Simon Crosby has made a career of technology on the edge. From leading the charge with the Xen hypervisor to microVMs with Bromium and much more in between, Crosby has led some of the most innovative technology developers in the industry.
If Simon is involved, there is a good chance it will succeed as well. Have a listen to what Simon is all about now.
As usual, the streaming audio is immediately below, followed by the transcript of our conversation.
Transcript
Alan Shimel: Hey, everyone this is Alan Shimel, DevOps.com, Security Boulevard, and we’re here for another DevOps Chat. I’m really lucky to be joined by an old friend of mine for this chat, none other than Simon Crosby who is the CTO of a hot company out there called swim.ai. Simon, welcome.
Simon Crosby: Thanks, Alan. It’s great to speak to you again.
Shimel: Thank you. It’s actually my pleasure. Simon, maybe we have some young ones in the audience who maybe aren’t familiar with the one and only Simon Crosby. Why don’t you give folks a little background of how you came to be here today? Little bit of _____ –
Crosby: Well, I was lucky enough to cofound a company called “XenSource,” which built and maintained the open-source Xen hypervisor, which was – so XenSource was bought by Citrix, but then went on to fame because we built Amazon AWS. And then on, from there, I then cofounded a company called “Bromium,” which used micro-virtualization to do security. And that has, funnily enough, turned also into Amazon’s micro-virtualization, which they very recently open-sourced, as well as the micro-virtualization in Azure.
Shimel: So, in many ways, you’ve given birth to so – “given birth to”? – you’ve helped launch so many of the key technologies that are powering the cloud today, right?
Crosby: It’s fun.
Shimel: Yeah. You know, I think a lot of people tend to think hypervisor, VMware, and they forget, really, how important Xen was and still is.
Crosby: Still is. It still has more VMs than any other hypervisor on the planet.
Shimel: Absolutely. And then Bromium too – I mean, Bromium micro-segmentation, micro-perimeterization, whatever you wanna call it, again, was a – I mean, it was really a defining moment, you know? It was a –
Crosby: Yeah. So I –
Shimel: – _____ approach security.
Crosby: So what Bromium does is every single task on the OS, every tab in your browser, we document your residency – its own little micro-VM.
Shimel: Right.
Crosby: And those things are tiny. And so if bad guys show up, it’s _____ _____. That technology has gone into Windows 10. It’s coming out in stages from Microsoft and it’s also available in Azure and in AWS.
Shimel: Fantastic. But enough about that. We’re here to talk about SWIM.AI. And I’m gonna assume that most of our audience, probably, has not heard of SWIM.AI. They may get from the title that it is based on SWIM, but they may or may not even know SWIM. Why don’t you clue us in, Simon?
Crosby: Well, the name is kind of a clue – it’s “software in motion.” We’re focused on the edge – in fact, intelligent edge computing – and so I’m gonna bend your mind a bit with a definition here. We’re all familiar with cloud and cloud computing as a set of abstractions for building stuff, not as a place, right? So, when we think of it, I’d like you to think of edge computing as needing a set of abstractions. And I think the current abstractions we use for cloud don’t apply very well at the edge. So let’s think of edge computing as a new set of abstractions, the ones that we need to deal with the world, which is generating more data than we can ever hope to store. And so what are the abstractions to do intelligent computing on that?
Now, when we talk about edge intelligence, I mean, I guess four things: first of all, self-training and inference; analytics; any form of query – MapReduce, relational graph cap query, or any old other thing that you might wanna throw into data to analyze. And the key challenges with the current paradigms, the cloud world, which has been so tremendously successful, is really three things: REST, stateless computing, and the notion of a database. And all of these things are vital for all of the apps that we know and love today, but they completely screw up everything that you’re trying to do at the edge.
So just think about this way: if I have an event coming of some sensor at the edge and I throw it up to an AWS Lambda thing, which then goes and looks up the old value in a database, does some computation and puts a new value in database, you know, that’s probably taken 100 milliseconds, 50 milliseconds maybe. If I’m running towards the edge in a distributed application, that’s tens of billions of CPU cycles.
