are different. There are limitations in there also. So this supercloud, which is sitting on top, basically, your platform is sitting into the cloud, but the supercloud is built on top of using your platform. So that abstracts all those complexities, all those limitations. So now your limitations are whatever the limitations of that platform are. So now your platform, that platform is in our control. So we can keep building it, we can keep scaling it horizontally. Because one of the things is that, you know, in this cloud era, one of the things is autoscaling these services. So why can't the network now autoscale also, just like your other services. >> Network autoscaling is a genius idea, and I think that's a killer. I want to ask the the follow on question because I think, first of all, I love what you guys are doing. So, I think it's a great example of this new innovation. It's not obvious until you see it, right? Geographical is huge. So, you know, single instance, global instances, multiple instances, you're seeing global. How do you guys look at that global equation? Because as companies expand their clouds into geos, and then ultimately, you know, it's obviously continent, region and locales. You're going to have geographic issues. So, this is an extension of your network cloud? >> Amir: It is the extension of the network cloud because if you look at this hyperscalers, they're sitting pretty much everywhere in the globe. So, wherever their regions are, the beauty of building a supercloud is that you can by definition, be available in those regions. It literally takes a day or two of testing for our stack to run in those regions, to make sure there are no nuances that we run into, you know, for that region. The moment we bring it up in that region, all customers can onboard into that solution. So literally, what used to take months or years to build a global infrastructure, now, you can configure it in 10 minutes basically, and bring it up in less than one hour. Since when did we see any solution- >> And by the way, >> that can come up with. >> when the edge comes out too, you're going to start to see more clouds get bolted on. >> Exactly. And you can expand to the edge of the network. That's why we call cloud the new edge, right? >> John: Yeah, it is. Now, I think you guys got a good solutions, network clouds, superclouds, good. So the question on the premise side, so I get the cloud play. It's very cool. You can expand out. It's a nice layer. I'm sure you manage the SLAs between latency and all kinds of things. Knowing when not to do things. Physics or physics. Okay. Now, you've got the on-premise. What's the on-premise equation look like? >> So on-premise, the kind of customers, we are working with large enterprises, mid-size enterprises. So they have on-prem networks, they have deployed, in many cases, they have deployed SD-WAN. In many cases, they have MPLS. They have data centers also. And a lot of these companies are, you know, moving the applications from the data center into the cloud. But we still have large enterprise- >> But for you guys, you can sit there too with non server or is it a box or what is it? >> It's a software stack, right? So, we are a software company. >> Okay, so no box. >> No box. >> Okay, got it. >> No box. >> It's even better. So, we can connect any, as I mentioned, any endpoint, whether it's data centers. So, what happens is usually these enterprises from the data centers- >> John: It's a cloud endpoint for you. >> Cloud endpoint for us. And they need highspeed connectivity into the cloud. And our network cloud is sitting inside the or supercloud is sitting inside the cloud. So we need highspeed connectivity from the data centers. This is like multi-gig type of connectivity. So we enable that connectivity as a service. And as Amir was saying, you are able to bring it up in minutes, pretty much. >> John: Well, you guys have a great handle on supercloud. I really appreciate you guys coming on. I have to ask you guys, since you have so much experience in the industry, multiple inflection points you've guys lived through and we're all old, and we can remember those glory days. What's the big deal going on right now? Because you can connect the dots and you can imagine, okay, like a Lambda function spinning up some connectivity. I need instant access to a new route, throw some, I need to send compute to an edge point for process data. A lot of these kind of ad hoc services are going to start flying around, which used to be manually configured as you guys remember. >> Amir: And that's been the problem, right? The shadow IT, that was the biggest problem in the enterprise environment. So that's what we are trying to get the customers away from. Cloud teams came in, individuals or small groups of people spun up instances in the cloud. It was completely disconnected from the on-premise environment or the existing IT environment that the customer had. So, how do you bring it together? And that's what we are trying to solve for, right? At a large scale, in a carrier cloud center (indistinct). >> What do you call that? Shift right or shift left? Shift left is in the cloud native world security. >> Amir: Yes. >> Networking and security, the two hottest areas. What are you shifting? Up or down? I mean, the network's moving up the stack. I mean, you're seeing the run times at Kubernetes later' >> Amir: Right, right. It's true we're end-to-end virtualization. So you have plumbing, which is the physical infrastructure. Then on top of that, now for the first time, you have true end-to-end virtualization, which the cloud-like constructs are providing to us. We tried to virtualize the routers, we try to virtualize instances at the server level. Now, we are bringing it all together in a truly end-to-end virtualized manner to connect any endpoint anywhere across the globe. Whether it's on-premise, home, multiple clouds, or SaaS type environments. >> Yeah. If you talk about the technical benefits beyond virtualizations, you kind of see in virtualization be abstracted away. So you got end-to-end virtualization, but you don't need to know virtualization to take advantage of it. >> Exactly. Exactly. >> What are some of the tech involved where, what's the trend around on top of virtual? What's the easy button for that? >> So there are many, many use cases from the customers and they're, you know, some of those use cases, they used to deliver out of their data centers before. So now, because you, know, it takes a long time to spend something up in the data center and stuff. So the trend is and what enterprises are looking for is agility. And to achieve that agility, they are moving those services or those use cases into the cloud. So another technical benefit of like something like a supercloud and what we are doing is we allow customers to, you know, move their services from existing data centers into the cloud as well. And I'll give you some examples. You know, these enterprises have, you know, tons of partners. They provide connectivity to their partners, to select resources. It used to happen inside the data center. You would bring in connectivity into the data center and apply like tons of ACLs and whatnot to make sure that you are able to only connect. And now those use cases are, they need to be enabled inside the cloud. And the customer's customers are also, it's not just coming from the on-prem, they're coming from the cloud as well. So, if they're coming from the cloud as well as from on-prem, so you need like an infrastructure like supercloud, which is sitting inside the cloud and is able to handle all these use cases. So all of these use cases have to be, so that requires like moving those services from the data center into the cloud or into the supercloud. So, they're, oh, as we started building this service over the last four years, we have come across so many use cases. And to deliver those use cases, you have to have a platform. So you have to have your own platform because otherwise you are depending on somebody else's, you know, capabilities. And every time their capabilities change, you have to change. >> John: I'm glad you brought up the platform 'cause I want to get your both reaction to this. So Bob Muglia just said on theCUBE here at Supercloud, that supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. So the question is, is supercloud a platform or an architecture in your view? >> That's an interesting view on things, you know? I mean, if you think of it, you have to design or architect a solution before we turn it into a platform. >> John: It's a trick question actually. >> So it's a, you know, so we look at it as that you have to have an architectural approach end to end, right? And then you build a solution based on that approach. So, I don't think that they are mutually exclusive. I think they go hand in hand. It's an architecture that you turn into a solution and provide that agility and high availability and disaster recovery capability that it built into that. >> It's interesting that these definitions might be actually redefined with this new configuration. >> Amir: Yes. >> Because architecture and platform used to mean something, like, aight here's a platform, you buy this platform. >> And then you architecture solution. >> Architect it via vendor. >> Right, right, right. >> Okay. And they have to deal with that architecture in the place of multiple superclouds. If you have too many stove pipes, then what's the purpose of supercloud? >> Right, right, right. And because, you know, historically, you built a router and you sold it to the customer. And the poor customer was supposed to install it all, you know, and interconnect all those things. And if you have 40, 50,000 router network, which we saw in our lifetime, 'cause there used to be many more branches when we were growing up in the networking industry, right? You had to create hierarchy and all kinds of things to figure out how to solve that problem. We are no longer living in that world anymore. You cannot deploy individual virtual instances. And that's what approach a lot of people are taking, which is a pure overly network. You cannot take that approach anymore. You have to evolve the architecture and then build the solution based on that architecture so that it becomes a platform which is readily available, highly scalable, and available. And at the same time, it's very, very easy to deploy. It's a SaaS type solution, right? >> So you're saying, do the architecture to get the solution for the platform that the customer has. >> Amir: Yes. >> They're not buying a platform, they end up with a platform- >> With the platform. >> as a result of Supercloud path. All right. So that's what's, so you mentioned, that's a great point. I want to double click on what you just said. 'Cause I like that what you said. What's the deployment strategy in your mind for supercloud? I'm an architect. I'm at an enterprise in the Midwest. I'm an insurance company, got some cloud action going on. I'm mostly on-premise. I've got the mandate to transform the company. We have apps. We'll be fully transformed in five years. What's my strategy? What do I do? >> Amir: The resources. >> What's the deployment strategy? Single global instance, code in every region, on every cloud? >> It needs to be a solution which is available as a SaaS service, right? So from the customer's perspective, they are onboarding into the supercloud. And then the supercloud is allowing them to do whatever they used to do, you know, historically and in the new world, right? That needs to come together. And that's what we have built is that, we have brought everything together in a way that what used to take months or years, and now taking an hour or two hours, and then people test it for a week or so and deploy it in production. >> I want to bring up something we were talking about before we were on camera about the TCP/IP, the OSI model. That was a concept that destroyed the proprietary narcissist. Work operating systems of the mini computers, which brought in an era of tech prosperity for generations. TCP/IP was kind of the magical moment that allowed for that kind of super networking connection. Inter networking is what's called as a category. It feels like something's going on here with supercloud. The way you describe it, it feels like there's this unification idea. Like the reality is we've got multiple stuff sitting around by default, you either clean it up or get rid of it, right? Or it's almost a, it's either a nuance, a new nuisance or chaos. >> Yeah. And we live in the new world now. We don't have the luxury of time. So we need to move as fast as possible to solve the business problems. And that's what we are running into. If we don't have automated solutions which scale, which solve our problems, then it's going to be a problem. And that's why SaaS is so important in today's world. Why should we have to deploy the network piecemeal? Why can't we have a solution? We solve our problem as we move forward and we accomplish what we need to accomplish and move forward. >> And we don't really need standards here, dude. It's not that we need a standards body if you have unification. >> So because things move so fast, there's no time to create a standards body. And that's why you see companies like ours popping up, which are trying to create a common infrastructure across all clouds. Otherwise if we vent the standardization path may take long. Eventually, we should be going in that direction. But we don't have the luxury of time. That's what I was trying to get to. >> Well, what's interesting is, is that to your point about standards and ratification, what ratifies a defacto anything? In the old days there was some technical bodies involved, but here, I think developers drive everything. So if you look at the developers and how they're voting with their code. They're instantly, organically defining everything as a collective intelligence. >> And just like you're putting out the paper and making it available, everybody's contributing to that. That's why you need to have APIs and terra form type constructs, which are available so that the customers can continue to improve upon that. And that's the Net DevOps, right? So that you need to have. >> What was once sacrilege, just sayin', in business school, back in the days when I got my business degree after my CS degree was, you know, no one wants to have a better mousetrap, a bad business model to have a better mouse trap. In this case, the better mouse trap, the better solution actually could be that thing. >> It is that thing. >> I mean, that can trigger, tips over the industry. >> And that that's where we are seeing our customers. You know, I mean, we have some publicly referenceable customers like Coke or Warner Music Group or, you know, multiple others and chart industries. The way we are solving the problem. They have some of the largest environments in the industry from the cloud perspective. And their whole network infrastructure is running on the Alkira infrastructure. And they're able to adopt new clouds within days rather than waiting for months to architect and then deploy and then figure out how to manage it and operate it. It's available as a service. >> John: And we've heard from your customer, Warner, they were just on the program. >> Amir: Yes. Okay, okay. >> So they're building a supercloud. So superclouds aren't just for tech companies. >> Amir: No. >> You guys build a supercloud for networking. >> Amir: It is. >> But people are building their own superclouds on top of all this new stuff. Talk about that dynamic. >> Healthcare providers, financials, high-tech companies, even startups. One of our startup customers, Tekion, right? They have these dealerships that they provide sales and support services to across the globe. And for them to be able to onboard those dealerships, it is 80% less time to production. That is real money, right? So, maybe Atif can give you a lot more examples of customers who are deploying. >> Talk about some of the customer activity. What are they like? Are they laggards, they innovators? Are they trying to hit the easy button? Are they coming in late or are you got some high customers? >> Actually most of our customers, all of our customers or customers in general. I don't think they have a choice but to move in this direction because, you know, the cloud has, like everything is quick now. So the cloud teams are moving faster in these enterprises. So now that they cannot afford the network nor to keep up pace with the cloud teams. So, they don't have a choice but to go with something similar where you can, you know, build your network on demand and bring up your network as quickly as possible to meet all those use cases. So, I'll give you an example. >> John: So the demand's high for what you guys do. >> Demand is very high because the cloud teams have- >> John: Yeah. They're going fast. >> They're going fast and there's no stopping. And then network teams, they have to keep up with them. And you cannot keep deploying, you know, networks the way you used to deploy back in the day. And as far as the use cases are concerned, there are so many use cases which our customers are using our platform for. One of the use cases, I'll give you an example of these financial customers. Some of the financial customers, they have their customers who they provide data, like stock exchanges, that provide like market data information to their customers out of data centers part. But now, their customers are moving into the cloud as well. So they need to come in from the cloud. So when they're coming in from the cloud, you cannot be giving them data from your data center because that takes time, and your hair pinning everything back. >> Moving data is like moving, moving money, someone said. >> Exactly. >> Exactly. And the other thing is like you have to optimize your traffic flows in the cloud as well because every time you leave the cloud, you get charged a lot. So, you don't want to leave the cloud unless you have to leave the cloud, your traffic. So, you have to come up or use a service which allows you to optimize all those traffic flows as well, you know? >> My final question to you guys, first of all, thanks for coming on Supercloud Program. Really appreciate it. Congratulations on your success. And you guys have a great positioning and I'm a big fan. And I have to ask, you guys are agile, nimble startup, smart on the cutting edge. Supercloud concept seems to resonate with people who are kind of on the front range of this major wave. While all the incumbents like Cisco, Microsoft, even AWS, they're like, I think they're looking at it, like what is that? I think it's coming up really fast, this trend. Because I know people talk about multi-cloud, I get that. But like, this whole supercloud is not just SaaS, it's more going on there. What do you think is going on between the folks who get it, supercloud, get the concept, and some are who are scratching their heads, whether it's the Ciscos or someone, like I don't get it. Why is supercloud important for the folks that aren't really seeing it? >> So first of all, I mean, the customers, what we saw about six months, 12 months ago, were a little slower to adopt the supercloud kind of concept. And there were leading edge customers who were coming and adopting it. Now, all of a sudden, over the last six to nine months, we've seen a flurry of customers coming in and they are from all disciplines or all very diverse set of customers. And they're starting to see the value of that because of the practical implications of what they're doing. You know, these shadow IT type environments are no longer working and there's a lot of pressure from the management to move faster. And then that's where they're coming in. And perhaps, Atif, if you can give a few examples of. >> Yeah. And I'll also just add to your point earlier about the network needing to be there 'cause the cloud teams are like, let's go faster. And the network's always been slow because, but now, it's been almost turbocharged. >> Atif: Yeah. Yeah, exactly. And as I said, like there was no choice here. You had to move in this industry. And the other thing I would add a little bit is now if you look at all these enterprises, most of their traffic is from, even from which is coming from the on-prem, it's going to the cloud SaaS applications or public clouds. And it's more than 50% of traffic, which is leaving your, you know, what you used to call, your network or the private network. So now it's like, you know, before it used to just connect sites to data centers and sites together. Now, it's a cloud as well as the SaaS application. So it's either internet bound or the public cloud bound. So now you have to build a network quickly, which caters to all these use cases. And that's where like something- >> And you guys, your solution to me is you eliminate all that work for the customer. Now, they can treat the cloud like a bag of Legos. And do their thing. Well, I oversimplify. Well, you know I'm talking about. >> Atif: Right, exactly. >> And to answer your question earlier about what about the big companies coming in and, you know, now they slow to adopt? And, you know, what normally happens is when Cisco came up, right? There used to be 16 different protocols suites. And then we finally settled on TCP/IP and DECnet or AppleTalk or X&S or, you know, you name it, right? Those companies did not adapt to the networking the way it was supposed to be done. And guess what happened, right? So if the companies in the networking space do not adopt this new concept or new way of doing things, I think some of them will become extinct over time. >> Well, I think the force and function too is the cloud teams as well. So you got two evolutions. You got architectural relevance. That's real as impact. >> It's very important. >> Cost, speed. >> And I look at it as a very similar disruption to what Cisco's the world, very early days did to, you know, bring the networking out, right? And it became the internet. But now we are going through the cloud. It's the cloud era, right? How does the cloud evolve over the next 10, 15, 20 years? Everything's is going to be offered as a service, right? So slowly data centers go away, the network becomes a plumbing thing. Very, you know, simple to deploy. And everything on top of that is virtualized in the cloud-like manners. >> And that makes the networks hardened and more secure. >> More secure. >> It's a great way to be secure. You remember the glory days, we'll go back 15 years. The Cisco conversation was, we got to move up to stack. All the manager would fight each other. Now, what does that actually mean? Stay where we are. Stay in your lane. This is kind of like the network's version of moving up the stack because not so much up the stack, but the cloud is everywhere. It's almost horizontally scaled. >> It's extending into the on-premise. It is already moving towards the edge, right? So, you will see a lot- >> So, programmability is a big program. So you guys are hitting programmability, compatibility, getting people into an environment they're comfortable operating. So the Ops people love it. >> Exactly. >> Spans the clouds to a level of SLA management. It might not be perfectly spanning applications, but you can actually know latencies between clouds, measure that. And then so you're basically managing your network now as the overall infrastructure. >> Right. And it needs to be a very intelligent infrastructure going forward, right? Because customers do not want to wait to be able to troubleshoot. They don't want to be able to wait to deploy something, right? So, it needs to be a level of automation. >> Okay. So the question for you guys both on we'll end on is what is the enablement that, because you guys are a disruptive enabler, right? You create this fabric. You're going to enable companies to do stuff. What are some of the things that you see and your customers might be seeing as things that they're going to do as a result of having this enablement? So what are some of those things? >> Amir: Atif, perhaps you can talk through the some of the customer experience on that. >> It's agility. And we are allowing these customers to move very, very quickly and build these networks which meet all these requirements inside the cloud. Because as Amir was saying, in the cloud era, networking is changing. And if you look at, you know, going back to your comment about the existing networking vendors. Some of them still think that, you know, just connecting to the cloud using some concepts like Cloud OnRamp is cloud networking, but it's changing now. >> John: 'Cause there's apps that are depending upon. >> Exactly. And it's all distributed. Like IT infrastructure, as I said earlier, is all distributed. And at the end of the day, you have to make sure that wherever your user is, wherever your app is, you are able to connect them securely. >> Historically, it used to be about building a router bigger and bigger and bigger and bigger, you know, and then interconnecting those routers. Now, it's all about horizontal scale. You don't need to build big, you need to scale it, right? And that's what cloud brings to the customer. >> It's a cultural change for Cisco and Juniper because they have to understand that they're still could be in the game and still win. >> Exactly. >> The question I have for you, what are your customers telling you that, what's some of the anecdotal, like, 'cause you guys have a good solution, is it, "Oh my god, you guys saved my butt." Or what are some of the commentary that you hear from the customers in terms of praise and and glory from your solution? >> Oh, some even say, when we do our demo and stuff, they say it's too hard to believe. >> Believe. >> Like, too hard. It's hard, you know, it's >> I dont believe you. They're skeptics. >> I don't believe you that because now you're able to bring up a global network within minutes. With networking services, like let's say you have APAC, you know, on-prem users, cloud also there, cloud here, users here, you can bring up a global network with full routed connectivity between all these endpoints with security services. You can bring up like a firewall from a third party or our services in the middle. This is a matter of minutes now. And this is all high speed connectivity with SLAs. Imagine like before connecting, you know, Singapore to U.S. East or Hong Kong to Frankfurt, you know, if you were putting your infrastructure in columns like E-connects, you would have to go, you know, figure out like, how am I going to- >> Seal line In, connect to it? Yeah. A lot of hassles, >> If you had to put like firewalls in the middle, segmentation, you had to, you know, isolate different entities. >> That's called heavy lifting. >> So what you're seeing is, you know, it's like customer comes in, there's a disbelief, can you really do that? And then they try it out, they go, "Wow, this works." Right? It's deployed in a small environment. And then all of a sudden they start taking off, right? And literally we have seen customers go from few thousand dollars a month or year type deployments to multi-million dollars a year type deployments in very, very short amount of time, in a few months. >> And you guys are pay as you go? >> Pay as you go. >> Pay as go usage cloud-based compatibility. >> Exactly. And it's amazing once they get to deploy the solution. >> What's the variable on the cost? >> On the cost? >> Is it traffic or is it. >> It's multiple different things. It's packaged into the overall solution. And as a matter of fact, we end up saving a lot of money to the customers. And not only in one way, in multiple different ways. And we do a complete TOI analysis for the customers. So it's bandwidth, it's number of connections, it's the amount of compute power that we are using. >> John: Similar things that they're used to. >> Just like the cloud constructs. Yeah. >> All right. Networking supercloud. Great. Congratulations. >> Thank you so much. >> Thanks for coming on Supercloud. >> Atif: Thank you. >> And looking forward to seeing more of the demand. Translate, instant networking. I'm sure it's going to be huge with the edge exploding. >> Oh yeah, yeah, yeah, yeah. >> Congratulations. >> Thank you so much. >> Thank you so much. >> Okay. So this is Supercloud 2 event here in Palo Alto. I'm John Furrier. The network Supercloud is here. Checkout Alkira. I'm John Furry, the host. Thanks for watching. (lively music)