Shimel: Okay.
Crosby: And so this notion of forgetting everything, which is really is focused scaling out the cloud, is terrible at the edge. What you want is a STAPLE model. And so STAPLE computation is fundamental in the edge – anybody who’s written a device driver knows this – and distribution is fundamental. Computing on data as it is generated, which means learning on the fly, analyzing on the fly, are fundamental. Okay? So not storing things on disks and pulling it off later because then you’re analyzing past data and you can only reason about the past. And we have to reason about things as they happen and respond in real time.
Shimel: You know, what’s interesting about this to me, Simon, is – so I guess it’s gotta be, what, 2004, 2005, we start really seeing the cloud go mainstream, if you will. And I –
Crosby: 2000 – you know what? AWS is beginning of 2006.
Shimel: Okay. So fair enough.
Crosby: Oh, but Salesforce, obviously, is a long time ago, but there you go.
Shimel: Yeah, but, you know, this thing “the cloud,” right, and how it kind of exploded on the scene. And your company sort of started in ASP, back in the late ’90s – it was hosting Lotus Notes before there were hypervisors and clouds and _____ bandwidth. But, anyway, part and parcel of the cloud, though, was this notion that there would be this centralized – if not, but it would be distributed – sort of center where certain computation and hosting, like database, et cetera, would – and storage – would happen at that nebulous cloud. But there was always supposed to be the other piece of it, the edge. Right? There was the cloud and the edge. And we got so enamored with the cloud and so much happened with AWS and Azure and Google and a million other clouds out there, hybrid and all privates, we forgot about the edge for about –
Crosby: Well, I think –
I mean, I wanna be a little bit generous here. I think job one was to get enterprise IT out of bring in IT. In other words, get out of running exchange _____ _____ use things like Boomawipe because, gosh, let them run – let somebody else run their stuff for you. It’s not mission-critical. So the trillion dollars of traditional enterprise IT needs to get pushed to the cloud, and that’s happening at a huge pace today, and you see the winners emerging there. But there’s another trend which is happening and that is that, well, if you look at ARM, you know, ARM is licensing 20 billion cores a year.
Shimel: Wow.
Crosby: That’s a big number of CPUs. And every one of those is gonna be connected and generating data. And so that is a trend which is caught up. It’s because everything is smart now, right? You can embed these cores in anything. And so, when you deal with a world which has got that much data coming out of it, we have to do something other than simply save everything in a big data store and think about it later because there will never be a later. There is so much data coming out. And so there is a fundamental requirement that we process this data on the fly. I wanna introduce you a slightly mind-twisting thought. There is going to be so much data that things have to write their own programs. There aren’t enough humans to write programs for the things, right?
Shimel: Yeah, no, I agree.
Crosby: The things generating data will learn experientially, a little bit like you do – okay, you form a hypothesis about the world; you see the way the world develops; and then you change your hypothesis – so things will do that and they will learn on their own data. And, in “learning,” what I mean is you might – they’re writing a program, effectively, these things, right? So the program might be the wait of a DNN or something, and you might not understand what it means, but, when they have absorbed enough data, they’re really good at understanding their world and predicting the way it will evolve around. And that’s the way the world is going, that things write their own programs.
Shimel: You know, I had a conversation with a guy named Luke Kiely, about two or three years ago. Luke was the founder and CEO, at the time, of Puppet. And he had a similar thing – he said, “Alan, it may not be this year, next year, five years or maybe even 10 years or more, but there will come a time where the software needs to write its own software.”
Crosby: Yeah.
Shimel: But, you know, it just – the humans are the bottleneck. Right? The human is the resource constraint.