>> TheCUBE presents HPE Discover 2022 brought to you by HPE. >> Welcome back to theCUBE's coverage of HPE Discover 2022. We're here at the Venetian convention center in Las Vegas Dave Vellante for John Furrier. Cam Amirs here is the director of technical alliances at Cribl'. Cam, good to see you. >> Good to see you too. >> Cribl'. Cool name. Tell us about it. >> So let's see. Cribl' has been around now for about five years selling products for the last two years. Fantastic company, lots of growth, started there 2020 and we're roughly 400 employees now. >> And what do you do? Tell us more. >> Yeah, sure. So I run the technical alliances team and what we do is we basically look to build integrations into platforms such as HPE GreenLake and Ezmeral. And we also work with a lot of other companies to help get data from various sources into their destinations or, you know other enrichments of data in that data pipeline. >> You know, you guys have been on theCUBE. Clint's been on many times, Ed Bailey was on our startup showcase. You guys are successful in this overfunded observability space. So, so you guys have a unique approach. Tell us about why you guys are successful in the product and some of the things you've been doing there. >> Yeah, absolutely. So our product is very complimentary to a lot of the technologies that already exist. And I used to joke around that everyone has these like pretty dashboards and reports but they completely glaze over the fact that it's not easy to get the data from those sources to their destinations. So for us, it's this capability with Cribl' Stream to get that data easily and repeatably into these destinations. >> Yeah. You know, Cam, you and I are both at the Snowflake Summit to John's point. They were like a dozen observability companies there. >> Oh yeah. >> And really beginning to be a crowded space. So explain what value you bring to that ecosystem. >> Yeah, sure. So the ecosystem that we see there is there are a lot of people that are kind of sticking to like effectively getting data and showing you dashboards reports about monitoring and things of that sort. For us, the value is how can we help customers kind of accelerate their adoption of these platforms, how to go from like your legacy SIM or your legacy monitoring solution to like the next-gen observability platform or next-gen security platform >> and what you do really well is the integration and bringing those other toolings to, to do that? >> Correct, correct. And we make it repeatable. >> How'd you end up here? >> HP? So we actually had a customer that actually deployed our software on the HPS world platform. And it was kind of a light bulb moment that, okay this is actually a different approach than going to your traditional, you know, AWS, Google, et cetera. So we decided to kind of hunt this down and figure out how we could be a bigger player in this space. >> You saw the data fabric announcement? I'm not crazy about the term, data fabric is an old NetApp term, and then Gartner kind of twisted it. I like data mesh, but anyway, it doesn't matter. We kind of know what it is, but but when you see an announcement like that how do you look at it? You know, what does it mean to to Cribl' and your customers? >> Yeah. So what we've seen is that, so we work with the data fabric team and we're able to kind of route our data to their, as a data lake, so we can actually route the data from, again all these very sources into this data lake and then have it available for whatever customers want to do with it. So one of the big things that I know Clint talks about is we give customers this, we sell choice. So we give them the ability to choose where they want to send their data, whether that's, you know HP's data lake and data fabric or some other object store or some other destination. They have that choice to do so. >> So you're saying that you can stream with any destination the customer wants? What are some examples? What are the popular destinations? >> Yeah so a lot of the popular destinations are your typical object stores. So any of your cloud object stores, whether it be AWS three, Google cloud storage or Azure blob storage. >> Okay. And so, and you can pull data from any source? >> Laughter: I'd be very careful, but absolutely. What we've seen is that a lot of people like to kind of look at traditional data sources like Syslog and they want to get it to us, a next-gen SIM, but to do so it needs to be converted to like a web hook or some sort of API call. And so, or vice versa, they have this brand new Zscaler for example, and they want to get that data into their SIM but there's no way to do it 'cause a SIM only accepts it as a Syslog event. So what we can do is we actually transform the data and make it so that it lands into that SIM in the format that it needs to be and easily make that a repeatable process >> So, okay. So wait, so not as a Syslog event but in whatever format the destination requires? >> Correct, correct. >> Okay. What are the limits on that? I mean, is this- >> Yeah. So what we've seen is that customers will be able to take, for example they'll take this Syslog event, it's unstructured data but they need to put it into say common information model for Splunk or Elastic common schema for Elastic search or just JSON format for Elastic. And so what we can do is we can actually convert those events so that they land in that transformed state, but we can also route a copy of that event in unharmed fashion, to like an S3 bucket for object store for that long term compliance user >> You can route it to any, basically any object store. Is that right? Is that always the sort of target? >> Correct, correct. >> So on the message here at HPE, first of all I'll get to the marketplace point in a second, but it's cloud to edge is kind of their theme. So data streaming sounds expensive. I mean, you know so how do you guys deal with the streaming egress issue? What does that mean to customers? You guys claim that you can save money on that piece. It's a hotly contested discussion point. >> Laughter: So one of the things that we actually just announced in our 350 release yesterday is the capability of getting data from Windows events, or from Windows hosts, I'm sorry. So a product that we also have is called Cribl' Edge. So our capability of being able to collect data from the edge and then transit it out to whether it be an on-prem, or self-hosted deployment of Cribl', or or maybe some sort of other destination object store. What we do is we actually take the data in in transit and reduce the volume of events. So we can do things like remove white space or remove events that are not really needed and compress or optimize that data so that the egress cost to your point are actually lowered. >> And your data reduction approach is, is compression? It's a compression algorithm? >> So it is a combination, yeah, so it's a combination. So there's some people what they'll do is they'll aggregate the events. So sometimes for example, VPC flow logs are very chatty and you don't need to have all those events. So instead you convert those to metrics. So suddenly you reduced those events from, you know high volume events to metrics that are so small and you still get the same value 'cause you still see the trends and everything. And if later on down the road, you need to reinvestigate those events, you can rehydrate that data with Cribl' replay >> And you'll do the streaming in real time, is that right? >> Yeah. >> So Kafka, is that what you would use? Or other tooling? >> Laughter: So we are complimentary to a Kafka deployment. Customer's already deployed and they've invested in Kafka, We can read off of Kafka and feed back into Kafka. >> If not, you can use your tooling? >> If not, we can be replacing that. >> Okay talk about your observations in the multi-cloud hybrid world because hybrid obviously everyone knows it's a steady state now. On public cloud, on premise edge all one thing, cloud operations, DevOps, data as code all the things we talk about. What's the customer view? You guys have a unique position. What's going on in the customer base? How are they looking at hybrid and specifically multi-cloud, is it stitching together multiple hybrids? Or how do you guys work across those landscapes? >> So what we've seen is a lot of customers are in multiple clouds. That's, you know, that's going to happen. But what we've seen is that if they want to egress data from say one cloud to another the way that we've architected our solution is that we have these worker nodes that reside within these hybrid, these other cloud event these other clouds, I should say so that transmitting data, first egress costs are lowered, but being able to have this kind of, easy way to collect the data and also stitch it back together, join it back together, to a single place or single location is one option that we offer customers. Another solution that we've kind of announced recently is Search. So not having to move the data from all these disparate data sources and data lakes and actually just search the data in place. That's another capability that we think is kind of popular in this hybrid approach. >> And talk about now your relationship with HPE you guys obviously had customers that drove you to Greenlake, obviously what's your experience with them and also talk about the marketplace presence. Is that new? How long has that been going on? Have you seen any results? >> Yeah, so we've actually just started our, our journey into this HPE world. So the first thing was obviously the customer's bringing us into this ecosystem and now our capabilities of, I guess getting ready to be on the marketplace. So having a presence on the marketplace has been huge giving us kind of access to just people that don't even know who we are, being that we're, you know a five year old company. So it's really good to have that exposure. >> So you're going to get customers out of this? >> That's the idea. [Laughter] >> Bring in new market, that's the idea of their GreenLake is that partners fill in. What's your impression so far of GreenLake? Because there seems to be great momentum around HP and opening up their channel their sales force, their customer base. >> Yeah. So it's been very beneficial for us, again being a smaller company and we are a channel first company so that obviously helps, you know bring out the word with other channel partners. But HP has been very, you know open arm kind of getting us into the system into the ecosystem and obviously talking, or giving the good word about Cribl' to their customers. >> So, so you'll be monetizing on GreenLake, right? That's the, the goal. >> That's the goal. >> What do you have to do to get into a position? Obviously, you got a relationship you're in the marketplace. Do you have to, you know, write to their API's or do you just have to, is that a checkbox? Describe what you have to do to monetize. >> Sure. So we have to first get validated on the platform. So the validation process validates that we can work on the Ezmeral GreenLake platform. Once that's been completed, then the idea is to have our logo show up on the marketplace. So customers say, Hey, look, I need to have a way to get transit data or do stuff with data specifically around logs, metrics, and traces into my logging solution or my SIM. And then what we do with them on the back end is we'll see this transaction occur right to their API to basically say who this customer is. 'Cause again, the idea is to have almost a zero touch kind of involvement, but we will actually have that information given to us. And then we can actually monetize on top of it. >> And the visualization component will come from the observability vendor. Is that right? Or is that somewhat, do you guys do some of that? >> So the visualization is right now we're basically just the glue that gets the data to the visualization engine. As we kind of grow and progress our search product that's what will probably have more of a visualization component. >> Do you think your customers are going to predominantly use an observability platform for that visualization? I mean, obviously you're going to get there. Are they going to use Grafana? Or some other tool? >> Or yeah, I think a lot of customers, obviously, depending on what data and what they're trying to accomplish they will have that choice now to choose, you know Grafana for their metrics, logs, et cetera or some sort of security product for their security events but same data, two different kind of use cases. And we can help enable that. >> Cam, I want to ask you a question. You mentioned you were at Splunk and Clint, the CEO and co-founder, was at Splunk too. That brings up the question I want to get your perspective on, we're seeing a modern network here with HPE, with Aruba, obviously clouds kind of going next level you got on premises, edge, all one thing, distributed computing basically, cyber security, a data problem that's solved a lot by you guys and people in this business, making sure data available machine learnings are growing and powering AI like you read about. What's changed in this business? Because you know, Splunking logs is kind of old hat you know, and now you got observability. Unification is a big topic. What's changed now? What's different about the market today around data and these platforms and, and tools? What's your perspective on that? >> I think one of the biggest things is people have seen the amount of volume of data that's coming in. When I was at Splunk, when we hit like a one terabyte deal that was a big deal. Now it's kind of standard. You're going to do a terabyte of data per day. So one of the big things I've seen is just the explosion of data growth, but getting value out of that data is very difficult. And that's kind of why we exist because getting all that volume of data is one thing. But being able to actually assert value from it, that's- >> And that's the streaming core product? That's the whole? >> Correct. >> Get data to where it needs to be for whatever application needs whether it's cyber or something else. >> Correct, correct. >> What's the customer uptake? What's the customer base like for you guys now? How many, how many customers you guys have? What are they doing with the data? What are some of the common things you're seeing? >> Yeah. I mean, it's, it's the basic blocking and tackling, we've significantly grown our customer base and they all have the same problem. They come to us and say, look, I just need to get data from here to there. And literally the routing use case is our biggest use case because it's simple and you take someone that's a an expensive engineer and operations engineer instead of having them going and doing the plumbing of data of just getting logs from one source to another, we come in and actually make that a repeatable process and make that easy. And so that's kind of just our very basic value add right from the get go. >> You can automate that, automate that, make it repeatable. Say what's in the name? Where'd the name come from? >> So Cribl', if you look it up, it's actually kind of an old shiv to get to siphon dirt from gold, right? So basically you just, that's kind of what we do. We filter out all the dirt and leave you the gold bits so you can get value. >> It's kind of what we do on theCUBE. >> It's kind of the gold nuggets. Get all these highlights, hitting Twitter, the golden, the gold nuggets. Great to have you on. >> Cam, thanks for, for coming on, explaining that sort of you guys are filling that gap between, Hey all the observability claims, which are all wonderful but then you got to get there. They got to have a route to get there. That's what got to do. Cribl' rhymes with tribble. Dave Vellante for John Furrier covering HPE Discover 2022. You're watching theCUBE. We'll be right back.

>>mhm. What the special cube conversation here in Palo alto, I'm john Kerry host of the cube. We're here talking about kubernetes Cloud native and all things Cloud, cloud enterprise amir Sure VP of product and morgan Stanley is with me and we are great to have you on the cube. Thanks for coming on. I appreciate you taking the time, >>appreciate it, john good to be here. You >>know, cloud Native obviously super hot right now as the edges around the corner, you're seeing people looking at five G looking at amazon's wavelength outposts you've got as you got a lot of cloud companies really pushing distributed computing and I think one of the things that people really are getting into is okay, how do I take the cloud and re factor my business and then that's one business side then, the technical side. Okay, How do I do it? Like it's not that easy. Right. So it sounds, it sounds really easy to just go to move to the cloud. This is something that's been a big problem. So I know you guys in the center of all this uh and you've got, you know, microservices, kubernetes at the core of this, take a minute to introduce the company, what you guys do then I want to get into some specific questions. >>Mhm, of course. Well, bob Sani is a startup? Silicon Valley startup and what we do is automate system configuration that's typically worked at an engineer does and take lengthy and if done incorrectly at least to a lot of errors and cost overruns and the user experience problems. We completely automate that using an Ai and ml back end so that the engineering can focus on writing code and not worry about having to tune the little pieces working together. >>You know, I love the, I was talking to a V. C on our last uh startup showcase, cloud startup showcase and uh really prominent VC and he was talking about down stack up stack benefits and he says if you're going to be a down stack um, provider, you got to solve a problem. It has to be a big problem that people don't want to deal with. So, and you start getting into some of the systems configuration when you have automation at the center of this as a table stakes item problems are cropping up as new use cases are emerging. Can you talk about some of the problems that you guys see that you solve for developers and companies, >>of course. So they're basically, they're, the problem expresses itself in a number of domains. The first one is that he who pays the bills is separate from he who consumes the resources. It's the engineers that consume the resources and the incentives are to deliver code rapidly and deliver code that works well, but they don't really care about paying the bills. And then the CFO office sees the bills and there's a disparity between the two. The reason that creates a problem, a business problem is that the developers uh, will over provision stuff, uh to make sure that everything works and uh, they don't want to get caught in the middle of the night. You know, the bill comes due at the end of the month or into the quarter and then the CFO has smoke coming out of his ears because there's been clawed overruns. Then the reaction happens to all right, let's cut costs. And then, you know, there's an edict that comes down that says everything, reduce everything by 30%. So people go across and give a haircut to everything. So what happens next to systems out of balance? There's allocation resource misallocation and uh, systems start uh, suffering. So the customers become unhappy. And ironically, if you're not provisioned correctly, Not ironically, but maybe understandably, customers start suffering and that leads to a revenue problem down the line if you have too many problems unhappy. So you have to be very careful about how you cut costs and how you apportion resources. So both the revenue side is happy and it costs are happy because it all comes down to product experience and what the customers consume. You >>know, that's something that everyone who's done. Cloud development knows, you know, whose fault is it? You know, it's this fall. But now you can actually see the services you leave a switch open or, you know, I'm oversimplifying it. But, you know, you experiment services, you can the bills can just have massive, you know, overruns and then, and then you got to call the cloud company and you gotta call the engineers and say why did you do this? You got to get a refund or or the bad one. Bad apple could ruin it for everyone as you, as you highlighted over the bigger companies. So I have to ask you mean everyone lives this. How do companies have cost overruns? Is their patterns that you see that you guys wrote software 4-1, automate the obvious ones. Is there is there are certain things that you know always happen. Are there areas that have some indications? So why do, first of all, why do companies have cloud cost overruns? >>That's a great question. And let's start with a bit of history where we came from a pre cloud world, you built your own data centers, which means that you have an upfront Capex cost and you spend the money and you were forced to live within the needs that your data center provided. You really couldn't spend anymore. That provided kind of a predictable expenditure bottle it came in big chunks. But you know what, your budget was going to be four years from now, three years from now. And you built for that with the cloud computing, Your consumption is now on on demand basis and it's api enabled. So the developer can just ask for more resources. So without any kind of tools that tell the developer here is x amount of CPU or X amount of memory that you need for this particular service, that for it to deliver the right uh, performance that for the customer. The developers incentivized to basically give it a lot more than the application needs. Why? Because the developer doesn't want to pick up service tickets. He's incentivizing delivering functionality quickly and moving on to next project, not in optimizing costs. So that creates kind of uh an agency problem that the guy that actually controls how research are consumed is not incentivized to control the consumption of these resources. And we see that across the board in every company, engineers, engineering organization is a separate organization than the financial organization. So the control place is different. The consumption place and it breaks down the patterns are over provisions. And what we want to do is give engineers the tools to consume precisely the right amount of resources for the service level objectives that they have, given that you want a transaction rate of X and the literacy rate of Why here's how you configure your cloud infrastructure. So the application delivers according to the sls with the least possible resources consumed. >>So on this tool you guys have in the software you guys have, how how do you guys go to mark with that, you target the business buyer or the developer themselves and and how do you handle the developers say, I don't want anyone looking over my shoulder. I'm gonna go, I'm gonna have a blank check to do whatever it takes, um how do you guys roll that out because actually the business benefits are significant controlling the budget, I get that. Um how do you guys rolling this out? How do people engage with you? What's your strategy? >>Right. Are there, is the application owner, is the guy that owns the PML for the application? It tends to be a VP level or a senior director person that owns a SAAS platform and he or she is responsible for delivering good products to the market and delivering good financial results to the CFO So in that person of everything is rolled up, but that person will always favor the revenue site, which means consume more resources than you need in order to maximize customer happiness, therefore faster growth and uh they do that while sacrificing the cost side. So by giving the product owner the optimization tools autonomous of optimization tools that Sandy has, we allow him or her to deliver the right experience to the customer, with the right sufficient resources and address both the performance and the cost side of equation simultaneously, >>awesome. Can you talk about the impact c I C D s having in the cloud native computing on the optimization cycle? Um Obviously, you know, shifting left for security, we hear a lot of that, you're hearing a lot of more microservices being spun up, spun down automatically. Uh I'll see kubernetes clusters are going mainstream, you start to see a lot more dynamic uh activity if you if you in these new workflows, what is the impact of these new CSC D cloud? Native computing on the optimization cycle? >>C i c D is there to enable a fast delivery of software features basically. Uh So, you know, we have a combination of get get ups where you can just pull down repositories, libraries, open source projects from left and right. And using glue code, developers can deliver functionality really quick. In fact, microservices are there in service of that capability, deliver functionality quickly by being able to build functional blocks and then through a piece you put everything together. So ci cd is just accelerates the software delivery code. Between the time the boss says, give me an application until the application team plus the devops team plus SRE team puts it out in production. Now we can do this really quickly. The problem is though, nobody optimizes in the process. So when we deliver 1.0 in six months or less, we've done zero in terms of optimization and at one point, oh, becomes a way that we go through QA in many cases, unfortunately. And it also becomes a way that we go through the optimization. The customer screams that you eyes Laghi, you know, the throughput is really slow and we tinker and tinker and tinker and by the time it typically goes through a 12 month cycle of maturation, we get that system stability in the right performance with a I and machine learning that a person has enabled. We can deliver that, we can shrink that time out considerably. In fact, uh you know what we're going to announce in q khan is something that be called Kite storm is the ability to uh install our product and kubernetes environment in roughly 20 minutes and within two days you get the results. So before you have this optimization cycle that was going on for a very long time now that it's frank down and because of Ci Cd, you know, you don't have the luxury of waiting and the system itself can become part of the way of contributing system. The system being the uh ai ml service, that the presiding deliveries can be uh part and parcel of the Ci cd pipeline, that optimizes the code and gives you the right configuration and you get to go. So >>you guys are really getting down and injecting in some uh instrumentation for metadata around key areas. That right. Is that kind of how it's working? Are you getting in there with codes going to watch? Um how was it working under the hood? Can you just give me a quick example of, you know, how this would play out and what people might expect, how it would handle, >>of course. So what the way we optimize application performance is we have to have a metric against which we measure performance. That metric is an S L O service level, objective and in a kubernetes environment, we typically tap into Prometheus, which is the metrics gathering place metrics database for kubernetes workloads and we really focus on red metrics, the rate of transactions, the error rate and the for delay or latency. So we focus on these three metrics and what we have to do is inject a small container, it's an open source container into the application work space that we call that a container. Servo. Servo interacts with Prometheus to get the metrics and then it talks to our back end to tell the M L engine what's happening and then L engine and does this analysis and comes back with a new configuration which then servo implements in a canary instance. So the Canary instances where we run our experiments and we compare it against the main line, Which the application is doing after roughly 20 generations or so. The Bellingen Learns what part of the problem space to focus on in order to optimize to deliver optimal results. And then it very quickly comes to the right set of solutions to try and it tries those inside uh inside the canary instance and when it finds the optimal solution, it gives the recommendation back to the application team or alternatively, when you have enough trust in the tiny you can ought to promote it into mainline that >>gets the learning in there is a great example of some cloud native action. I want to get into some examples with your customer, but before we get there, I want to ask you, since I have you here, if you don't mind, what is cloud native mean these days, because you know, cloud native become kind of much cloud computing, um which essentially go move to the cloud, but as people start developing in the cloud where there's real new benefits, people talk about the word cloud native, could you take a quick minute to define? What is cloud Native, Does that even mean? What does cloud native mean? >>I'll try to give you my understanding government, we could get into a bit of philosophy. Uh Yeah, that's good. But basically cloud Native means it's, your application is built for the cloud and it takes advantages of the