Crosby: Well, actually, I mean, they’re – we’re on DevOps podcast, but, you know, there will never be DevOps for my younger _____ _____ _____ _____. I’m telling you, there just will never be that. There aren’t enough people to do this, right? And so we have to back up –
Shimel: Go ahead. No, no, you go on ’cause I agree with you. I mean, the _____ – I mean, the human is the single – just like in security, a lot of times, it was – that was the problem. [Chuckles]
Crosby: Yep. No, that’s right. And so more automation is key. By the way, there’s another skillset which I think is hypothesized too much, and that is data science. I mean, programmers are rare, good programmers rarer. Data scientists? Good luck trying to find one.
Shimel: You are not kidding.
Crosby: Hard to find, right? And so there is this workflow which both Amazon and – well, Amazon, Microsoft, and Google posit, which is this: collect all the data; somebody builds a model, trains the model, and then pushes this model towards the edge, where it can do inference. You know, that’s just hypothetical nonsense. Okay? It’s not gonna happen. [Chuckles]
Shimel: Go ahead.
Crosby: And, by the way, so, first of all, you’re gonna collect all the data and, by the way, they’re happy if you do that ’cause then you’re stuck.
Shimel: Right.
Crosby: Right? ‘Cause then you’re stuck on that cloud.
Shimel: Exactly.
Crosby: And then, you know, you gotta find this person to build a model and train it. That’s really hard. And if the data is wrong, then you get over- or under-fitting, and then you gotta push all that stuff to edge. Nobody knows how to do that. It’s just not –
Shimel: No, they don’t. You know, I was talking – I guess it was maybe last week. A friend of mine, actually from this gym we go to – she’s a recruiter. She’s recruiting down in San Jose and they’re trying to get Hadoop folks, just basic Hadoop programmers. And, Simon, look, I know things are crazy in the valley. I think they were paying maybe $240,000.00 or $250,000.00 and they –
Crosby: Unbelievable.
Shimel: They can’t find them, though. They can’t hire them. There’s not enough people with that data scientist, with that type of big data kind of background.
Crosby: There aren’t. And I think, you know, as we move towards a more real-time business environment, organizations are going – let’s posit a future for enterprise IT. Enterprise IT, as you know, today, it needs to change towards something where the gunk is dealt with by somebody else. And then enterprise IT needs to feed real-time insights for different components of the business to the stakeholders, so they can make better decisions.
Shimel: Okay.
Crosby: Yep. And whether that’s supply chain or employees or whatever it happens to be, we need to put enterprise IT into position to deliver real-time insights. And how are they gonna do that? That’s really interesting problem.
Shimel: Absolutely. But there’s something else to what you said too, Simon, that I wanted to hit on and that is – you know, you mentioned – what’d you say? – ARM had 20 billion or licensed 20 billion CPUs?
Crosby: Yeah, per year.
Shimel: Per year. You know, to me, you look at Uber, right? Some people will argue, “Is Uber a software company? Is Uber a drive company? What is Uber?” What Uber really was is they saw a glut of resource, right? People had cars that they weren’t using all the time, that they could monetize. Right? To a certain extent, it’s the same thing here – the amount of computing power on the edge or potential computing power on the edge.
Crosby: Yeah.
Shimel: So massive. Right?
Crosby: Yeah, it’s huge. And so let me give – you’re actually right – so let me give you a really shocking example. So we solve this problem of prediction of traffic and I’ll give you two ways of doing it. And my example is the city of Palo Alto in California.
Shimel: Mm-hmm.
Crosby: And so we get about four terabytes a day from Palo Alto. That’s bigger than Twitter.
Shimel: Wow.
Crosby: Bigger than all of Twitter. Okay?
Shimel: Mm-hmm.
Crosby: So you could take all that data and push it up to AWS Lambda, into a model, and then learn and predict. Okay? Cool. That’ll cost you about $8,000.00 a month. Okay? And learning at the edge, as the data streams off the infrastructure, we bought no new hardware and it costs us pennies.
Shimel: Wow.