inherent benefits that a cloud environment can give you, which means that you can grow and shrink resources on the fly, if you built your application correctly, that you can scale up and scale down, you're a number of instances very quickly and uh, everything has taken advantage of a P I S so initially that was kind of done inside of the environment. Uh AWS Ec two is a perfect example of that. Kubernetes shifted cloud native to container its workload because it allows for rapid, more, rapid deployment and even enables or it takes advantage of a more rapid development cycle as we look forward. Cloud Native is more likely to be a surplus environment where you write functions and the backend systems of the cloud service provider, just give you that capability and you don't have to worry about maintaining and managing a fleet of any sort, whether it's VMS or containers, that's where it's gonna go. Currently we are to contain our space >>so as you start getting into the service molly good land, which we've been playing with, loves that as you get into that, that's going to accelerate more data. So I gotta ask you as you get into more of this this month, I will say monitoring or observe ability, how we want to look at it. You gotta get at the data. This becomes a critical part of solving a lot of problems and also making sure the machine learning is learning the right thing. How do you view that you guys over there? Because I think everyone is like getting that cloud native and it's not hard sell to say that's all good, but we can go back, you know, the expression ships created ships and then you have shipwrecks, you know, there's always a double edged sword here. So what's the downside? If you don't get the data right? >>Uh well, so the for us, the problem is not too much data, it's lack of data. So if you don't get data right is you don't have enough data. And the places where optimization cannot be automated is where the transaction rates are slow, where you don't have enough fruit. But coming into the application and it really becomes difficult to optimize that application with any kind of speed. You have to be able to profile the application long enough to know what moves its needle and in order for you to hit the S. L. O. Targets. So it's not too much data, it's not enough data. That seems to be the problem. And there are a lot of applications that are expensive to run but have a low throughput. And I would uh in all cases actually in every customer environment that have been in, where that's been the case if the application is just over provision, if you have a low throughput environment and it's costing too much, don't use ml to solve it. That's a wrong application of the technology. Just take a sledgehammer and back your resources by 50%, see what happens. And if that thing breaks back it again, until you find the baggage point. >>Exactly for you over prison, you bang it back down again. It's like the old school now with the cloud. Take me through some examples when you guys had some success, obviously you guys are in the right area right now, you're seeing a lot of people looking at this area to do that in some cases like changing the whole data center and respect of their business. But as you get it with customers with the app side, what some successes can you share some of the use cases, what you guys are being successful, your customers can get some examples. >>Yeah. So well known financial software for midsize businesses that that does accounting. It's uh there are customer during a large fleet and this product has been around for a while. It's not a container ice product. This product runs on VMS. Angela is a large component of that. So the problem for this particular vendor has been that they run on heterogeneous fleet that the application has been a along around for a very long time. And as new instance types on AWS have come in, developers have used those. So the fleet itself is quite heterogeneous and depending on the time of the day and what kind of reports are being run by organisations, they, the mix of resources that the applications need are different. So uh when we started analyzing the stack, we started we started looking at three different tiers, we looked at the database level, we looked at the job of mid tier and we looked at the web front end. And uh one of the things that became counterproductive is that m L. Discovered that using for the mid tier using larger instances but fear of a lot for better performance and lower cost and uh typically your gut feel is to go with smaller instances and more of a larger fleet if you would. But in this case, what the ML produced was completely counter intuitive And the net result for the customer was 78% cost reduction while agency went down by 10%. So think about it that you're, the response time is less, uh 10% less but your costs are down almost 80% 78% in this case. And the other are the fact that happened in the job of mitt here is that we improve garbage collection significantly and because whenever garbage collection happens on a JV M it takes a pause and that from a customer perspective it reflects as downtime because the machines are not responding so by tuning garbage collection Andrzej VMS across this very large fleet we were able to recover over 5000 minutes and month across the entire fleet. So uh, these are some substantial savings and this is what the right application of machine learning on a large fleet can do for assess business. >>And so talk about this fleet dynamic, You mentioned several lists. How do you see the future evolving for you guys? Where are you skating to where the puck is? As the expression goes? Um obviously with server list is going to have essentially unlimited fleets potentially That's gonna put a lot of power in the hands of developers. Okay. And people building experiences, What's the next five years look like for you guys? >>So I'm looking at the product from a product perspective, the service market depends on the mercy of the cloud service provider and typically the algorithms that they use. Uh basically they keep very few instances warm for you until you're the rate of api calls goes up and they start they start uh start turning on VMS are containers for you and then the system becomes more responsive over time. One place that we can optimize the service environment is give predictability of what the cyclicality of load is. So we can pre provision those instances and warm up the engine before the loads come into the system always stays responsive. You may have noticed that some of your apps on your phone that when you start them up, they may have a start up like a minute or two. Especially if it's a it's a terror gap. What's happening in those cases that you're starting an api calls goes in containers being started up for you to start up that instance, not enough of our warm to give you that rapid response. And that can lead to customer churn. So by by analyzing what the load on the overall load of the system is and pre provision the system. We can prevent the downtime uh prevent the lag to start up black on the downside. Which when you know when the usage goes down, it doesn't make sense to keep that many instances up. So we can talk to the back in infrastructure and the commission of those VMS in order to make to prevent cost creeps basically. So that's one place that we're thinking about extending our technology. >>So it's like, it's like the classic example where people say, oh during black monday everyone searches to do e commerce. You guys are thinking about it on A level that's a user centric kind of use case where you look at the application and be smart about what the expectation is on any given situation and then flex the resources on that. Is that right? That by getting right? So if it's your example, the app is a good one. If I wanted to load fast, that's the expectation. It better load fast. >>Yes, that's exactly but more romantic. So I use valentine's day and flowers my example. But you know, it doesn't have to be annual cycles. It can be daily cycles or hourly cycles. And all those patterns are learning about by an Ml back in. >>Alright, so I gotta ask you love the, this, this this new concept because most people think auto scaling right? Because that's a server concept. Can auto scale or database. Okay. On a scale up, you're getting down to the point where, okay, we'll keep the engines warm, getting more detailed. How do you explain this versus a concept like auto scaling. Is it the same as a cousins? >>They're they're basically the way they're expressed, it's the same technology but their way there expressed is different. So uh in a cooper native environment, the H. B A is your auto scaler basically in response to the need, response more instances and you get more containers going on. What happens as services? Less environment is you're unaware of the underpinnings that do that scale up for you. But there is an auto Scaler in place that does that scale up for you. So the question becomes that we're in a stack from a customer's perspective, are you talking about if you imagine your instances we're dealing with the H. B. A. If you're managing at the functional level we have to have api calls on the service provider's infrastructure to pre warm up the engine before the load comes. >>I love I love this under the hood is kind of love new dynamics kind of the same wine, new bottle but still computer science, still coding, still cool and relevant to make these experiences great. Thanks for coming on this cube conversation. I really appreciate it. Take a minute to put a plug in for the company. What are you guys doing in terms of status funding scale employees, what are you looking for? And if someone's watching this and there should be a customer of you guys, what what's, what's, what's going on in their world? What tells them that they need to be calling you? >>Yeah, so we're serious. Dave we've had the privilege of uh, our we've been privileged by having a very good success with large enterprises. Uh, if you go to our website, you'll see the logos of who we have, we will be at Q khan and there were going to be actively targeting the mid market or smaller kubernetes instances, as I mentioned, it's gonna take about 20 minutes to get started and we'll show the results in two hours. And our goal is for our customers to deliver the best user experience in terms of performance, reliability. Uh, so that they, they delight their customers in return and they do so without breaking the bank. So deliver excellent products, do it at the most efficient way possible, deliver a good financial results for your stakeholders. This is what we do. So we encourage anybody who is running a SAS company to come and take a look at us because we think we can help them and we can accelerate there. The growth at the lower cost >>and the last thing people need is have someone coming breathing down their necks saying, hey, we're getting overcharged. Why are you guys screwing up when they're not? They're trying to make a great experience. And I think this is kind of where people really want to do push the envelope and not have to go back and revisit the cost overruns, which if it's actually a good sign if you get some cost overruns here and there because you're experimenting. But again, you don't want to get out of control. >>You don't want to be a visual like the U. S. Debt. >>Exactly. I'm here. Thank you for coming on. Great. We'll see a coupe con. The key will be there in person is a hybrid event. So uh, coupon is gonna be awesome and thanks for coming on the key. Appreciate it. >>John is a pleasure. Thank you for having me on. >>Okay. I'm john fryer with acute here in Palo alto California remote interview with upsetting hot startup series. I'm sure they're gonna do well in the right spot in the market. Really well poisoned cloud Native. Thanks for watching. Yeah.

(gentle music) >> From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi, I'm Stu Miniman. And this is a special CUBE conversation. We've been talking a lot, of course for many years about the ascendancy of cloud. And today in 2020, multicloud is a big piece of the discussion. And we're really happy to help unveil coming out of stealth Alkira, which is helping the networking challenges when it comes to multicloud and I have the two co-founders, they are brothers. I have Amir, who is the CEO and Atif, who is the CTO, the Khan brothers, thank you so much for joining us, and congratulation on the launch of the company. >> Thank you Stu for having us on the Show. It's a pleasure to see you again. >> All right, so Amir, we've had you on the program. Your previous company that you've done was of course Viptela, the two of you have worked together at, I believe, five companies, a successful companies. Acquired the most recent one into Cisco. So, Amir, obviously, strong networking team, your brother, the CTO is going to talk to us about the engineering but give us just the story of Alkira, what you've been building and now ready to unveil to the world. >> Certainly, Stu, so when around 2018 timeframe, we started looking into the next big problem to solve in the industry, which was not only a substantial from the market size perspective, but also from the customers perspective was solving a major pain point. So when we started looking into the cloud customers and started talking to our customers, they were struggling from the cloud networking perspective, even in a single cloud, and it was a new environment for them and they had to understand all the nitty gritty details of each one of these clouds and when you go to multicloud environment, it becomes exponentially complicated to address not only connectivity, but how to deploy services like firewall and other services, including load balancers and IP address management, et cetera, and remote access. So we started digging deeper into this problem and started working with the customers and took a clean sheet of paper and came up with a very comprehensive approach to offering a solution which is as-a Service. This time, we are not shipping any hardware software it is just like any other SaaS application, you just come to our portal just drag and drop, literally draw out your network and click on provision. And come back after 40 minutes or so your whole global cloud infrastructure is up and running. >> All right, Atif your brother laid out a pretty broad vision there, any of us from the networking world, we know there's a lot of complexity there. And therefore it takes a lot of work, when I want to do things simply, as-a Service is a huge growth area bring us inside the engineering challenges that you and the team have been working on to build this solution. >> Certainly, Stu, so we've been working both Amir and myself in the networking industry for more than 25 years now. And the way we have worked and what we have believed in is that we need to solve customer problems. We never believe in doing a science project. So here also we started working with customers as we have always done in the past. We understood the customers pain points, the challenges they were facing, especially in this case and in cloud networking space, multicloud networking space, based on the user requirements, users, or the customers use cases, we started building our service. And here what we have built as a complete network as-a Service. It's a multicloud network as-a service, which not only provides connectivity to multiple clouds, but also addresses the needs for bringing in networking services, as well as security services, making sure that you have a full policy based infrastructure on top of it, you have deep visibility into the clouds as well as into on-premise end to end visibility, end to end monitoring, troubleshooting. And all of it is delivered to you as-a service. So that's what we have been doing here at Alkira. >> Excellent! So when we've looked at multicloud, of course, every cloud, they have some similar things, they have some different things. They all tend to do things a little bit differently. One of the secret sauces that have been talked about for the last few years is the SD-WAN space, like you had built with the tele to help really enable those environments. So Atif we've got a diagram here, which I think will help explain a little bit as to where out here and how it plugs into these different environments, walk us through a little bit what we're seeing here, and what you're actually doing at Alkira. >> So here we are building a global unified, multicloud network. It's consumed as a service. Think of it as consuming it just like you would consume any other SaaS, like our SaaS application. So you come to Alkira's portal, you register. And then there you go, and you start building your global multicloud unified network with integrated services. So here what you see is a Alkira's cloud services exchange which comprises of the cloud exchange points. You can bring these up these cloud exchange points up anywhere on the globe. You can decide like what networking services security services you need in these cloud exchange points, you can connect to multiple clouds. From there, you can bring your existing on-prem connectivity into the CXPs. All these CXPs have a full mesh of overlay, high speed, low latency connectivity among each other. So there is a full network which comes up between these CXPs. And the whole infrastructure scales with customers as our customers scale. So it's a horizontally scalable, very highly redundant and resilient infrastructure, which we had built. >> All right, so, Amir now that we understand the basics of the technology, you've got some strong investors including Sequoia, Kleiner Perkins, give us what is being announced that you're coming out of stealth, where are you with the product? How many employees you have? And where are you with the discussion of customer adoption. >> So Stu we are obviously, bringing this to the market, and we will be announcing it on April 15. It's available for the customers to consume our solution as a service on that day. So they are welcome to reach out to us and we'll be happy to help them. And as a matter of fact, just come to our website and register for the service. And yeah, I mean you rightly said that we have a superstar team of not only the venture capital companies, but also the board members representing those companies, the Bill Coffin and Mamun Ahmed, who the leading VCs are on the board of our company, including myself and Atif. >> All right, Amir I'd love to actually bring up the second slide that we have here. Walk us through you said the service, how do people get started? How do they understand, walk us through what they do. >> So the biggest challenge when we started looking into these problems, Stu was that it was very complicated. You had to piecemeal bring up instances in the cloud and stitch them together. And when you try to integrate the services, that was a different challenge for the customers. So we want to make sure that it was so simple and clean, that the customer didn't even have to think about any underlying construct on any of the clouds, they should not have to worry about learning each individual part from the networking perspective. So here's your portal, you just come, step one is come to our portal register. Step two is you start drawing your network based on your intent, what on-prem connectivity you want to bring into this service, what type of services you need, like a lot of firewalls and then what pilots you need to connect and everything happens seamlessly, from on-prem, prem through services into the cloud, across multiple clouds. It's a seamless service that we have created and with full analytics capabilities and full governance built in. >> All right, so Atif bring us into what this means for customers, how do they manage it? Is this the networking team? Is it the cloud architects? What API's are there? How does this fit into kind of what customers are doing today? And solve some of those challenges that we laid out earlier in the discussion. >> Yes, from the customer's perspective, as I said, it's completely delivered as a service. Customers come to our portal, they draw out the network, they select the services, they click on provision and the whole network comes up within minutes. So the main thing here is that from a customer's point of view, if they are connecting to different clouds, they don't need to understand any of the underlying specifics or underlying constructs of any of the cloud in order to bring up connectivity. So what we are doing here is we are abstracting the cloud chair. So we are building a virtual cloud network. So if you think of, if you compare with what we did in the previous life, we virtualized the WAN. So here what we are doing is we are virtualizing the cloud network, so underlying doesn't matter which cloud you sit on which cloud you need to connect to, which networking services, whether a cloud native services or whether you want to consume Alkira services, or we also support like customer bringing in third party services as well. So it's all offered from our platform all offered is service to the customer. Again, no expertise required in any of the underlying networking constructs of any of these clouds. >> Give us what we should be looking at from a technology roadmap from Alkira, through the rest of 2020. >> Good question, Stu. So as I mentioned earlier, our roadmap is dictated by customer requirements, so we prioritize what customers need from us. So we have come out with a scalable platform, we have come out with a marketplace for networking services in there. In the near term, we'll be expanding our marketplace with more services. We will be addressing more use cases and when I talk about use cases, I can give you some examples. Like there's, you not just only need connectivity into cloud, you might have different requirements from throughput perspective or bandwidth perspective or different services that you need to contend your cloud when you may have certain applications such as Internet facing application where you need like traffic coming in from the internet, inbound to those applications, you might need services like a load balancer, like an external load balancer in our services exchange. You might also need like a firewall, you might need traffic engineering, or sorry, service chaining capabilities where you chain service through multiple traffic through multiple of these services like a firewall and a load balancer. So we built a platform which gives you all those capabilities going forward, we will be adding more services more use cases to it. We have a long ways ahead of us and we will be putting a lot of effort in delivering a roadmap as we go. >> All right, so Amir your technical team definitely has their hands full and robust roadmap to work on. Give us the high level, what we should be looking for Alkira, for people that are out there, multicloud and networking tends to get talked a lot. There's many big companies and small ones. What will separate Alkira from the rest of the market today? And what should we be looking to see the company's progression through 2020? >> Yeah, thanks for asking that. Yeah, certainly. I mean, from the solution perspective, Atif said that it's so fundamentally important to have a very strong basis. And that's what we have done. We are bringing out a certain number of services and now we will continue to grow on that we'll create a big marketplace. We will continue to improve on which clouds we connect to and how and we will building our own services in certain cases as well. Now, building a technology is just one piece of it, we have to go out to market with a company that the customers can trust every single department in that company, whether it's sales or how they do business with us all the business back end pieces, after we sorted out and that's what we've been working with. And then go to market partners, that is very, very important, support is very important. So let me spend a little bit of time on go to market strategy. We have been working with the service providers so that we can extend our reach not only to the large customers, but also to mid-size customers across the globe. So you will see us in the future announcing major service provider, partnerships, as well as we've been working with large SIS, WAAS and system integration partners. And also we have taking a slightly different approach this time because it's a service. So we are going with telecom master agents, which have been working with the service providers, the cloud providers, the cable providers, as a channel, and they have a huge reach into the customer base. So we have a very comprehensive strategy not only from the go to market perspective and the technology perspective, but also how we are going to support our customers and continue to build our relationship to build a lasting company. >> Yeah, Amir super important point there. Absolutely, we've seen the maturation and change in the service providers, as today they are working with many of the public cloud providers and they're, as you said, the close touch point and a trusted partner for customers. All right, so before I let you go, you two are brothers, everybody in today's day and age is spending even more time with family but your situation you've worked together for a long time. What keeps bringing the two of you together, working together and talk about that bond? >> So I mean we're a very close knit family, we have four brothers and one sister, and obviously Atif and I have been the closest because we have been working together for the longest, we've at least work in five different companies together, our families traveled together, we have three daughters each, we live about five minutes, walk from each other. And we just have this bond where we not only have the family close, but also very close knit friends circle, which we both hang out with, and we obviously have common interests in the sports as well. We play squash and tennis and workout. So Atif if you want to take a stab at that also. >> Yeah, so we've always been very close. In fact, we've been together for the last like, ever since I can remember like even college days, we were roommates for some time also, we have our circle of friends, is the same old source. So, again, we are very close. And we worked well together so we complement each other's skills. And it's worked out in the past. Hopefully it will work out again. And I look forward to working with them for many, many more years to come. >> Amir and Atif thank you so much for sharing the coming out of stealth. After all, Alkira we definitely look forward to watching your progress and seeing how you're helping customers in this multicloud world. Thank you for joining us. >> Stu thank you so much. >> Thank you for having us. >> All right, I'm Stu Miniman. And thank you so much for watching this special CUBE conversation on theCUBE. (gentle music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation I am stupid a man and this is a special cube conversation we've been talking a lot of course for many years about the ascent of cloud and today in 2020 multi cloud is a big piece of the discussion and we're really happy to help unveil coming out of cell al kiram which is helping the networking challenges when it comes to multi cloud and I have the two co-founders they are brothers I have Amir who is the CEO and a DIF who is the CTO the Khan brothers thank you so much for joining us and congratulations on the launch of the company thank you sue for having us on the show it's a pleasure to see you again all right so Amir we've had you on the program your previous company that you've done was of course the fella you the two of you have worked together at I believe five companies successful companies acquired you know the most recent one into Cisco so a mirror obviously you know you know strong networking theme your brother the CTO I was going to talk to us about the engineering but give us you know just the the story of Al Kyra what you've been building and now ready to unveil to the world certainly needs to so in around 2018 timeframe we started looking into the next big problem to solve in the industry which was not only a substantial you know from the market size perspective but also from the customers perspective was solving a major pain point so when we started looking into the cloud customers and started talking to our customers they were struggling from the cloud networking perspective even in a single cloud and it was a new environment for them and they had to understand all the nitty-gritty details of each one of these clouds and when you go to multi cloud environment it becomes exponentially complicated to address not only connectivity but how to deploy services like firewall and other services including low balancers and IP address management etc and remote access so we started digging deeper into this problem and start working with the customers and took a clean sheet of paper and came up with a very comprehensive approach to offering a solution which is as a service this time we are not shipping any hardware or software it is you know just like any other SAS application you just come to our portal I just drag and drop literally draw out your network and click on provision and you know come back after 40 minutes or so your full global cloud infrastructure is up and running so out if your brother laid out a pretty broad vision there any of us from the networking world we know there's a lot of complexity there and therefore it takes a lot of work when I want to do things simply as a service is you know a huge growth area bring us inside the engineering challenges that you and the team have been working on to build this solution second let's do so we've been working both our men and myself in the networking industry for more than 25 years now and our the way we have worked and what we have believed in is that we need to solve customer problems we never believed in like doing a science project so here also we started working with customers as we have always done in the past we understood the customers pain points the challenges they were facing especially in this case and in cloud networking space multi-cloud networking space based on the user requirements users or the customers use cases we started the building a service and here what we have built is a complete network as a service it's a multi cloud met work as a service which not only provides connectivity to multiple routes but also addresses the needs for bringing in networking services as well as security services making sure that you have a full policy based infrastructure on top of it you have deep visibility into into the clouds as well as into on-premise into and visibility into and monitoring troubleshooting and all of it is delivered to you as a service so that's what we have been doing here at ELQ here excellent so when we look at multi-cloud of course you know every cloud they have some similar things they have some different things they all tend to do things a little bit differently you know one of the secret sauces that have been talked about for the last few years is ESP BAM space like you and built with Nutella to help really enable those environments so if we've got a diagram here which I think will help explain a little bit as you know we're out here it how it plugs into these different environments walk us through a little bit what we're seeing here and what you're actually doing a tell Kira so here we are building a global unifying the multi cloud Network it's consumed as a service think of it as consuming it just like you would consume any other SAS like our SAS issue so you come to lqs portal you register and then there you go and you start building your global multi-cloud unified network with integrated services so here what you see is is a Elka's cloud services exchange with comprises of cloud exchange points you can bring these up these cloud exchange points up anywhere on the globe you can decide like what networking services security services you need in these cloud exchange points you can connect the multiple clouds from there you can bring your existing on-premise connector matiee into the CX PS all these CX B's have a full mesh of overlay high speed low latency connectivity among each other so there is a full network which comes up between these CX B's and this the whole infrastructure scales with customers as as a customer scale so it's a horizontally scalable veil a very highly redundant and resilient infrastructure which we have both all right so armor now that we understand the basics of the technology you've got some strong investors including Sequoia kleiner perkins give us you know what is being announced day you're coming out of stealth where are you with the product you know how many employees you have and where are you with the discussion of customer adoption so stew we're obviously bringing this to the market and we will be announcing it on April 15th it's available for the customers to consume our solution as a service on that day so they are welcome to reach out to us and we'll be happy to help them and as a matter of fact just come to our website and register for the service and yeah we rightly said that we have a superstar team of not only the venture capital companies but also the board members representing those companies the bill Cochran and mamoon Hamid Wright who the leading VCS are on the board of our company including myself inactive all right I'm all right love to actually bring up the second slide that we have here walk us through you said you know the service you know how do people get started how do they understand you know what would walk us through what what they do so the biggest challenge when we started looking into these problems you know Stu was that it was very complicated you have to piecemeal bring up instances and the cloud and stitch them together and when you try to integrate the services that was a different challenge for the customers right so we wanted to make sure that it was so simple and clean that the customer didn't even have to think about any underlying construct on any of the clouds they should not have to worry about learning each individual power from the you know networking perspective so here's your portal you just come you know step one is come to a portal or register step two is you start drawing your network based on your intent what on-prem an activity you want to bring into this service what type of services you need like all all the firewalls and then you know what pilots you need to connect and everything happens seamlessly the from on pram pram through services into the cloud and across multiple clouds it's a seamless service that we have created and with full analytics capabilities and full governance built in alright so I'll to bring us into what this means for customers you know how do they manage it you know is this the networking team is it the cloud architects you know what api's are there how does this fit into kind of what customers are doing today and you know solve some of those challenges that we laid out earlier in the discussion yes trauma from the customers perspective it's as I said it's it's completely delivered as a service customers come to our portal they draw out the network they select the services they click on provision and the whole network comes up within minutes so the main thing here is that from a customer's point of view if they are connecting to different clouds they don't need to understand any of the underlying specifics or underlying constructs of any of the of the cloud in order to bring can I bring up connectivity so we what we are doing here is we are abstracting the clouds here so we are building a virtual cloud network so if you if you think of if you compare it with what we did in the in the previous life be virtualized the when so here would be a doing is we are virtualizing the cloud network so underlying doesn't matter which cloud you sit on which cloud you need to connect to which networking services whether cloud native services or whether you you want to consume our care services or we also support like customer bringing in third-party services as well so it's all all offered from our platform all offered is a service for to the customer again no expertise required in any of the underlying networking constructs of any of these cards give us what we should be looking at from a technology roadmap from Akira through the rest of 2020 good question as to so as I mentioned earlier our roadmap is dictated by customer requirements so we prioritize what customers need from us so we have come out with a scalable platform we have come out with a marketplace for networking services in there in the near term we'll be expanding our market place with more services we will be addressing more use cases and when I talk about use cases I can give you some examples like there's a view you not just only need connectivity into cloud you might have different requirements from from throughput perspective or bandwidth perspective or different services that you need to front-end your cloud but you may have certain applications such as internet basing application where you eat like traffic coming in from the internet inbound to those applications you might need services like a load balancer like an external load balancer in our services exchange you might also need like a firewall you might need traffic engineering or sorry service eaning capability is where you would chain service through multiple or traffic through multiple of these services like a firewall in the load balancer so we have built a platform which gives you all those capabilities going forward we will be adding more services more use cases to it we have a long ways ahead of us and we will be putting all our effort in delivering a roadmap as we go all right so Amma your technical team definitely has their hands full and uh you know robust after work on uh give us the the high-level what we should be looking for out Kira for people that are out there you know multi-cloud and networking you know tend to get talked a lot there's many big companies and some small ones what will separate al Kira from the rest of the market today and what should we be looking to see the company's progression through 2020 yeah thanks for asking that yeah certainly I mean you know from the solution perspective out it's said that you know it's so fundamentally important to have a very strong basis right and that's what we have done we are bringing out a certain number of services and now we will continue to grow on that will create a big marketplace we will continue to improve on which clouds we connect to and how and we will be building our own services in certain cases as well now building a technology is just one piece of it we have to go out to market with a company that the customers can trust every single you know the department in that company whether it's sales or how they do business with us all the business back-end pieces have to be sorted out and that's what we've been working with and you know then go to market partners that is very very important right support is very important so let me spend a little bit of time on go to market strategy we have been working with the service riders so that we can extend our reach not only to the large customers but also to midsize customers across the globe so you will see us in the future announcing major service water partnerships as well as we've been working with large sis bars and system integration in a partners and also we have taking a slightly different approach this time because it's a service so we are going with telecom master agents which have been you know working with the service providers the cloud providers the cable providers as a channel and they have a huge reach into the customer base so we we have a very comprehensive strategy not only from the go to market in the technology perspective but also how we are going to support our customers and continue to build a relationship to build a lasting company yeah I'm a super important point there absolutely we've seen the maturation and change in the service providers as today they are working with many of the public cloud providers and they're as you said a close touch point and a trusted partner of our customers all right so before I let you go you know YouTuber brothers everybody in today's day and age is spending even more time with family but you know your your situation you've worked together for a long time what keeps bringing the two of you together working together and then talk about that ball so I mean we're very close-knit family we have four brothers and one sister and obviously active and I have been the closest because we have been working together for the longest we have at least work in five different companies together our families travel together we have three daughters each we live about five minutes you know walk from each other and we you know just have this bond where we not only have you know the family close but also very close-knit friends a circle which we both hang out with and we you know obviously have common interest in the sports as well we play squash and tennis and work out so after four if they want to take a stab at it but also yeah so we've always been very close in fact we've been together for the last like ever since I can remember like even even college days he was we were roommates for for some time also he ever say we have like our circle of friends is the same also so again we're very close and we work well together so we complement each other's skills and and it's it's worked out in the past hopefully it will work out again and I look forward to working with them for many many more years to come yeah well I'm or not - thank you so much for sharing the the coming out of stealth for Al Kyra we definitely look forward to watching your progress and you know seeing how you're helping customers in this multi-cloud world thank you for joining us - thank you so much thank you for having us all right I'm Stu minimun and thank you so much for watching this special cube conversation on the cube [Music]

from the cube Studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi I'm Stu mana man and welcome to a special cube conversation talking to leaders around the globe happy to welcome to the program two guests actually one is a cube alumni the other one I believe the first time on the program the Khan brothers I have a mirror and on TIFF so gentlemen thanks so much for joining us oh thank you for having us again yeah so here you know you and your brother you're coming out of stealth al Kyra is the company you two both work together at many companies we know you most recently from Katella we're a mirror you know we've spoken with you on the cube I believe right after that acquisition proc it was another company back in the day also happened to sell to Cisco but we know that you know networking is one of the you know toughest things in our industry now everybody thinks that their environment is is something challenging but you know we know networking in the weeds there's so many protocols and while networking as a whole doesn't change really fast there have been a lot of waves of change happening most recently of course cloud having a major impact on what's happening to the networking world Amir let's start with you you're the CEO your brother a tip is the CTO you're both co-founders give us a little bit of the background and the why of Al Kyra yes but after we sold patella I was looking for the next idea and we talked to multiple customers and multiple service providers a common theme that came across was the cloud networking was complex and when it came to multiple clouds it became exponentially complex it was not only learning multiple clouds but all the underlying constructs were different in each one of us so we decided that we need to take a look at a common solution which provides one way of connecting to all clouds that exist out there and provides common services like firewall and then from the governance perspective it becomes very challenging if you have very diverse environments so we are bringing analytics a common way of monitoring and managing the networks on top of that so it's a very comprehensive solution that the industry needed and they needed it as a service so we we dug deeper and came up with this idea went to the VCS and got funded very quickly to solve the problem and very proud to say that we are the first ones were in the industry for bringing computer networking as a service for multi cloud environments to the market all right now that's a that's a big vision and definitely something you know we hear from talking to customers you know absolutely multi-cloud most of the people there trying to figure out exactly what that means to their cuspid to their companies as well as it is not simple today aught if I want to pull you in you know we've seen you know many you know groups of talented you know development engine developers and engineers sometimes they try Ville in packs so when you look at you know what Amir just talked about the challenge in front of you give our audience a little bit of the understanding of you know the history of what you've been working on and is this you know a you know just turning the crank based on the latest and greatest technologies is that the coming together of some of the pieces that you've been thinking about for many years you know help us under the hood a little bit as to you know that this problem and the talents and what you're moving forward with for the last many many years both Amma and myself and other team are other team members we've been working on large customer networks whether those are service provider networks or whether those are enterprise networks so we have a deep understanding of the challenges they have based over over time so and now as I mentioned customers are transitioning to public clouds they are transitioning to SAS environments and there are a lot of challenges with which they are running into so we decided to take this challenge on and we brought together like a stellar team we have people who joined us from from botella as well as people from other large organizations they've been working on we have people from AWS we have people from Azure via people from other large companies so we have put together a great team we are very well funded and as I mentioned our approach has always always been to work with customers always understand their pain points and solve the real issues which which are having so that's approach we took and that's what we are doing here Adele here yeah so I did if I want to dig down a little bit more and actually we've got a slide I think you'll talk to because we know in networking a lot of the words sound the same you know is this an overlay I'm your said that at the service what gets deployed where is it what are the customers need to do where does this all live so if you could explain this this this diagram here for our audience so what the buildings do is a is a service and it's a cloud services exchange if you look at it and what it is comprised off is east cloud exchange points you can think of these as virtual pops and virtual polos and you can you can decide which region in the world or on the globe you want the DC X is to be spun up you come to our portal since it's consumed as a service you decide like what needs to reside on the CX PS which networking services you need there you can bring your own services or you can consume LTS services and when I talk about services or when I say services these can mean security services other networking services such as load balancers ipam DDI whatnot from the EC XPS you're gonna connect to multiple pounds from here without you as as Enterprise know without you knowing need to know anything about the underlying cloud providers networking concerts so all you do is you give us the requirements you say that you need to connect to this cloud this this is your appointment and we we do it for you so what basically what we are doing is we are virtualizing the clouds networks it's a so in the past we virtualize the when in our previous company and patella if you look at it it's virtualizing plan over different types of transports here we are virtualizing cloud it doesn't matter which public clouds you're sitting on which public cloud you need to connect to you know one service to consume you know one way of doing about networking great III that diagram definitely helped me a lot Amir let's talk my understanding you've got some customers what are some of the early things that they're using this for is this you know hybrid cloud going from their data center to a public cloud we talked about multi cloud does that mean we're actually connecting services between some of the public clouds help us understand what what your customers are seeing and starting to use it's a very interesting questions too we've been talking to multiple customers that we said and without a doubt every single customer that we are talking to has some sort of a multi cloud strategy and the reasons for them to get into multiple clouds could be either their some teams are using some applications which are optimized for a particle cloud one of the customers that we went to they were using one cloud and then acquired a company because of which you know another cloud was brought into the environment so all of a sudden they have a need to very quickly you know integrate those two environments and then there are you know with what's going on in the market today people are going to remote access requirements you know people are working from home and they need to get onto the network very quickly and consume applications which scale much better in the cloud so there's a demand from that perspective right so and in some cases one company becomes let's say Amazon becomes a competitor somewhere and people want to move to another cloud that could be another alternative right so so without fail many people are you know getting into cloud environments and the primary reason that I'm hearing now that many more companies will move into the cloud is going to be regulatory issues right so people are starting to think about pushing all the financial companies the healthcare companies to adopt multiple clouds for redundancy for high availability etc yeah you bring up a great point one of the questions we've been asking for the last couple of years is how is multi-cloud the same or different from what we used to see with multi-vendor aught if you know I think back to you go back you know 10 15 20 years ago and some of the M&A discussions that Omer was talking about you know you know I buy another company Oh what are they doing for their network well throw out their whole network and let's standardize on the vendor of choice that we have because it's better if I can go homogeneous well it's not as easy to do that in the public cloud so help us understand you know it's not as easy as that saying oh all these clouds they have API is they all use similar type type of abstractions and the like you know where does Al Kira really help make things easy for customers when they're when they're doing multi cloud so you know every cloud is doing things differently when it comes to networking so yeah at the end of the day functionality might be the same but how to achieve that how to get that working it's very different between each plug so now what we are seeing with these enterprises is that they have to build a deep understanding of each cloud before they can take on that cloud and nowadays cloud architects are in big demand they don't come cheap either so you don't necessarily just need the personal cloud architect with expertise in one account you need like architects with the expertise and multiple thoughts so so we wanted to solve that problem that's why we took this challenge on and we wanted to make it a make it one way of working with all these clouds so from a cloud architects perspective from a enterprises perspective why can't there be just one way of connecting to all these clouds why do I have to know the details of each cloud as armor said each cloud spring brings its own own its own value in different ways and there is a multi cloud strategy out there so either customers are in multiple pounds or they're looking at at multiple doubt so that's that's a big challenge right now and since what we are offering is as a service it's a unified global multi cloud network and again we're virtualizing the cloud network all right Amir you mentioned early on in the conversation that Acura is well funded you've got Sequoia and kleiner perkins as two of your investors you know definitely companies that look closely and understand the networking space help us understand the basics of the company though coming out of stealth right now is the product available where is it available you know where are you with with customer and deployments certainly we are making our product available on April 15 and many customers are trying it right now they're in the process of deploying it in production these are across industries healthcare manufacturing high-tech you know financial industries so customers span across multiple different verticals in addition to that many service providers are working with us to offer this to extend their capabilities into the cloud so many service providers have been struggling with that and this makes it very easy for them to complement their private connectivity solutions and extend their reach deeper into the cloud so the industry is very excited right now and we are excited because this is an opportunity you know which the cloud migration has brought to the table from networking perspective and if we had thought about it 10 years ago 20 years ago it was just not possible and now we have this capability in the clouds to offer in lastik capabilities do not only provide connectivity but but integrate the services like firewalls scale them up and down and provide proper governance yeah you bring up such a such a big point there I talked before about multi vendor and today you know the network team it's not just oh well I touch all of the gear that I manage as you said it's the service providers public clouds they need to span all those environments we've definitely seen a huge shift in service riders and their relationships with the public cloud over the last five years or so atif I guess that brings up a the important point here who's gonna be managing all this when it comes to a Keira is this the traditional Network person if they've been you know doing land maybe a little bit of an are they going to be doing it is the cloud team you know where does this sit in the you know conversation and jostling of roles that we're seeing as organizations are more and more embracing public cloud so it's Stu it's still a networking solution so what we are seeing working with our customers is that it's a it's a cloud architects who are were very excited when they see our solution now they can see that the network can can can show the same agility which they have been very they're being seen with with compute and storage and and rest of the stuff moving into the cloud so network was always like behind it took a lot lot of effort a lot of work to get the network to extend into clouds meeting all the all the enterprise requirements so now network architects are excited because they it's it's become very simple for them to to move into the cloud or extend their private connectivity into the club I'll give you an example we've been working with some large enterprises and they many of them they don't need to open up a manual or documentation to use our solution so our goal was always to make it so simple that anyone should be able to connect to a cloud without knowing anything about the networking constructs of a given cloud so you just come in you should just be able to give us the requirements that you need need this much capacity you need this much throughput you need B services to front-end the clouds these are your security policies which are global and you need like a multi global transport or global network a multi-level network and you should be able to just bring it up and you should on you should not need like certifications in cloud networking or certifications in using tools to orchestrate cloud connectivity so we are built a very scalable infrastructure which allows the customers to get a service from us or use our service which which meets the requirements all right I'd like to ask both of you you know it's April 2020 you're coming out of stealth you know I'm sure the product has you know all the features that your customers are asking for today but give us a look as we go through the kind of the next six to 12 months armor first from kind of a customer standpoint not if from the technology standpoint what should people be looking at both with your product how you're working with partners and you know really this multi cloud networking landscape our focus is on mid to large size enterprises and depending on which company you talk to their strategy varies to get into the cloud somewhere some are at a very early stage others have moved significant amount of workloads into into the cloud already the reality that we are seeing out there is that the hybrid environment is going to exist for a long long time and that's why we have built a solution which seamlessly integrates their existing wide area networking infrastructure to bring traffic into our solution and then we tie them seamlessly to the cloud and provide integrated services and Governors governance on top of that and they can define their own internal policies based on you know their enterprise needs so that's what we are seeing right now and going into the future I think that industry is going to continue to evolve it's still early the solutions will evolve quite a bit we have a first mover advantage to provide networking as a service and we are so excited to just continue to you know accelerate and help customers to migrate into the cloud as quickly as possible provided with the highest resiliency and security yes so from from a technology perspective as I said like we we believe in working with the customers and solving their use cases we are innovating at a very rapid pace and there are many many different use cases which which we are working on we have some of the use cases which we are which we have delivered so far where we are going out with a certain number of use cases and we will be adding more use cases as we as we go support support for more use cases and we support like all the major clouds right now we will be adding more clouds to our offering we offer a Marketplace on our portal as well so it's a it's a it's a cloud service with a built in marketplace of network services we will be expanding that marketplace as well with more services it's all based on the on the customer requirements and and we prioritize our offerings based on the requirements and there's a there's a lot of work ahead of us also it's just the beginning and as I said we are very excited to solve all these problems and make our customers successful excellent if you're a good setup for armors last slide here so we've got a slide here is that how do customers get started walk us through yeah as I have said we've our goal is to make it as simple as possible you know we live in a different world now where things change very very quickly but the customers just come to our portal they register or our service just like any other SAS based service they you know basically are taken to a canvas where they can draw their infrastructure drop services create policies within minutes and then you know across the globe and then click on the provision button and they can go and have coffee and the full infrastructure comes up literally in less than an hour as a matter of fact in many cases we have seen a comment in around half an hour across the globe this is the type of a solution or capability that the industry has never seen before in the industries we are very proud of bringing the solution to the market well congratulations both of you a lot of work goes into bringing a company to launch before I let you go I do have one last question you two are brothers you've worked together at a number of companies so you know give our audience but you know what it's like and you know what what keeps bringing you back to working together well we are very close family we've always you know you know studied together work together and many different companies we live about five minutes away from each other our kids hang out together we travel together so you know it's kind of interesting you know we've always had a very very close relation yeah this is this is a fifth company I guess where we are working together as he said like we have always worked together we have a we we have a same circle of friends also so so we have very very close to each other so yeah this is one of the best squash players in the country and nowadays he doesn't get much time but he was ranked in the top three for a long time in amateurs in the country well that is awesome well hopefully you know you get to get a little bit of break you know once the company goes into spell they know a lot of work moving forward but I'm Erin atif congratulations and thank you so much for joining us to announce coming out of stealth thank you thank you for having us thank you it's a pleasure to talk to you alright and we definitely look forward to tracking al Kyra in the future as that they move forward with adoption with their customers and their solution I'm still minimun and as always thank you for watching the queue [Music]