Crosby: Okay? So I’m saying thousands of times cheaper than AWS. Okay? So REST, statelessness, and databases are my enemy, number one, and then you’re absolutely right – there is a ton of unused CPU towards the edge, out in this distributed real world.
Shimel: Nature abhors a vacuum.
Crosby: And if I have 10 billion spare CPUs, I mean, I can do an inferencing cycle on Raspberry Pi in the time that it takes a packet to get from the city to AWS.
Shimel: To AWS and then coming back. Mm-hmm.
Crosby: We’re done, right?
Shimel: Yeah. Yeah. Hey, Simon, as I mentioned, the time goes really quick. I wanna focus, ’cause your marketing people appreciate it, Swim.ai. How can people engage? What do you recommend how do they engage?
Crosby: Okay. So what is Swim? Swim is a platform for application developers that want to develop massively distributed, stateful, real-time applications. Okay? It’s a tiny, two-megabyte extension to JVM, okay, which deploys and replaces a huge stack of cloud-based stuff, including the analytics and learning software, the pipelines, Pub/Sub, all the databases you might want. Okay? So we replace millions and millions of lines of code with something very simple.
And it’s an extension of JVM because, that way, we get architecture independence. We found ourselves running ARM and X86 and everything else, all at the same time. And all you have to do is define what’s in your data and then SWIM builds a model for you, trains the model, and does the inference and prediction. Okay? So, simple, right? So the goal here is to make it simple for somebody who is a competent Java programmer, can do a bit of the business of doing really complex analytical stuff on streaming data, towards the edge.
Shimel: Excellent. And, obviously, the website is swim.ai, yeah?
Crosby: Yeah, developer.swim.ai is where you’d go and download it and run apps and everything else. You can download the platform, as well as the example app, so, if you wanna do something like New Relic, download Swim, stick it in each one of your VMs, and you’re up and running in a flash.
Shimel: Excellent. So, Simon, I think I already know the answer to this, but I’m gonna ask it anyway. You have – I mean, your reputation precedes you, your accomplishments. What got you really excited about Swim.ai?
Crosby: You know, if you can deliver improvements in performance and timeliness, which are in the orders of thousands – not 10X, 1,000X, okay? – and really change the world in dramatic way, that’s really exciting. So let me just – let me give you a somewhat critical view of the cloud, right? The cloud vendors make huge margins keeping memory warm for lots and lots of copies of Windows or Linux. Okay?
Shimel: Uh-huh. Okay.
Crosby: And most of that is idle.
Shimel: I got it.
Crosby: Most of those things are sitting there, doing nothing –
Shimel: Just waiting.
Crosby: – while REST happens. And we’ve moved compute from binary into these ASCII-based REST interfaces with lots and lots of pausing. Okay, cool. And now you’re paying for it.
Shimel: Yeah.
Crosby: What I’m saying is that you can do that same computation really enormously cheaply, towards the edge, on stuff you already own. And that’s very interesting.
Shimel: It really – and I could say, “Well, it’s sort of as expected.” Well, Simon, people can go to the website to find out more. You’re talking at any conferences or the company is _____ –
Crosby: Yeah, so we’re launching Swim app at the developer week in Oakland, on Thursday.
Shimel: This Thursday? Which would be February 21st.
Crosby: Yeah.
Shimel: Well, by the time this is up, that might have passed, but they can probably check it out there and see in the website. Well, Simon, if it’s anything like the other stuff you work on, this is gonna be great.
Crosby: Thank you, sir. It’s a privilege to spend time with you.
Shimel: We’ll be watching you closely. We wish you lots of luck. And, you know, living life on the computing edge –
Crosby: It’s fun.
Shimel: It is fun. Hey, man, it’s great catching up with you and best of luck, Simon.
Crosby: Thank you, Alan. It’s great to speak to you again.
Shimel: All righty. Simon Crosby, CTO of Swim.ai. Check him out, computing on the edge. This is Alan Shimel for DevOps.com. Have a great day, everyone.