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Hi, I'm Stu Miniman and welcome to a special CUBE Conversation, talking to leaders around the globe. Happy to welcome to the program two guests, actually one is a CUBE alumni and the other one, I believe is the first time on the program, the Khan brothers, I have Amir and Atif. So gentlemen, thanks so much for joining us. >> Oh, thank you for having us again Stu. >> Yeah, so Amir. >> Thank you for (mumbles). >> You know, you and your brother, you're coming out of stealth. Alkira is the company, you two have both worked together at many companies. We know you most recently from Viptela, where Amir you know, we'd spoken with you on the Cube, I believe right after that acquisition. >> Right. >> (mumbles) with another company back in the day also happened to sell to Cisco, but we know that, you know, networking is one of the, you know, toughest things in our industry. You know, everyone thinks that their environment is something challenging but you know, we know networking in the weeds, there's so many protocols and while networking as a whole doesn't change really fast, there have been a lot of waves of change happening, most recently, of course, Cloud having a major impact on what's happening to the networking world. Amir, let's start with you, you're the CEO, your brother, Atif is the CTO, you're both co-founders, give us a little bit of the background and the why of Alkira. >> Yeah, so after we sold Viptela, I was looking for the next idea and we talked to multiple customers and multiple service providers. A common theme that came across was the Cloud Networking, was complex and when it came to multiple clouds it became exponentially complex. It was not only learning multiple clouds but all the underlying constructs were different in each one of the clouds. So we decided that we need to take a look at a common solution, which provides one way of connecting to all clouds that exists out there, and provides common services like Firewall and then from the governance perspective, it becomes very challenging if you have very diverse environments. So we are bringing analytics, common way of monitoring and managing the networks on top of that. So it's a very comprehensive solution that the industry needed and they needed it at, as a service. So we dug deeper and came up with this idea, went to the VCs and got funded very quickly to solve the problem and very proud to say that we are the first ones in the industry, who are bringing computer networking as a service or multicloud environments to the market. >> All right, that's a big vision and definitely something that, you know, we hear from talking to customers, you know, absolutely multicloud, most of the people, they're trying to figure out exactly what that means to their customers, to their companies, as well as it is not simple today. Atif, I want to pull you in, you know, we've seen, you know, many, you know, groups of talented, you know, develop an engineer, developers and engineers, some, they travel in packs. So when you look at, you know, what Amir just talked about, the challenge in front of you, give our audience a little bit of the understanding of, you know, the history of what you've been working on, and is this, you know, a, you know, just turning the crank based on the latest and greatest technologies? Is it the coming together of some of the pieces that you've been thinking about for many years? You know, help us under the hood a little bit as to, you know, this problem and the talents and what you're moving forward with. >> Atif: (mumbles) Stu, (mumbles) that we, our approach has always been to work with our customers and understand the customers' real use cases as well as pain points. So for the last like many, many years, both Amir and myself and other team members, we've been working on large customer networks, whether those are service provider networks or whether those are enterprise networks, so we have a deep understanding of the challenges that they have faced over over time. So and now, as Amir mentioned, customers are transitioning to public clouds, they are transitioning to SaaS environments and there are a lot of challenges which they're running into. So we decided to take this challenge on and we brought together like the stellar team. We have people who joined us from Viptela as well as people from other large organizations, they've been working on, we have people from AWS, we have people from Azure, we have people from other large companies. So we have put together a great team. We are very well funded and as I mentioned, our approach has always been to work with customers, always understand their pain points and solve the real issues which they are having. So that's the approach we took and that's what we are doing here at Alkira. >> Yeah, so I've, Atif I want to dig down a little bit more, and actually we've got a slide I think, you'll talk to because we know in networking, a lot of the words sound the same, you know, is this an overlay? Amir though said that it's a service. What gets deployed? Where is it? What do the customers need to do? Where does this all live? So if you could explain this diagram here for our audience. >> Atif: So what they're building Stu is a service. And it's a Cloud Services Exchange, if you look at it, and what it is comprised of is these cloud exchange points. You can think of these as virtual pops or virtual portals. And you can decide which region in the world or on the globe you want the CXPs to be spun up. You come to our portal since it's consumed as a service, you decide like what needs to reside on the CXPs which networking services you need there, you can bring your own services or you can consume the Alkira services. And when I talk about services or when I say services, these can be security services, other networking services, such as load balancers, IPAM, DDI, whatnot. From these CXPs you can connect to multiple clouds from here without you as a enterprise, without you knowing, need to know anything about the underlying cloud providers networking constructs. So all you do is you give us the requirements, you say that you need to connect to this cloud, this is your requirement and we do it for you. So what basically what we are doing is we are virtualizing the cloud networks. It's a, so in the past, we virtualized WAN in our previous company in Viptela, if you look at it, it's virtualizing WAN over different types of transports. Here we are virtualizing cloud, it doesn't matter which public cloud you're you're sitting on, which public cloud you need to connect to, you have one service to consume, your one way of doing cloud networking. >> Great, I, that diagram definitely helped me a lot. Amir, let's talk, my understanding, you've got some customers, what are some of the early things that they're using this for? Is this you know, hybrid cloud going from their data center to a public cloud? We talk about multicloud, does that mean we're actually connecting services between some of the public clouds, help us understand what your customers are seeing and starting to use. >> It's a very interesting question Stu. We've been talking to multiple customers, as we said, and without doubt, every single customer that we are talking to has some sort of a multicloud strategy. And the reasons for them to get into multiple clouds could be either that there some teams are using some applications which are optimized for a particular cloud. One of the customers that we went to, they were using one cloud and then acquired a company because of which, you know, another cloud was brought into the environment. So all of a sudden they have a need to very quickly, you know, integrate those two environments. And then there are you know, with what's going on in the market today, people are going to remote access requirements, you know, people working from home, and they need to get onto the network very quickly and consume applications, which scale much better in the cloud. So there's a demand from that perspective, right, so and in some cases, one company becomes, let's say, Amazon becomes a competitor somewhere and people want to move to another cloud, that could be another alternative, right? So without fail, many people are, you know, getting into cloud environments and the primary reason that I'm hearing now that many more companies will move into the cloud is going to be regulatory issues, right? So people are starting to think about pushing all the financial companies, the healthcare companies to adopt multiple clouds, for redundancy, for high viability, et cetera. >> Yeah, you bring up a great point. One of the questions we've been asking for the last couple of years is, how is multi-cloud the same or different from what we used to see with multi-vendor? Atif, you know, I think back to, you go back, you know, 10, 15, 20 years ago and some of the M and A discussions that Amir was talking about, you know, I buy another company, "Oh, what are they doing for their network? "Well throw out their whole network and let's standardize "on the vendor of choice that we have because it's better "if I can go homogeneous." Well, it's not as easy to do that in the public cloud. So help us understand, you know, it's not as easy as they're saying, "Oh, all these clouds." They have API's, they all use similar type of abstractions and the like, you know, where does Alkira really help make things easy for customers when they're doing multicloud? >> Stu you know, every cloud is doing things differently when it comes to networking, so yeah, at the end of the day, functionality might be the same but how to achieve that, how to get that working, it's very different between each cloud. So now what we are seeing with these enterprises is that they have to build a deep understanding of each cloud before they can take on that cloud. And nowadays, cloud architects are in big demand, they don't come cheap, either. So you don't necessarily just need a person, a cloud architect with expertise in one cloud, you need like cloud architects with the expertise in multiple clouds. So we wanted to solve that problem, that's why we took this challenge on and we wanted to make it one way of working with all these clouds. So from a cloud architect's perspective, from an enterprise's perspective, why can't there be just one way of connecting to all these clouds? Why do I have to know the details of each cloud? As Amir said, each cloud brings it's the, its own value in different ways and there is a multi-cloud strategy out there. So either customers are in multiple clouds or they are looking at multiple clouds. So that's a big challenge right now and since what we are offering is as a service, it's a unified, global, multicloud network and again, we're virtualizing the cloud network. >> All right, Amir, you mentioned early on in the conversation that Alkira is well funded. You've got Sequoia and Kleiner Perkins as two of your investors. >> Amir: Yes. >> You know, definitely companies that look closely and understand the networking space. Help us understand the basics of the company, though. Coming out of stealth right now, is the product available? Where's it available? You know, where are you with customer and deployments? >> Certainly, we are making our product available on April 15th and many customers are trialing it right now, they're in the process of deploying it in production. These are across industries, healthcare, manufacturing, high tech, you know, financial industries. So customers span across multiple different verticals. In addition to that many service providers are working with us to offer this to extend their capabilities into the cloud. So many service providers have been struggling with that and this makes it very easy for them to complement their private connectivity solutions and extend their reach deeper into the cloud. So the industry is very excited right now and we are excited because this is an opportunity, you know, which the cloud migration has brought to the table from networking perspective and if we had thought about it 10 years ago, 20 years ago, it was just not possible. And now we have this capability in the clouds to offer elastic capabilities to not only provide connectivity but integrate the services like Firewalls, scale them up and down and provide proper governance. >> Yeah, you bring up such a big point there. I talked before about multi-vendor and today, you know, the network team, it's not just, "Oh, well, I touch all of the gear that I manage." As you said, it's the service providers and the public clouds, they need to span all those environments. We've definitely seen a huge shift in service providers and their relationships with the public cloud over the last five years or so. Atif, I guess that brings up a really important point here, who's going to be managing all this? When it comes to Alkira, is this the traditional network person if they've been, you know, doing LAN maybe a little bit of WAN are they going to be doing it as the cloud team? You know, where does this sit in the, you know, conversation and jostling of roles that we're seeing as organizations are more and more embracing public cloud? >> So Stu, it's still a networking solution. So what we are seeing working with our customers is that it's a cloud architect so we are very excited when they see our solution. Now, they can see that the network can show the same agility, which they have been very, they've been seeing with compute and storage and rest of the stuff moving into the cloud. So network was always like behind, it took a lot of effort, a lot of work to get the network to extend into clouds, meeting all the enterprise requirements. So now, network architects are excited because they, it's become very simple for them to move into the cloud or extend their private connectivity into the cloud. I'll give you an example, where we've been working with some large enterprises and they, many of them, they don't need to open up a manual or documentation to use our solution so our goal was always to make it so simple that anyone should be able to connect to a cloud without knowing anything about the networking constructs of a given cloud. So you just come in, you should just be able to give us the requirements that you need this much capacity, you need this much throughput, you need these services to front end the clouds, these are your security policies, which are global and you need like a multi-global transport or global network or multicloud network and you should be able to just bring it up. And you should not need like certifications in cloud networking or certifications in using tools to orchestrate cloud connectivity. So we have built a very scalable infrastructure, which allows the customers to get a service from us or use our service, which meets their requirements. >> All right, I'd like to ask both of you, you know, it's April 2020, you're coming out of stealth, you know, I'm sure the product has, you know, all the features that your customers are asking for today but give us a look as we go through that kind of the next six to 12 months. Amir first, from kind of the customer standpoint, Atif from the technology standpoint, what should people be looking at, both with your product, how you're working with partners and you know, really just multicloud networking landscape? >> Our focus is on mid to large sized enterprises and depending on which company you talk to their strategy varies to get into the cloud. Some are at a very early stage, others have moved a significant amount of workloads into the cloud already. The reality that we are seeing out there is that the hybrid environment is going to exist for a long, long time and that's why we have built a solution which seamlessly integrates their existing wide area networking infrastructure to bring traffic into our solution and then we tie them seamlessly to the cloud and provide integrated services and governance on top of that and they can define their own internal policies based on, you know, their enterprise needs. So that's what we are seeing right now and going into the future, I think the industry is going to continue to evolve, it's still early, the solutions will evolve quite a bit. We have a first mover advantage to provide networking as a service, and we are so excited to just continue to, you know, accelerate and help customers to migrate into the clouds as quickly as possible and provided with the highest resiliency and security. >> Atif. >> So, from a technology perspective, as I said, like we believe in working with the customers and solving their use cases. We are innovating at a very rapid pace and there are many many different use cases which we are working on. We have some of the use cases which we are, which we have delivered so far, where we are going out with a certain number of use cases and we'll be adding more use cases as we go, support for more use cases. And we support like all the major clouds right now. We will be adding more clouds to our offering. We offer Marketplace on our portal as well, so it's a cloud service with a built in marketplace of network services, we will be expanding that marketplace as well with more services. It's all based on the customer requirements and we prioritize our offerings based on the requirements. And there's a lot of work ahead of us also, it's just the beginning and as Amir said, we are very excited to solve all these problems and make our customers successful. >> Excellent, Atif, you're a good setup for Amir's last slide here. So we've got a slide here that, how do customers get started? Walk us through. >> Amir: Yeah, as Atif said, we've, our goal is to make it as simple as possible. You know, we live in a different world now where things change very, very quickly. So the customers just come to our portal, they register for our service, just like any other SaaS based service, they, you know, basically are taken to a canvas where they can draw out their infrastructure, draw out services, create policies within minutes, and then, you know, across the globe, and then click on the, on provision button and they can go and have coffee and the whole infrastructure comes up literally in less than an hour. As a matter of fact, in many cases, we have seen it come in, in around half an hour across the globe. This is the type of a solution or capability that the industry has never seen before in the industry, so we are very proud of bringing the solution to the market. >> Well, congratulations, both of you. A lot of work goes into bringing a company to launch. Before I let you go, I do have one last question. You two are brothers, you've worked together at a number of companies so, you know, give our audience, you know what it's like and you know, what keeps bringing you back to working together? >> Well, we are very close family, we've always, you know, studied together, worked together in many different companies, we live about five minutes away from each other, our kids hang out together, we travel together (chuckles). So, you know, it's kind of interesting. You know, we've always had a very, very close relationship. >> And this is our fifth company, I guess, where we are working together. As he said, like we've always worked together. We have a same circle of friends also so we were very close to each other so. >> Amir: Yeah (mumbles) what I'm thinking about Atif is one of the best squash players in the country. Nowadays, he doesn't get much time but he was ranked in the top three for a long time in amateurs in the country (laughs). >> Well, that is awesome. Well, hopefully, you know, you get a little bit of break, you know, once the company goes in stealth, I know a lot of work moving forward, but Amir and Atif. Congratulations, and thank you so much for joining us to announce coming out of stealth. >> Amir: Thank you Stu. >> Thank you so much, thank you for having us. >> Thank you, it's a pleasure to talk to you. All right, and we definitely look forward to tracking Alkira in the future as they move forward with adoption with their customers and their solution. I'm Stu Miniman and as always, thank you for watching theCUBE. (upbeat music)

>> Narrator: Live from Nassau in the Bahamas. It's The Cube. Covering Polycon 18, brought to you by Polymath. >> Welcome back, everyone, this is The Cube exclusive coverage at Polycon 18, put on by Grid Capital and Polymath, of course The Cube is independent publishing, digital TV and research. Of course we're covering all the action this year in the crypto-space blockchain, crypto-currency token economics. Big news here with Polymath is announcing a partnership. We've got Trevor Koverko is the CEO of Polymath Amir Kaltak, CEO at Lexit. You guys just came off stage and announced a partnership. Your ecosystem is growing, you guys are enabling platform. Talk about the relationship. >> You want to start? >> Please, I insist. >> Alright, fine. So awhile ago we just realized that what Trevor and his team are doing is just fitting right in what we do. We, the marketplace for M&A, helping to liquidize assets, and a security token, in its purest form, is an asset. So we want to help, we want to work together and create an ecosystem in the future between Polymath and Lexit, that is basically the thing that we want to figure out and how to do it. >> Yeah, no, we're big fans of the project, and more importantly the team behind the project. That's always what we look for when it comes to any investment, or purchase, or partnership. We're really excited. >> This is really a great sign for you guys. Congratulations, Polymath and Lexit, you guys are growing companies. This is the magic of platforms, right? You guys have collaboration, ecosystem partners really become instrumental for you guys, so it's a good sign. You get the leverage, the platform, you get some time to market faster, time to value, this is what it's all about, right? >> I believe that security tokens are going to be a big part of our future revenue on Lexit itself, and I can't miss out on that one, and I'm happy that we meet at that early stage, so to say, where everything happens. Where we set the path into the future. So let's see what happens. >> Amir I want to ask you, as someone who's partnered (inaudible), why Polymath? What was compelling for you? What was the reason? Obviously they have a secure token, so it's a platform, and it's a trend that's your friend right now. So why Polymath? >> There are multiple ones. Trend isn't that right, but the thing is, I'm old school, right? If somebody I know and trust tells me this is a great person I need to talk to, this is a great project, then I do it. So, Tim Frost of Taurus Solutions was the guy who connected us in New York on a brief meeting, and now more and more, and so this is how we started. And I go with my gut feeling. If I see a sincere man, I see a sincere man, and I would like to work with him. >> Great. Platform-wise, API's, how's it going to work? You guys, can you share any details? I missed the announcement because we were doing Cube interviews. What was announced on stage? >> For me, this is kind of what I've been echoing all week. It's all about building the components of this ecosystem. We're trying to, literally, re-imagine Wall Street, and to do that it requires new forms of structure formation of capital. So we have private equity, we have mergers and acquisitions, we have venture capital, and with Polymath we're just trying to be the base layer that other exciting projects like Lexit can build on top of. >> What are some of the most important things in the platform, Amir, that you like? Just get under the hood a little bit. What's, what about Polymath is going to be a good deal for you guys? What's the key? Is it saving time, is it the certain things on the platform? What specifically about these guys- >> Free t-shirts? >> Free t-shirts definitely. And after that, the free t-shirt contest. What contest? I'm kidding. No, to me it's like, look, you want to have a security token, right? And then there are multiple jurisdictions, and there's a lot of legal compliance. It's a mountain of work in front of you. Those guys figured out how to do this simple and reliable for all of us. >> Kind of like what you're doing on the M&A side, except they do it for the security token. >> Sort of. >> Always breaking down barriers, that's the name of the game. That's the definition of an entrepreneur. >> Removing the blockers in front of you is the key, and not to waste time on management cycles, on things that someone else's doing. That, to me, it good partnership. Sounds like that's what you guys are offering, right? >> Absolutely. >> Absolutely. >> Alright, guys, well thank you for sharing the news. A final word, >> John, thank you. >> What do you see as the outlook, partnerships, you guys going to make some money together, you've got to build the product out first? How's the sequence, the order of operations of the partnership? Share the quick overview, then we'll end the segment. >> So we have a lot of work ahead of us. And right now it's about getting Polymath, the demo is out, the alpha is out, it's live, you can use it. And my biggest party right now is getting the application layer to the market, and that simply means a user interface, drag and drop, point and click, and that is my life right now. So once we get that out the door, these guys are ready. >> The thing is we are launching globally, full developed, since two years away in develop, in June. And soon after that, we will hopefully be ready for their platform. But, speaking of that, it's public now, but we will work closely right away to figure out how to optimize everything in between our systems. So it's going to be an ongoing process where we to be careful with resources, of course, but it's going to happen during this year, I hope. >> Amen. >> Well congratulations on the building blocks of success, you've got to start with the core. This is The Cube bringing a live coverage from the Bahamas. Big news here on the partnerships of the two companies, Polymath and Lexit. Look for more coverage. We'll be right back with more coverage after this short break. (techno music)

(bubbly electronic music) >> Narrator: Live from Nassau in the Bahamas, it's theCUBE, covering Polycon 18. Brought to you by Polymath. >> Okay, welcome back everyone. We're live here in the Bahamas. This is theCUBE's exclusive coverage of the token economics world cryptocurrency blockchain, the new innovation that's changing the world. And of course, word on the ground floor, day two of coverage our next guest Amir Kaltak, CEO and founder of L-exit, L-exit, Lexit, called legalized exit, legit exit. He's automating the M&A process in a decentralized way. This is exactly the kind of value we see with cloud computing, and when you see automation and efficiencies, that's disruptive. Amir, congratulations on your awesome venture. Love your model. Let's get into details, because I think-- >> Thank you. >> You're demonstrating, in my opinion, where value is being created and then ultimately captured faster, more efficiently, because you're automating the M&A process. For people to get exit in a highly volatile, value creation, value capture world. Take a minute to explain your company. This is fantastic. Thank you for this kind intro. Hello, world. Lexit, in a nutshell, is M&A on blockchain, and I hope you guys will love it. What we do is we give you access to the world of M&A, which is currently a big boys club, and we want you guys to be all participating in it, from the small entrepreneur who just started out, who crashed his startup but created a great tac. He can sell it on it. The whole world's going to see it. Or to the seasoned entrepreneur, to the big entity, to the big enterprise, you can sell it on there too. You will be seen by all acquirers in the world, and at a penny of the cost, at multitudes of speed, you will be able to liquidize and asset your business. >> So we, Dave and I, predict that there's going to be a lot of liquidity going on at many levels, obviously. Token economics drives that, but in the startup world, you either make it or you sell it, or you put it out of business. In this world, as people start developing technology, the difference between a company and a feature might not be the same. So I might build the best app for social entrepreneurship, for solving world hunger or tracking the water supply through blockchain. And someone says, "Damn, I love that. I'm going to buy that." Now I got to go to a banker, I got to get legal fees. My choices are-- >> Yes, limited. >> Limited, hassle, costs cash. >> As a guy who just crashed a startup, let's take this example, because the majority does fail: over 90%, as matter of fact 96%. Now, you just failed, but you have this great technology you created, right? What are you going to do? You check your address book. Who might buy it? But it's limited too, because you just started out. You don't know nobody, so what you do is you go to a consultancy: M&A consultancy, the lawyers who are connected to this sphere. >> John: The gatekeepers. >> Yes, the gatekeepers. Big boys. >> John: And they take a big cut of it. >> They will tell you, it's an amazing technology. We'll help you sell it, but you make a down payment of $10,000 right now, and we'll look into it. But you don't have $10,000 right now, for instance. So what are you going to do? And even if you pay them, the likeability of them getting back to you is not that high, so what you do is on Lexit is, you get to Lexit, you open up your account, get KYC'd. We're very strict on that. It's fully legit platform, and you list it. You get assessed by our professional M&A network, given a value to it. It's not the value it will be sold for, but it's a value the professional assessor thinks you're worth, and why. He's going to say these reasons. Now, the buyers are going to see it, make the bids, and there you are. Access. >> So you guys automate that entire end-to-end process. >> Absolutely. So, Lexit is, from the listing, down to the final due diligence and drafting of agreements. Everything is in it. The final signature and the transfer of ownership. It's a full solution. >> Yes. The future of work, obviously, is about automation. I mentioned cloud computing, because we look at that market heavily. On the tech side, automation drives it, but managing processes, automating processes away is threatening to a lot of people. You're basically putting people out of business, potentially. >> Yes, I keep doing that. >> If you're successful, a cadre of ecosystem partners, service providers, traditionally go out of business, so I like that. >> Potentially. >> Well, they're going to have to adapt or change. I see this in global service integrators, like Accenture. These guys are getting eaten up by machine learning automated coding, because they can do it faster and better. >> You know my business better than I do! (both laugh) >> What we do at theCUBE, we know our stuff. So, this is disruptive, and at the end of the day, the other thing I want to get your reaction to is open-source. A lot of people in the ethos of the mission based open-source world is, I wrote coding as an open-source. If my company fails, and my VC's make it proprietary, it's like an owned asset in bankruptcy, or whatever, dying, you can't put it back, but with open-source code, there's always going to be value there, to some level. It might not be great. So, I might say "Hey, you had a failed venture, I'll buy your code." >> Exactly. >> Transfer your GitHub over. Done. >> That's how it works. >> So this is kind of like the dynamic that... Do you see that? >> This is the direction that we're heading to. We want to connect the dots, because we started Lexit out of a community approach. We figured out two years ago, when we started it... So we're two years into that right now, that this is direly needed. We don't have access, but if I got this problem right now as a startupper, so do many. And out of this thinking, we claim ourselves to be the startups for startups and empower the community. I believe that in terms of leadership, for instance, you're only a good leader if you empower everyone around you to become a good leader, based on respect and mutual purpose. >> So I got to ask you a question. >> Please. >> Cause everyone's going to ask this question of all startups. You got to know where you are. Are you a startup? Are you a growing company? Where's the product? How far along are you? When is it going to be released? Talk about the momentum of the offering that you have. Is it available in Beta? What's the status of the product itself? Because I'm sure it'll be used a lot. >> As I said, we started two years ago. The first year we didn't even write a single line of code. It was just like how do you put this huge M&A process into a usable yet powerful but simple to use platform. How do you do that? Just scalable from the small, small asset you want to sell, a line of code, an algorithm up to a large enterprise. The first year was finding out a process. What is necessary? How do we cover all aspects on different jurisdictions, and all this stuff, right? How to make it work on the legal side too. And we figured it out, and then we started doing it. And right now I can tell you guys we are scheduling the launch of Lexit, this year, in June. So we'll not just-- >> The product will be ready for production, shipping product. >> Absolutely. Available worldwide, completely worldwide ready to operate. Ready to make your deals, to put your listings, to make your bids, to get the best technology out there, but not just technology. Letters M&A, it means any kind of of business from a pizza chain, to a high tech company, to a biotech company, to food, supplies you can sell. >> Usually when I do legal documents, you see an exhibits in there, and say oh, exhibit A is all the IP, or whatever the seller's selling and the buyer's buying. When you deal with decentralized asset creation and capture, use that blockchain involved, how much is the tech involved in your process? Obviously, the legal stuff, I can really see automating away. That's like check one. But when you start dealing with assets that are either code or something durable, like property, that's maybe stored in blockchain, how do you guys look at that? Is that part of the automation? Is that a factor? Where does that impact? Is that an exhibit? Do I just say "Here's my key"? How do you deal with that? >> Alright, let's put it this way. We do want to connect existing M&A space to Lexit. The exits, they're huge structures. We do want to disrupt them, that's true, but to do that you can't just create entirely everything new. You have to kind of find a way for the big boys old club, the big banks, and all those folks around there to participate, right? To give them a familiar way to work. What we did is the token model economics in a way that people get rewarded, people pay for stuff inside of it, and such, right? Everything is triggered with smart contracts, obviously, to know did you do the down payment, did the signature happen. The smart contracts are automating the whole thing down to the final transaction. When the final transaction happens we get our commissions paid out from the Astro we have. The Lexit Crypto Astro. Everything is transferred and secure. Everybody involved into a deal knows exactly what's happening. >> John: And they have a shared incentive too. >> Absolutely >> They're tokenizing the process so there's a reward element. Right? >> Yes. >> Am I getting this right? >> Yes, the access. There are three parties in Lexit. Buyer, seller, obviously and the assessors. Professional M&A guys. They get rewarded in tokens, and that greatly. Pretty much in the magnitude of what they do in billables at the Big Four, PWC and so on. There's a high incentive there to do this in this assessment, and they get rewarded from the community pool which gets feed with all those listing fees, unlocking futures and everything that's happening within Lexit itself. The kicker is that we at Lexit believe that much in token that the commission you have to pay us is between 8 and 2%. 2% of about 35 million dollars in volume, and it gets a bit higher down to the lower ones. We take this commission only in our own token. I don't want dollars, not even Bitcoin. >> So you have your own token? >> Yes. >> Utility token or security token? >> It's a utility token strictly, and it's called LXT. >> LXT. Great. And is it available now, or are you going to launch it in June? >> Right now we are in the private pre-sale. I'll put through, and it looks like we'll keep it in the presale. It looks like the page was selling out LXT right now to the private backers. It's that high that we think in two weeks from now on, speaking mid-March, it's sold out >> What's the numbers? Hard cap, soft cap? Do you have the numbers? >> I told my team "Listen, everybody tells me: 'you're doing M&A on blockchain, you can raise hundreds of millions,'" and everybody will say "That's okay." I said "We don't need that money." I just want to raise what we do need to finalize the last mile of the dev and launch it this year. The hard cap is 10,000 ETH. 10K ETH only, roughly $9 million right now, and that's it. >> And you're going to reserve the other tokens for the community to do the work and be part of this new future of work equation? >> 50% of the total supply, which is 18 million, it's zero, goes to sale, to the market. Just 10% to us founders. You don't need more. >> So you're not greedy? >> No. >> You guys are playing it right to create-- >> I want the community to be empowered, this whole-- >> You need the community. You need the community. >> I need the community. >> So that is a different dynamic... Well, not different. That is the dynamic that everyone is agreeing on in the community in the ecosystem here, is that if you have bogarting or hoarding coins, or people taking down allocations, you miss the dynamic of the human capital, which is what the future of work is doing. You are an example-- >> Free promotion, you know what I mean? >> You're engaging. The future of work requires human capital. So if one institutional buyer buys the token out, there's no people. >> I interrupted you. You said "We are an example for what"? >> The future of work. >> I love that. >> You are executing, potentially, disruptive M&A, but you're not going after the banks directly. They can play, too. >> Right. >> So you guys are a service. You're like an Amazon.com website cloud service for... >> You could say that. >> M&A. Well, not like, but automated. Automating away things is the way to go. Do you see other examples that are like you guys, that are emerging in use cases? Obviously you're taking a known process, M&A, automating it away, making it tokenized. What other things do you see out there that's ripe for disruption? >> I do think that if somebody out there... Lexit, what we do, let's put it aside for a moment. I think supply, the supply chains of the world are ripe for disruption. I think they're inefficient. I think even food production, down to the basic needs of a human being, this is ripe for disruption. >> When I got my MBA back in the 90s, after I got my Computer Science degree in the 80s, I remember the word that always stick in my head from the books that they teach you is the "Value Chain." >> Value Chain. >> The Value Chain is a concept of anything, of value creation. This notion of chaining, blockchain, you see it... Anything that has value creation process. >> Let's take food production for a moment. Rice, okay? Rice. So now there is this farmer, somewhere in Asia, or in elsewhere, and he's producing, and he's selling it to somebody, who's picking it up, and he sells to the next distributor. He sells to an international distributor. He sold it for probably... I don't know, maybe 20 cents a pound tops? Probably just five. I don't know the prices. What happens if we could chain that supply chain, that we have a decentralized nature of how all these people can directly feed into the system and just jump those middlemen entirely. So this is what I'm speaking about. It's going to disrupt everything. Somebody's going to figure out that one. >> So you guys have a good formula, just to recap. You're automating the M&A process, you're creating a huge supply of tokens available to the community, that will help you change the game on M&A, which is also part of the process of your value chain, now tokenized, and you're taking a small cut that's a tiered commission, if you will, on the M&A transaction. >> It's like six times cheaper. >> Higher for the lower numbers and as you go higher, which you want more deals, you take a smaller cut, so it's not greedy, you're not taking a grotesque-- >> Nope. We go even beyond. Around Lexit we created a partner program. This partner program is fueling directly deals onto Lexit, and we give them 50% off the commission. People tell me "You're crazy." No I'm not. You need to incentify. So if you get thousands of these partners one day... Think of that. 50% is still a lot. I believe in sharing everything except my girlfriend. Everything is fine, so we share. You can have my beer, that's fine. (both laugh) Speaking of that, I believe in this-- >> Well, the Network Effect, too. Sharing is an ethos of distribution, so distribution is sharing. Sharing is also a social thing, but social gamification really is about distribution. You're essentially creating a network effect, and this is the fundamental pattern in token economics, is the networks. >> Totally true. >> You see that? >> That's how it happens. >> What's your situation now? You've got a deal going on. Are you with Polymath? >> That's amazing, I-- >> Talk about that. You're announcing it on stage in about an hour. >> It's true, yeah. The stage is about to come. Trevor and his team do a great job. Boarding startups with the tokens to become a security token. I believe there is a huge business for them in the future. And now we want to work with them together, so we partner up, and what we do is... One of the models is that we will help their clients to liquidize these tokens, then, over Lexit. So this is one of the thoughts we have. We're just figuring out a few things, but we're very excited. >> John: And it's all API-based, I'm assuming, right? >> All API-based. It's been highly automated, of course. Automation. It's all about automation. You have to lower the cost to make it efficient, to make it cheap for everybody involved, so you have to automate everything you can, and smart contracts are, per se, an automation tool. >> Well, Amir, good luck with your venture, Lexit. I love the idea, I love what you're doing. I think this is what we look for in theCUBE, this kind of innovation. We think it's awesome. Good luck on your team. Product's almost pre-launch. >> And the pre-sale is almost through, so if you guys want in before ETH (mumbles), let me just drop that one in two weeks. It's closing and we distro in between four to six weeks, we're going to distro the token. So it's everything happening right now, and soon after that the exchanges are waiting, and you'll be surprised. They're going to be the good ones. >> This is innovations theCUBE are covering: the blockchain, the cryptocurrency. We're at Polycon 18. Polymath is the folks putting on the event with Grit Capital, a Canadian contingency, but they know their cryptography. If you know Canada, you know what the deal is there. It's theCUBE covering it live. We'll be back with more live coverage after this short break. >> Thank you. (reverb-heavy electronic music) (moody ambient electronic) (moody ambient electronic) >> Hi, I'm John Furrier, the co-founder of SiliconANGLE Media and co-host of theCUBE. I've been in the tech business since I was 19, first programming on minicomputers in a large enterprise, and then worked at IBM and Hewlett Packard, a total of nine years in the enterprise. Various jobs from programming, training, consulting and, ultimately as an executive salesperson, and then started my first company, it was in 1997, and moved to Silicon Valley in 1999. I've been here ever since. I've always loved technology, and I love covering emerging technology. I was trained as a software developer, and loved business. I loved the impact of software technology to business. To me, creating technology that starts a company and creates value and jobs is probably one of the most rewarding things I've ever been involved in. And I bring that energy to theCUBE because theCUBE is where all the ideas are and where the experts are, where the people are and I think what's most exciting about theCUBE is that we get to talk to people who are making things happen. Entrepreneurs, CEO of companies, Venture Capitalists. People who are really, on a day in and day out basis, building great companies. In the technology business, there's just not a lot of real-time live TV coverage, and theCUBE is a non-linear TV operation. We do everything that the TV guys on cable don't do. We do longer interviews. We ask tougher questions. We ask sometimes some light questions. We talk about the person and what they feel about. It's not prompted and scripted. It's a conversation. It's authentic. And for shows that have theCUBE coverage, it makes the show buzz, it creates excitement, and more importantly, it creates great content, great digital assets that can be shared instantaneously to the world. Over 31 million people have viewed theCUBE and that is the result of great content, great conversations, and I'm so proud to be part of theCUBE, a great team. Hi, I'm John Furrier. Thanks for watching theCUBE. (emotive electronic music) >> Narrator: Robert Herjavec!

(upbeat music) >> Welcome everyone to this CUBE coverage of AWS re:Invent 2021. We have a lot going on at this year's re:Invent with over 100 guests on the program, and I'm excited to welcome two of those guests here with me right now. We are joined by Ralph Munsen, the Chief Information Officer at Warner Music Group and Atif Khan, the CTO of Alkira and founder of Alkira as well. Gentlemen, welcome to the program. >> Thank you so much, Lisa. So glad to be here with you. >> Good to be here. >> Yeah. Good old fashioned Zoom is become our best friend in the last 22 months or so I'm losing count. Atif, I'd like to start with you. I know Alkira has been on the key before, but it's been a while and you guys are a relatively young company. Give the audience an overview of Alkira and what it is that you deliver. >> Absolutely, Lisa. So we started back in may of 2018, and the Cloud networking space, multicloud networking. And we came out of stealth mode back in April of 2020, and launched the company. In fact, one of our first events coming out of stealth mode was a Cuban interview back in April of 2020. So here at Telecare, what we are doing is we are building a Cloud platform, which allows customers to build a common network across multiple Clouds with built-in network and security services, with the policy and management layer on top full end to end visibility and governance capabilities. And all of this is delivered as a service and consumed as a service as well. And I'm very glad to be here with Ralph, who is from Warner Music Group and is one of our marquee customers. So I'll let Ralph introduce himself, and tell us a bit more about Alkira and WMTS Cloud journey. >> That sounds great. Ralph, why don't you start by giving the audience? I'm sure everyone knows Warner Music Group, but in case there's anyone out there that might not. Give us a little bit of a background. >> Yeah, so the Warner Music Group has been around since 1950 and 1940 even it had its roots at Hollywood and out of Warner Brothers Pictures, Today, say global company in 79 countries we operated. If the 100 employees and we have two major divisions, we have our era recorded music division, which has the labels people commonly turn to Atlantic records, Warner brothers records, and so forth. And then we have our publishing division, which is more a chapel, which is where our songwriters live. And of course we have some singer songwriters that are on both sides of our business. But now currently people may know our artists. We have ed Sheeran, Bruno Mars, Coldplay, Cardi B, Blake Shelton and I could go on and on. But exciting, great year, we're having one of our best years ever. And I'm so glad to be here and partnering with an Alkira. >> Excellent. I love all of those artists that you mentioned. Fantastic. So let's talk a little bit now Ralph about the backstory. Talk to me about the IT infrastructure at Warner Music Group, what you had there and some of the challenges that you had that you came to Alkira to solve. >> Yeah, well initially when I took over about five years ago now, we were very much a data center based business with traditional networking and IT functions. Additionally with our foreign affiliates, IT was sort of decentralized in the sense that a lot of the networking and data center components were left to regions. And so while we operated globally, we didn't really operate globally, at Warner among our affiliates. So one of the challenges was how do we get out of the data center? Cloud was new. One of the big things that were coming with big data, which is absolutely right for moving, going straight to the Cloud, especially if you don't have anything on-prem and how do we rationalize all of these different locations and conduct all the M&A work we've been doing? So it was quite a challenge, really. At the end, we wanted to have one view of the network, and Alkira. I looked at many a company and Alkira seemed the best to provide that to us. So. >> Well, talk to me a little bit more about why Alkira, because as Atif was saying, they're very young. What came out of stealth mode during the pandemic Warner Music Group, being around since the 40s and 50s, the legacy institution, a great brand. What made you take a risk on such an early stage startup? >> Quite frankly, there was nothing in the space (chuckles) at the time you loved, there were companies that had components of it, of what Alkira does, which is basically network orchestration allowing us to use existing components. And nobody has the whole package, especially incorporating security. So, we figured why not take, take a chance? There's no, it won't hurt you no harm. And if anything is successful, it will give us a great ability to manage our network, much more efficiently taking things that took days down to hours and being able to do it much more efficiently with much fewer staff, as opposed to hiring a lot more because when you orchestrate all the components that are underneath, obviously it requires more bodies, more resources. >> Right. That efficiency and cost optimization is key there. Atif I have to ask you, talk to me about, this is only a few years ago, the gap in the market that you and your brothers saw a few years ago, when you founded the company, because as Rob was saying, there was nobody else in the market at the time that could do what you're doing. >> Yeah, absolutely. So Lisa, as you know, myself and Amir, we were also a part of the founding team of Viptela, which was the SD-WAN Company. So back in the day when we did SD-WAN, the requirement was to connect sites together. So if you go back like 5, 10, 5, 7, 10 years ago, networking was done to connect sites together, which could be remote sites, data centers, sites to data centers, all of that together. But fast forward, a few more years with the adoption of Cloud, requirements changed from the networking perspective. So now your network is not just connecting sites together, but most of the traffic now is from sites or users, which could be sitting anywhere. If you look at, what's going on? in the pandemic people are working from all across the globe. They are not just sitting in campuses or sites. So traffic patterns are from sites or users mostly to the Cloud or SaaS applications. So now networks also need to evolve and they need to be built inside the Cloud rather than from outside or connecting into the Cloud. So Cloud access is one capability, but building a network inside the Cloud becomes a requirement. And secondly, now it's not just only about connectivity because security becomes even more important because your security perimeter is changing as well. So securing all these Cloud networks becomes very, very complicated. And now as Ralph can tell you, majority of the enterprises have a multicloud strategy and each Cloud is done differently. So the moment you bring in multiple Clouds, multiple regions across the globe, it becomes so complicated for enterprises to build and manage. They need something, or a platform which makes it easy, gives them one way of doing networking, building a common network across whether you're connecting multiple Clouds or Clouds to your on-prem locations or Clouds to internet or sites to internet. So that's where we saw this gap and we decided to build Alkira to tackle this problem. >> Got it. So Rob, let's talk now about what you've implemented as a team was saying we live in this, in this work from anywhere hybrid multicloud world. Talk to us about Warner, what you implemented and maybe a little bit about your multicloud strategy, if you've got one. >> Ralph: Yeah. So over the last five years, Warner has migrated entirely into Cloud. And to this point before it's multicloud, we're mainly in AWS, but we do have some pleasure and some Google Cloud. And with that, I was telling Atif and Amir. It was interesting and they built a Cloud on site. They totally forgot about the networking aspect. So (laughs), you have ease of use for services and servers inside (indistinct) cloud, but networking is not really present, not to mention when it was built out, it wasn't made to go to competing Clouds. So most companies are facing this problem. How do you treat these environments as a single holistic environment? How do you turn things up, turn things down? How do you secure it, When every single one is different habits, selling unique ways of doing things? So that really was, how we ended up looking for an out Alkira, because I just kept looking at the costs and the profit print grow and grow and grow. And the complexity to a (indistinct) before is growing exponential. One change in one thing would lead to two changes to another. If you add another Cloud or you add another point on the network, you've got exponential growth and complexity, complexity, you have to deal with. So one stop shop. (chuckles) >> One stop shop and reducing that complexity. Talk to me about reducing complexity, and what you're accomplishing there. Especially, in the last year and a half as things have been so dynamic, shall we say? (chuckles) >> Yeah, well, I will say this. It was turnkey for the most part. It took a matter of months as opposed to years, because out of the box, there was a lot of integrations with the major network of players. So as of right now, you can buy firewalls, routing, VPC, things like this, they all exist, but they're not orchestrated together. Right? And then you have policies and security, again not orchestrating a different set of tools. So it really only took us two to three months to get it up and running, I acts, I just had a conversation (chuckles) with them when we were going to finish. So I think we'll be finishing this up completely in January and sometime. So, I was pretty sure. >> LISA: That's fantastic. So really, >> Yeah. >> Sorry Relaph fast time to market there with getting things implemented. Talk to me about from a business outcome perspective, you are CIO, what are some of the outcomes? That this technology is enabling you to deliver back to the business? >> Yeah, it really, the number 1, 2 big ones come to mind. One being able to provide them a secure enterprise. I know when there is the change it's made uniforms for our network without, some of older something's being forgotten about. So that's number one, security is big. You can imagine a company like more ever marquee brands, all brands, any company of marquee brands are targets today. That's number one. Number two is our time to market for eminent. So when we buy a company the time it takes us to get them to be completely part of Warner and therefore start realizing the business case and benefits sort of reasonably bought. Bought the company to begin with. So, we're buying a lot more and we're turning them up and turning those business cases up faster. But usually those cases would say things like six months to a year to integrate with us, and then we can unlock the set of benefits. Now it's more like, two to three months and you start to be able to lock the benefits sooner. And of course, those are different than a case by case basis, but that's. >> Sure, but significantly faster there, you're looking at a two to three X multiplier there, as you talked about. >> Ralph: right. >> Now, you mentioned multicloud Ralph. So here we are at re:Invent. I imagine part of your AWS as part of your Cloud infrastructure and they're a technology partner of ALkira's. >> Ralph: Correct. Yeah. So AWS is actually our biggest Cloud provider of the three, and yeah (laugh) they're their partner without cure. So Good. >> And Atif then you, Alkira's technology partner of AWS, correct? >> Yeas. Alkira is a technology partner of AWS, we are also available on AWS marketplace. So customers can consume, AlKira's platform from AWS marketplace as well. >> But given the fact that so many businesses in every industry are multicloud, I assume that you work with all the Cloud vendors. Atif Yeah? >> Absolutely. So our platform runs inside of the Cloud and runs in AWS is a Cloud as well. And from there it connects to multiple Clouds. So if customers need to connect to Azure or AWS from there or Oracle Cloud or any other Cloud, for that matter, they can connect from our platform and our platform is it scales horizontally. So as customers needs scale, it scales as well. And one of the key advantages is, it's consumed as a service. So there's no software to download or hardware to run for or to acquire for any of the customers. It's a software solution and it's consumed as a service. >> Got it. Ralph one on one more question for you before we wrap things up here, want to get your recommendations for IT Executives, CEOs, who might be in a similar situation to you, whether or not they are with a legacy organization, what are some of your recommendations that you say you need to be looking at a, B and C? >> Yeah, I would primarily say really need to be looking at some of these newer technologies that can help speed up, people, especially in this case to transition to the Cloud and that planning ahead of time, especially goal-setting, I find to be it's any of these places, providers is absolutely Paramount, because you can, if you don't make your own (indistinct) take that step forward and you can end up with shelter. So I make sure that it's very important that when you commit to that, you commit fully, you plan it out and you make sure you actually use it to get the benefits. One of my tech key is software. So. (chuckles) (Lisa Laughing) I'm a bit of it so. >> Well, you've been there and It costs a lot of money and it doesn't do any good. It doesn't move the business forward. And in this day and age, there is a competitor right behind the rear view mirror who might be smaller, more nimble, and more agile, who can take your place easily. >> Absolutely. >> If the organization isn't willing to take the risks and commit, as you said, Atif last question over for you, where are the customers go to learn more? I know you are at re:Invent your booth 1628, but what do you recommend folks go attendees of the event, as well as just other prospects to go to learn more about what you guys are delivering for companies like Warner Music Group. >> So if you're at re:Invent, please stop by our booth. And one of our Cloud specialists will give you a demo as well. So it's a very quick demo and you'll see, how we are reinventing networking for the Cloud narrow. You can also go to our website and you'll find a lot of information on our website. You can request a demo there as well. So look forward to seeing most of you at our booth and those who are not attending in person, please go visit our website. >> Lisa: Reinventing Networking. I like your play on words. They are Atif very appropriate. Gentlemen, thank you for joining me today talking about Alkira, Warner Music Group, what you guys are doing together and how this new early stage technology is really quite transformative. We appreciate your insights. >> Thank you. >> Thank you so much. >> For Ralph Munsen and Atif Khan, I'm Lisa Martin, and you're watching theCUBE's continuous coverage of AWS re:Invent 2021. Thanks for watching. (soft techno music)

(upbeat music) >> Welcome everyone to this CUBE coverage of AWS re:Invent 2021. We have a lot going on at this year's re:Invent with over 100 guests on the program, and I'm excited to welcome two of those guests here with me right now. We are joined by Ralph Munsen, the Chief Information Officer at Warner Music Group and Atif Khan, the CTO of Alkira and founder of Alkira as well. Gentlemen, welcome to the program. >> Thank you so much, Lisa. So glad to be here with you. >> Good to be here. >> Yeah. Good old fashioned Zoom is become our best friend in the last 22 months or so I'm losing count. Atif, I'd like to start with you. I know Alkira has been on the key before, but it's been a while and you guys are a relatively young company. Give the audience an overview of Alkira and what it is that you deliver. >> Absolutely, Lisa. So we started back in may of 2018, and the Cloud networking space, multicloud networking. And we came out of stealth mode back in April of 2020, and launched the company. In fact, one of our first events coming out of stealth mode was a Cuban interview back in April of 2020. So here at Telecare, what we are doing is we are building a Cloud platform, which allows customers to build a common network across multiple Clouds with built-in network and security services, with the policy and management layer on top full end to end visibility and governance capabilities. And all of this is delivered as a service and consumed as a service as well. And I'm very glad to be here with Ralph, who is from Warner Music Group and is one of our marquee customers. So I'll let Ralph introduce himself, and tell us a bit more about Alkira and WMTS Cloud journey. >> That sounds great. Ralph, why don't you start by giving the audience? I'm sure everyone knows Warner Music Group, but in case there's anyone out there that might not. Give us a little bit of a background. >> Yeah, so the Warner Music Group has been around since 1950 and 1940 even it had its roots at Hollywood and out of Warner Brothers Pictures, Today, say global company in 79 countries we operated. If the 100 employees and we have two major divisions, we have our era recorded music division, which has the labels people commonly turn to Atlantic records, Warner brothers records, and so forth. And then we have our publishing division, which is more a chapel, which is where our songwriters live. And of course we have some singer songwriters that are on both sides of our business. But now currently people may know our artists. We have ed Sheeran, Bruno Mars, Coldplay, Cardi B, Blake Shelton and I could go on and on. But exciting, great year, we're having one of our best years ever. And I'm so glad to be here and partnering with an Alkira. >> Excellent. I love all of those artists that you mentioned. Fantastic. So let's talk a little bit now Ralph about the backstory. Talk to me about the IT infrastructure at Warner Music Group, what you had there and some of the challenges that you had that you came to Alkira to solve. >> Yeah, well initially when I took over about five years ago now, we were very much a data center based business with traditional networking and IT functions. Additionally with our foreign affiliates, IT was sort of decentralized in the sense that a lot of the networking and data center components were left to regions. And so while we operated globally, we didn't really operate globally, at Warner among our affiliates. So one of the challenges was how do we get out of the data center? Cloud was new. One of the big things that were coming with big data, which is absolutely right for moving, going straight to the Cloud, especially if you don't have anything on-prem and how do we rationalize all of these different locations and conduct all the M&A work we've been doing? So it was quite a challenge, really. At the end, we wanted to have one view of the network, and now Alkira. I looked at many of companies and I'm curious in the best to provide that to us. So. >> Well, talk to me a little bit more about why Alkira, because as Atif was saying, they're very young. What came out of stealth mode during the pandemic Warner Music Group, being around since the 40s and 50s, the legacy institution, a great brand. What made you take a risk on such an early stage startup? >> Quite frankly, there was nothing in the space (chuckles) at the time you loved, there were companies that had components of it, of what Alkira does, which is basically network orchestration allowing us to use existing components. And nobody has the whole package, especially incorporating security. So, we figured why not take, take a chance? There's no, it won't hurt you no harm. And if anything is successful, it will give us a great ability to manage our network, much more efficiently taking things that took days down to hours and being able to do it much more efficiently with much fewer staff, as opposed to hiring a lot more because when you orchestrate all the components that are underneath, obviously it requires more bodies, more resources. >> Right. That efficiency and cost optimization is key there. Atif I have to ask you, talk to me about, this is only a few years ago, the gap in the market that you and your brothers saw a few years ago, when you founded the company, because as Rob was saying, there was nobody else in the market at the time that could do what you're doing. >> Yeah, absolutely. So Lisa, as you know, myself and Amir, we were also a part of the founding team of Viptela, which was the SD-WAN Company. So back in the day when we did SD-WAN, the requirement was to connect sites together. So if you go back like 5, 10, 5, 7, 10 years ago, networking was done to connect sites together, which could be remote sites, data centers, sites to data centers, all of that together. But fast forward, a few more years with the adoption of Cloud, requirements changed from the networking perspective. So now your network is not just connecting sites together, but most of the traffic now is from sites or users, which could be sitting anywhere. If you look at, what's going on? in the pandemic people are working from all across the globe. They are not just sitting in campuses or sites. So traffic patterns are from sites or users mostly to the Cloud or SaaS applications. So now networks also need to evolve and they need to be built inside the Cloud rather than from outside or connecting into the Cloud. So Cloud access is one capability, but building a network inside the Cloud becomes a requirement. And secondly, now it's not just only about connectivity because security becomes even more important because your security perimeter is changing as well. So securing all these Cloud networks becomes very, very complicated. And now as Ralph can tell you, majority of the enterprises have a multicloud strategy and each Cloud is done differently. So the moment you bring in multiple Clouds, multiple regions across the globe, it becomes so complicated for enterprises to build and manage. They need something, or a platform which makes it easy, gives them one way of doing networking, building a common network across whether you're connecting multiple Clouds or Clouds to your on-prem locations or Clouds to internet or sites to internet. So that's where we saw this gap and we decided to build Alkira to tackle this problem. >> Got it. So Rob, let's talk now about what you've implemented as a team was saying we live in this, in this work from anywhere hybrid multicloud world. Talk to us about Warner, what you implemented and maybe a little bit about your multicloud strategy, if you've got one. >> Ralph: Yeah. So over the last five years, Warner has migrated entirely into Cloud. And to this point before it's multicloud, we're mainly in AWS, but we do have some pleasure and some Google Cloud. And with that, I was telling Atif and Amir. It was interesting and they built a Cloud on site. They totally forgot about the networking aspect. So (laughs), you have ease of use for services and servers inside (indistinct) cloud, but networking is not really present, not to mention when it was built out, it wasn't made to go to competing Clouds. So most companies are facing this problem. How do you treat these environments as a single holistic environment? How do you turn things up, turn things down? How do you secure it, When every single one is different habits, selling unique ways of doing things? So that really was, how we ended up looking for an out Alkira, because I just kept looking at the costs and the profit print grow and grow and grow. And the complexity to a (indistinct) before is growing exponential. One change in one thing would lead to two changes to another. If you add another Cloud or you add another point on the network, you've got exponential growth and complexity, complexity, you have to deal with. So one stop shop. (chuckles) >> One stop shop and reducing that complexity. Talk to me about reducing complexity, and what you're accomplishing there. Especially, in the last year and a half as things have been so dynamic, shall we say? (chuckles) >> Yeah, well, I will say this. It was turnkey for the most part. It took a matter of months as opposed to years, because out of the box, there was a lot of integrations with the major network of players. So as of right now, you can buy firewalls, routing, VPC, things like this, they all exist, but they're not orchestrated together. Right? And then you have policies and security, again not orchestrating a different set of tools. So it really only took us two to three months to get it up and running, I acts, I just had a conversation (chuckles) with them when we were going to finish. So I think we'll be finishing this up completely in January and sometime. So, I was pretty sure. >> LISA: That's fantastic. So really, >> Yeah. >> Sorry Relaph fast time to market there with getting things implemented. Talk to me about from a business outcome perspective, you are CIO, what are some of the outcomes? That this technology is enabling you to deliver back to the business? >> Yeah, it really, the number 1, 2 big ones come to mind. One being able to provide them a secure enterprise. I know when there is the change it's made uniforms for our network without, some of older something's being forgotten about. So that's number one, security is big. You can imagine a company like more ever marquee brands, all brands, any company of marquee brands are targets today. That's number one. Number two is our time to market for eminent. So when we buy a company the time it takes us to get them to be completely part of Warner and therefore start realizing the business case and benefits sort of reasonably bought. Bought the company to begin with. So, we're buying a lot more and we're turning them up and turning those business cases up faster. But usually those cases would say things like six months to a year to integrate with us, and then we can unlock the set of benefits. Now it's more like, two to three months and you start to be able to lock the benefits sooner. And of course, those are different than a case by case basis, but that's. >> Sure, but significantly faster there, you're looking at a two to three X multiplier there, as you talked about. >> Ralph: right. >> Now, you mentioned multicloud Ralph. So here we are at re:Invent. I imagine part of your AWS as part of your Cloud infrastructure and they're a technology partner of ALkira's. >> Ralph: Correct. Yeah. So AWS is actually our biggest Cloud provider of the three, and yeah (laugh) they're their partner without cure. So Good. >> And Atif then you, Alkira's technology partner of AWS, correct? >> Yeas. Alkira is a technology partner of AWS, we are also available on AWS marketplace. So customers can consume, AlKira's platform from AWS marketplace as well. >> But given the fact that so many businesses in every industry are multicloud, I assume that you work with all the Cloud vendors. Atif Yeah? >> Absolutely. So our platform runs inside of the Cloud and runs in AWS is a Cloud as well. And from there it connects to multiple Clouds. So if customers need to connect to Azure or AWS from there or Oracle Cloud or any other Cloud, for that matter, they can connect from our platform and our platform is it scales horizontally. So as customers needs scale, it scales as well. And one of the key advantages is, it's consumed as a service. So there's no software to download or hardware to run for or to acquire for any of the customers. It's a software solution and it's consumed as a service. >> Got it. Ralph one on one more question for you before we wrap things up here, want to get your recommendations for IT Executives, CEOs, who might be in a similar situation to you, whether or not they are with a legacy organization, what are some of your recommendations that you say you need to be looking at a, B and C? >> Yeah, I would primarily say really need to be looking at some of these newer technologies that can help speed up, people, especially in this case to transition to the Cloud and that planning ahead of time, especially goal-setting, I find to be it's any of these places, providers is absolutely Paramount, because you can, if you don't make your own (indistinct) take that step forward and you can end up with shelter. So I make sure that it's very important that when you commit to that, you commit fully, you plan it out and you make sure you actually use it to get the benefits. One of my tech key is software. So. (chuckles) (Lisa Laughing) I'm a bit of it so. >> Well, you've been there and It costs a lot of money and it doesn't do any good. It doesn't move the business forward. And in this day and age, there is a competitor right behind the rear view mirror who might be smaller, more nimble, and more agile, who can take your place easily. >> Absolutely. >> If the organization isn't willing to take the risks and commit, as you said, Atif last question over for you, where are the customers go to learn more? I know you are at re:Invent your booth 1628, but what do you recommend folks go attendees of the event, as well as just other prospects to go to learn more about what you guys are delivering for companies like Warner Music Group. >> So if you're at re:Invent, please stop by our booth. And one of our Cloud specialists will give you a demo as well. So it's a very quick demo and you'll see, how we are reinventing networking for the Cloud narrow. You can also go to our website and you'll find a lot of information on our website. You can request a demo there as well. So look forward to seeing most of you at our booth and those who are not attending in person, please go visit our website. >> Lisa: Reinventing Networking. I like your play on words. They are Atif very appropriate. Gentlemen, thank you for joining me today talking about Alkira, Warner Music Group, what you guys are doing together and how this new early stage technology is really quite transformative. We appreciate your insights. >> Thank you. >> Thank you so much. >> For Ralph Munsen and Atif Khan, I'm Lisa Martin, and you're watching theCUBE's continuous coverage of AWS re:Invent 2021. Thanks for watching. (soft techno music)

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

>> Thank you for coming to this talk. My name is Amir Safavi-Naeini I'm an Assistant Professor in Applied Physics at Stanford University. And today I'm going to talk about a platform that we've been developing here that allows for quantum and classical information processing using photons and phonons or mechanical motion. So first I'd like to start off, with a picture of the people who did the work. These are graduate students and postdocs in my group. In addition, I want to say that a lot of the work especially on polling of the Lithium niobate was done in collaboration with Martin Fejer's group and in particular Dr.Langrock and Jata Mishra and Marc Jankowski Now our goal is to realize a platform, for quantum coherent information processing, that enables functionality which currently does not exist in other platforms that are available. So in particular we want to have, a very low loss non-linearity that is strong and can be dispersion engineered, to be made broadband. We'd like to make circuits that are programmable and reconfigurable, and that necessitates having efficient modulation and switching. And we'd also really like to have a platform that can leverage some of the advances with superconducting circuits to enable sort of large scale programmable dynamics between many different oscillators on a chip. So, in the next few years what we're really hoping to demonstrate are few photon, optical nonlinear effects by pushing the strength of these non-linearities and reducing the amount of loss. And we also want to demonstrate these coupled, sort of qubit and many oscillators systems. Now the Material system, that we think will enable a lot of these advances is based on lithium niobate, so lithium niobate is a fair electric crystal. It's used very widely in optical components and in acousto optics and then surface acoustic wave devices. It's a fair electric crystal, that has sort of a built-in polarization. And that enables, a lot of effects, which are very useful including the piezoelectric effect, electro- optic effects. And it has a very large K2 optical non-linearity. So it allows for three wave mixing. It also has some effects that are not so great for example, pyroelectricity but because it's very, established material system there's a lot of tricks on how to deal with some of the less attractive parts of it of this material. Now most, Surface Acoustic Wave, or optical devices that you would find are based on kind of bulk lithium niobate crystals that either use surface acoustic waves that propagate on a surface or, you know, bulk waves propagating through a whole crystal, or have a very weak weakly guided low index contrast waveguide that's patterned in the lithium niobate. This was the case until just a little over a decade ago. And this work from ETH Zurich came showing that thin-film lithium niobate can be, bonded and patterned. And Photonic circuits very similar to assigning circuits made from three fives or Silicon can be implemented in this material system. And this really led to a lot of different efforts from different labs. I would say the major breakthrough came, just a few years ago from Marko Loncar, where they demonstrate that high quality factors are possible to realize in this platform. And so they showed resonators with quality factors in the tens of billions corresponding to, line widths of tens of megahertz or losses of, just a few, DB per meter. And so that really changed the picture and you know a little bit after that in collaboration with Martin Fejer's group at Stanford they were able to demonstrate polling and so very large this version engineered nonlinear effects and these types of waveguides. And, and so that showed that, sort of very new types of circuits can be possible on this platform Now our approach is very similar. So we have a thin film of lithium niobate and this time it's on Sapphire instead of oxide or some polymer. and sometimes we put oxide on top. Some Silicon oxide on top, and we can also put electrodes these electrodes can be made out of a superconductor like niobium or aluminum or they can be gold depending on what we're trying to do. The sort of important thing here is that the large index contrast means that, light is guided in a very highly confined waveguide. And it supports bends with small bending radii. And that means we can have resonators that are very small. So the mode volume for the photonic resonators can be very small and as is well known. The interaction rate scale is, one over squared of mode volume. And so we're talking about an enhancement of around six orders of magnitude in the interaction length interaction lengths, over systems using sort of bulk components. And this is in a circuit that's sort of sub millimeter in size and its made on this platform. Now interaction length is important but also quality factor is very important. So when you make these things smaller you don't want to make them much less here. That's, you know, you can look at, for example a second harmonic generation efficiency in these types of resonances and that scales as Q, to the power of three essentially. So you need to achieve, you win a lot by going to low loss circuits. Now loss and non-linearity or sort of material and waveguide properties that we can engineer, but design of these circuits, careful design of these circuits is also very important. For example, you know, because these are highly confined waves and dielectric wave guides they can, you can support several different orders of modes especially if you're working for a broad band light waves that span, you know, an octave. And now when you try to couple light in and out of these structures, you have to be very careful that you're only picking up the polarizations that you care about, and you're not inducing extra loss channels effectively reducing the queue, even though there's no material loss if you're these parasitic coupling, can lead to lower Q. so the design is very important. This plot demonstrates, you know, the types of extrinsic to intrinsic coupling that are needed to achieve very high efficiency SHG, which is unrelated to optical parametric oscillation. And, you know, you, so you sort of have to work in a regime where the extrinsic couplings are much larger than the intrinsic couplings. And this is generally true for any type of quantum operation that you want to do. So just just low material loss itself isn't enough to design is also very important. In terms of where we are, on these three important aspects like getting large G large Q and large cap up. So we've been able to achieve high Q in, in these structures. This is a Q a of a couple million, we've also been able to you can see from a broad transmission spectrum through a grading coupler you can see a very evenly spaced modes showing that we're only coupling to one mode family. And we can see that the depth of the modes is also very large, you know, 90% or more. And that means that our extrinsic coupling in intrinsic coupling is also very large. So we've been able to kind of engineer these devices and to achieve this in terms of the interaction, I won't go over it too much but, you know, in collaboration with Marty Feres group we were able to pull both lithium niobate on insulator and lithium niobate on Sapphire. We'll be able to see a very efficient, sort of high slope proficiency second harmonic generation, you know achieving approaching 5000% per watt centimeters squared for 1560 to 780 conversion. So this is all work in progress. And so for now, I'd like to talk a little bit about the integration of acoustic and mechanical components. So, first of all why would we want to integrate mechanical components? Well, there's lots of cases where, for example you want to have an extremely high extinction switching functionality. That's very difficult to do with electro optics because they need to control the phase, extremely efficiently with extreme precision. You would need very large, long resonators and or large voltages becomes very difficult to achieve you know, 60 DB types of, switching. Mechanical systems. On the other hand, they can have very small mode volumes and can give you 60 DB switching without too many complications. Of course the drawback is that they're slower, but for a lot of applications, that doesn't matter too much. So in terms of being able to make integrate memes, switching and tuning with this platform, here's a device that achieves that so that each of these beams is actuated through the Piezoelectric effect and lithium niobate via this pair of electrodes that we put a voltage across. And when you put a voltage across these have been designed to leverage one of the off diagonal terms in the piezoelectric tensor, which causes bending. And so this bending generates a very large displacement in the center of this beam, in this beam, you might notice is composed of a grading, and this grading effectively generates it's photonic crystal cavity. So it generates a localize optical mode in the center which is very sensitive to these displacements. And what we're able to see in this system is that you know, just a few millivolts so 50 millivolts here shifts the resonance frequency by much more than a line width just a few millivolts is enough to shift by a line width. And so to achieve switching we can also tune this resonance across the full telecom band and these types of devices whether in waveguide resonator form can be extremely useful for sort of phase control in a large scale system, where you might want to have many many face switches on a chip to control phases with, with low loss, because these wave guides are shorter. You have lower loss propagating across them. Now, these interactions are fairly low frequency. When we go to higher frequency, we can use the electro-optic effect. And even the electro-optic effect even though it's very widely used, and well-known on a Photonic circuit like these lithium Niobate for tying circuits has, interesting consequences and device opportunities that don't exist on the bulk devices. So for example, let's look at single sideband modulation. This is what an electro-optic sort of standard electro optics, single sideband modulator looks like you, you take your light, you split into two parts, and then you modulate each of these arms. You modulate them out of phase with an RFC tone that's out of phase. And so now you generate side bands on both and now because they're modulating out of phase when they are recombined and on the output splitter and this mock sender interferometer you end up dropping one of the side bands and then the pump and you end up with a shifted side pan. So that's possible you can do single side band modulation with an electronic device but the caveat is that this is now fundamentally lossy. So, you know, you have generated, this other side band via modulation, and the sideband is simply being lost due to interference. So it's their, It's getting combined, it's getting scattered away because there's no mode that it can get connected to. So actually you know, this is going kind of an efficiency less than 3DB usually much less than 3DB. And that's fine if you just have one of these single sideband modulators because you can always amplify, you can send more power but if you're talking about a system and you have many of these and you can't put amplifiers everywhere then, or you're working with quantum information where loss is particularly bad. This is not an option. Now, when you use resonators, you have another option. So here's a device that tries to demonstrate this. This is two resonators that are brought into the near-field of each other. So they're coupled with each other over here where they're, which causes a splitting. And now when we tune the DC voltage was tuned one of these resonators by sort of changing the effective half lengths And one of these resonators tunes, the frequency, we can see an We should see an anti crossing between the two modes and at the center of this splitting this is versus voltage, a splitting at the center at this voltage, let's say here it's around 15 volts. We can see two residences two dips, when we probed the line field going through. And now if we send in the pump resonant, with one of these, and we modulate at this difference frequency we generate this red side band but we actually don't generate the blue side band because there's no optical density of state. So the, so because there's this other side may has just not generated. This system is now much more efficient. In fact, so in Marco Loncar has give they've demonstrated. You can get a hundred percent conversion. And we've also demonstrated this in a similar experiment showing that you can get very large sideband suppression. So, you know more than 30 DB suppression of the side bands with respect to the sideband that you care about It's also interesting that these interactions now preserve quantum coherence. And this is one path to creating links between superconducting microwave systems and optical components. Because now the microwave signal that's scattered here preserves its coherence. So we've also been able to do acoustic optic interactions at these high frequencies. This is a, this is an acoustic optic modulator that operates at a few gigahertz. Basically you generate electric field here which generates a propagating wave inside this transducer made out of lithium niobate. These are aluminum electrodes on top. The phonons are focused down into a small phononic waveguides that guides mechanical waves. And then these are brought into this crystal area where the sound and the Mo and the light are both convert confined to wavelength skill mode volume and they interact very strongly with each other. And the strong interaction leads to very efficient, effective electro-optic modulation. So here we've been able to see, with just a few microwatts of power, many, many side bands being generated. So this is a fact that they like tropic much later where the VPI is, a few thousands of a volt instead of, you know, several volts, which is sort of the off the shelf, electro-optic modulator that you would find. And importantly, we've been able to combine these, photonic and phononic circuits into the same platform. So this is a lithium niobate on same Lithium niobate on Sapphire platform. This is an acoustic transducer that generates mechanical waves that propagate in this lithium niobate waveguide. You can see them here and we can make phononic circuits now. so this is a ring resonate. It's a ring resonator for phonons. So we send sound waves through. And when it's resonance, when its frequency hits the ring residences, we see peaks. and this is, this is cheeks in the drop port coming out. And what's really nice about this platform is that we actually don't need to unlike unlike many memes platforms where you have to have released steps that are usually not compatible with, you know other devices here, there's no release steps. So the phonons are guided in that thin lithium niobate layer. The high Q of these mechanical modes shows that these mechanical resonances can be very coherent oscillators. And so we've also worked towards integrating these with very non-linear microwave circuits to create strongly interacting phonons and phonon circuits. So this is a example of an experiment we did over a year ago, where we have sort of a superconducting Qubit circuit with mechanical resonances made out of lithium niobate shunting the Qubit capacitor to ground. So now vibrations of this mechanical oscillator generate a voltage across these electrodes that couples to the Qubits voltage. And so now you have an interaction between this qubit and the mechanical oscillator, and we can see that in the spectrum of the qubit as we tune it across the frequency band. And we see splittings every time the qubit frequency approaches the mechanical resonance frequency. And infact this coupling is so large, that we were able to observe for the first time, the phonon spectrum. So we can detune this qubit away from the mechanical resonance. And now you have a dispersive shift on the qubit which is proportional to the number of phonons. And because number of photons is quantized. We can actually see, the different phonon levels in the qubit spectrum. Moving forward, we've been trying to, also understand what the sources of loss are in the system. And we've been able to do this by demonstrating by fabricating very large rays in these mechanical oscillators and looking at things like, their quality factor versus frequency. This is an example of a measurement that shows a jump in the quality factor when we enter the frequency band where we expect our phononic band gap for this period, periodic material is this jump you know, in principle,if loss were only due to clamping only due to acoustic waves leaking out in these out of these ends, then this change in quality factor quality factor should go to essentially infinite or should be ex exponential losses should be exponentially suppress with the length So these, but it's not. And that means we're actually limited by other loss channels. And we've been able to determine that these are two level systems and the lithium niobate by looking at the temperature dependence of these losses and seeing that they fit very well sort of standard models that exist for the effects of two level systems on microwave and mechanical resonances. We've also started experimenting with different materials. In fact, we've been able to see that, for example, going to lithium niobate, that's dope with magnesium oxide changes or reduces significantly the effect of the two level systems. And this is a really exciting direction of research that we're pursuing. So we're understanding these materials. So with that, I'd like to thank the sponsors. NTTResearch, of course, a lot of this work was funded by DARPA, ONR, RAO, DOE very generous funding from David and Lucile Packard foundation and others that are shown here. So thank you.

Hi, I'm Hideo Mabuchi from Stanford University. This is my presentation on coherent nonlinear dynamics, and combinatorial optimization. This is going to be a talk, to introduce an approach, we are taking to the analysis, of the performance of Coherent Ising Machines. So let me start with a brief introduction, to ising optimization. The ising model, represents a set of interacting magnetic moments or spins, with total energy given by the expression, shown at the bottom left of the slide. Here the cigna variables are meant to take binary values. The matrix element jij, represents the interaction, strength and sign, between any pair of spins ij, and hi represents a possible local magnetic field, acting on each thing. The ising ground state problem, is defined in an assignment of binary spin values, that achieves the lowest possible value of total energy. And an instance of the easing problem, is specified by given numerical values, for the matrix j and vector h, although the ising model originates in physics, we understand the ground state problem, to correspond to what would be called, quadratic binary optimization, in the field of operations research. And in fact, in terms of computational complexity theory, it can be established that the, ising ground state problem is NP complete. Qualitatively speaking, this makes the ising problem, a representative sort of hard optimization problem, for which it is expected, that the runtime required by any computational algorithm, to find exact solutions, should asyntonically scale, exponentially with the number of spins, and four worst case instances at each end. Of course, there's no reason to believe that, the problem instances that actually arise, in practical optimization scenarios, are going to be worst case instances. And it's also not generally the case, in practical optimization scenarios, that we demand absolute optimum solutions. Usually we're more interested in, just getting the best solution we can, within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for computation. This focus is great interest on, so-called heuristic algorithms, for the ising problem and other NP complete problems, which generally get very good, but not guaranteed optimum solutions, and run much faster than algorithms, that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem, for which extensive compilations, of benchmarking data may be found online. A recent study found that, the best known TSP solver required median runtimes, across a library of problem instances, that scaled as a very steep route exponential, for an up to approximately 4,500. This gives some indication of the change, in runtime scaling for generic, as opposed to worst case problem instances. Some of the instances considered in this study, were taken from a public library of TSPs, derived from real world VLSI design data. This VLSI TSP library, includes instances within ranging from 131 to 744,710, instances from this library within between 6,880 and 13,584, were first solved just a few years ago, in 2017 requiring days of runtime, and a 48 core two gigahertz cluster, all instances with n greater than or equal to 14,233, remain unsolved exactly by any means. Approximate solutions however, have been found by heuristic methods, for all instances in the VLSI TSP library, with, for example, a solution within 0.014% of a known lower bound, having been discovered for an instance, with n equal 19,289, requiring approximately two days of runtime, on a single quarter at 2.4 gigahertz. Now, if we simple-minded the extrapolate, the route exponential scaling, from the study yet to n equal 4,500, we might expect that an exact solver, would require something more like a year of runtime, on the 48 core cluster, used for the n equals 13,580 for instance, which shows how much, a very small concession on the quality of the solution, makes it possible to tackle much larger instances, with much lower costs, at the extreme end, the largest TSP ever solved exactly has n equal 85,900. This is an instance derived from 1980s VLSI design, and this required 136 CPU years of computation, normalized to a single core, 2.4 gigahertz. But the 20 fold larger, so-called world TSP benchmark instance, with n equals 1,904,711, has been solved approximately, with an optimality gap bounded below 0.0474%. Coming back to the general practical concerns, of applied optimization. We may note that a recent meta study, analyze the performance of no fewer than, 37 heuristic algorithms for MaxCut, and quadratic binary optimization problems. And find the performance... Sorry, and found that a different heuristics, work best for different problem instances, selected from a large scale heterogeneous test bed, with some evidence, the cryptic structure, in terms of what types of problem instances, were best solved by any given heuristic. Indeed, there are reasons to believe, that these results for MaxCut, and quadratic binary optimization, reflect to general principle, of a performance complementarity, among heuristic optimization algorithms, and the practice of solving hard optimization problems. There thus arises the critical pre processing issue, of trying to guess, which of a number of available, good heuristic algorithms should be chosen, to tackle a given problem instance. Assuming that any one of them, would incur high cost to run, on a large problem of incidents, making an astute choice of heuristic, is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight, about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This is certainly pinpointed by researchers in the field, as a circumstance and must be addressed. So adding this all up, we see that a critical frontier, for cutting edge academic research involves, both the development of novel heuristic algorithms, that deliver better performance with lower costs, on classes of problem instances, that are underserved by existing approaches, as well as fundamental research, to provide deep conceptual insight, into what makes a given problem instance, easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law, and speculate about a so-called second quantum revolution, it's natural to talk not only about novel algorithms, for conventional CPUs, but also about highly customized, special purpose hardware architectures, on which we may run entirely unconventional algorithms, for common tutorial optimizations, such as ising problem. So against that backdrop, I'd like to use my remaining time, to introduce our work on, analysis of coherent using machine architectures, and associated optimization algorithms. Ising machines in general, are a novel class of information processing architectures, for solving combinatorial optimization problems, by embedding them in the dynamics, of analog, physical, or a cyber-physical systems. In contrast to both more traditional engineering approaches, that build ising machines using conventional electronics, and more radical proposals, that would require large scale quantum entanglement the emerging paradigm of coherent ising machines, leverages coherent nominal dynamics, in photonic or optical electronic platforms, to enable near term construction, of large scale prototypes, that leverage posting as information dynamics. The general structure of current of current CIM systems, as shown in the figure on the right, the role of the easing spins, is played by a train of optical pulses, circulating around a fiber optical storage ring, that beam splitter inserted in the ring, is used to periodically sample, the amplitude of every optical pulse. And the measurement results, are continually read into an FPGA, which uses then to compute perturbations, to be applied to each pulse, by a synchronized optical injections. These perturbations are engineered to implement, the spin-spin coupling and local magnetic field terms, of the ising hamiltonian, corresponding to a linear part of the CIM dynamics. Asynchronously pumped parametric amplifier, denoted here as PPL and wave guide, adds a crucial nonlinear component, to the CIM dynamics as well. And the basic CIM algorithm, the pump power starts very low, and is gradually increased, at low pump powers, the amplitudes of the easing spin pulses, behave as continuous complex variables, whose real parts which can be positive or negative, by the role of soft or perhaps mean field spins. Once the pump power crosses the threshold, for perimetric self oscillation in the optical fiber ring, however, the amplitudes of the easing spin pulses, become effectively quantized into binary values, while the pump power is being ramped up, the FPGA subsystem continuously applies, its measurement based feedback implementation, of the using hamiltonian terms. The interplay of the linearized easing dynamics, implemented by the FPGA , and the thresholds quantization dynamics, provided by the sink pumped parametric amplifier, result in a final state, of the optical plus amplitudes, at the end of the pump ramp, that can be read as a binary strain, giving a proposed solution, of the ising ground state problem. This method of solving ising problems, seems quite different from a conventional algorithm, that runs entirely on a digital computer. As a crucial aspect, of the computation is performed physically, by the analog continuous coherent nonlinear dynamics, of the optical degrees of freedom, in our efforts to analyze CA and performance. We have therefore turn to dynamical systems theory. Namely a study of bifurcations, the evolution of critical points, and typologies of heteroclitic orbits, and basins of attraction. We conjecture that such analysis, can provide fundamental insight, into what makes certain optimization instances, hard or easy for coherent ising machines, and hope that our approach, can lead to both improvements of the course CIM algorithm, and the pre processing rubric, for rapidly assessing the CIM insuibility of the instances. To provide a bit of intuition about how this all works. It may help to consider the threshold dynamics, of just one or two optical parametric oscillators, in the CIM architecture just described. We can think of each of the pulse time slots, circulating around the fiber ring, as are presenting an independent OPO. We can think of a single OPO degree of freedom, as a single resonant optical mode, that experiences linear dissipation, due to coupling loss, and gain in a pump near crystal, as shown in the diagram on the upper left of the slide, as the pump power is increased from zero. As in the CIM algorithm, the non-linear gain is initially too low, to overcome linear dissipation. And the OPO field remains in a near vacuum state, at a critical threshold value, gain equals dissipation, and the OPO undergoes a sort of lasing transition. And the steady States of the OPO, above this threshold are essentially coherent States. There are actually two possible values, of the OPO coherent amplitude, and any given above threshold pump power, which are equal in magnitude, but opposite in phase, when the OPO cross this threshold, it basically chooses one of the two possible phases, randomly, resulting in the generation, of a single bit of information. If we consider two uncoupled OPOs, as shown in the upper right diagram, pumped at exactly the same power at all times, then as the pump power is increased through threshold, each OPO will independently choose a phase, and thus two random bits are generated, for any number of uncoupled OPOs, the threshold power per OPOs is unchanged, from the single OPO case. Now, however, consider a scenario, in which the two appeals are coupled to each other, by a mutual injection of their out coupled fields, as shown in the diagram on the lower right. One can imagine that, depending on the sign of the coupling parameter alpha, when one OPO is lasing, it will inject a perturbation into the other, that may interfere either constructively or destructively, with the field that it is trying to generate, via its own lasing process. As a result, when can easily show that for alpha positive, there's an effective ferromagnetic coupling, between the two OPO fields, and their collective oscillation threshold, is lowered from that of the independent OPO case, but only for the two collective oscillation modes, in which the two OPO phases are the same. For alpha negative, the collective oscillation threshold, is lowered only for the configurations, in which the OPO phases are opposite. So then looking at how alpha is related to the jij matrix, of the ising spin coupling hamilitonian, it follows the, we could use this simplistic to OPO CIM, to solve the ground state problem, of the ferromagnetic or antiferromagnetic angles, to ising model, simply by increasing the pump power, from zero and observing what phase relation occurs, as the two appeals first start to lase. Clearly we can imagine generalizing the story to larger, and, however, the story doesn't stay as clean and simple, for all larger problem instances. And to find a more complicated example, we only need to go to n equals four, for some choices of jij for n equals four, the story remains simple, like the n equals two case. The figure on the upper left of this slide, shows the energy of various critical points, for a non frustrated n equals for instance, in which the first bifurcated critical point, that is the one that, by forgets of the lowest pump value a, this first bifurcated critical point, flows asyntonically into the lowest energy using solution, and the figure on the upper right, however, the first bifurcated critical point, flows to a very good, but suboptimal minimum at large pump power. The global minimum is actually given, by a distinct critical point. The first appears at a higher pump power, and is not needed radically connected to the origin. The basic CIM algorithm, is this not able to find this global minimum, such non-ideal behavior, seems to become more common at margin end, for the n equals 20 instance show in the lower plots, where the lower right pod is just a zoom into, a region of the lower left block. It can be seen that the global minimum, corresponds to a critical point, that first appears that of pump parameter a around 0.16, at some distance from the adriatic trajectory of the origin. That's curious to note that, in both of these small and examples, however, the critical point corresponding to the global minimum, appears relatively close, to the adiabatic trajectory of the origin, as compared to the most of the other, local minimum that appear. We're currently working to characterise, the face portrait typology, between the global minimum, and the adiabatic trajectory of the origin, taking clues as to how the basic CIM algorithm, could be generalized, to search for non-adiabatic trajectories, that jumped to the global minimum, during the pump up, of course, n equals 20 is still too small, to be of interest for practical optimization applications. But the advantage of beginning, with the study of small instances, is that we're able to reliably to determine, their global minima, and to see how they relate to the idea, that trajectory of the origin, and the basic CIM algorithm. And the small land limit, We can also analyze, for the quantum mechanical models of CAM dynamics, but that's a topic for future talks. Existing large-scale prototypes, are pushing into the range of, n equals, 10 to the four, 10 to the five, 10 to the six. So our ultimate objective in theoretical analysis, really has to be, to try to say something about CAM dynamics, and regime of much larger in. Our initial approach to characterizing CAM behavior, in the large end regime, relies on the use of random matrix theory. And this connects to prior research on spin classes, SK models, and the tap equations, et cetera, at present we're focusing on, statistical characterization, of the CIM gradient descent landscape, including the evolution of critical points, And their value spectra, as the pump powers gradually increase. We're investigating, for example, whether there could be some way, to explain differences in the relative stability, of the global minimum versus other local minima. We're also working to understand the deleterious, or potentially beneficial effects, of non-ideologies such as asymmetry, in the implemented using couplings, looking one step ahead, we plan to move next into the direction, of considering more realistic classes of problem instances, such as quadratic binary optimization with constraints. So in closing I should acknowledge, people who did the hard work, on these things that I've shown. So my group, including graduate students, Edwin Ng, Daniel Wennberg, Ryatatsu Yanagimoto, and Atsushi Yamamura have been working, in close collaboration with, Surya Ganguli, Marty Fejer and Amir Safavi-Naeini. All of us within the department of applied physics, at Stanford university and also in collaboration with Yoshihisa Yamamoto, over at NTT-PHI research labs. And I should acknowledge funding support, from the NSF by the Coherent Ising Machines, expedition in computing, also from NTT-PHI research labs, army research office, and ExxonMobil. That's it. Thanks very much.