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Microsoft Azure CTO Mark Russinovich (Photo courtesy Web Summit / cc2.0)

For Microsoft Azure, 2017 will probably be remembered as the year of serverless and edge computing; the two concepts have made their way into just about every appearance by one of its executives and have been a large part of its product strategy, as well.

Usually they are discussed separately, but I had a chance to catch up with Azure chief technology officer Mark Russinovich at the Structure conference in San Francisco last week, and we wound up talking about the intersection of serverless computing techniques — applications designed around events and functions — and edge computing, the notion that more and more computing is moving away from central data centers as connected devices and the industrial internet continue to evolve.

What follows is a lightly edited version of our conversation.

GeekWire: One thing I’m having a little bit of trouble understanding is that serverless is supposed to offer a degree of portability to other cloud providers (according to some backers). But, it seems like it’s almost the opposite, where if you build around a certain provider’s functions it’s not going to be that easy to migrate what you’ve just done to another function provider.

Mark Russinovich: I think that if you take a look at the serverless offerings — ours, Azure Functions, (Amazon Web Services’) Lambda, Google Cloud Functions, OpenWhisk — they’re all different. Basically, they’re application environments, so you need to write code and operations that work around those specific offerings.

As far as the portability of it, I think when you talk about serverless, you’re talking about the ‘as a service’ aspect as a key part of it. That also feeds into the operational requirements and design of an application that’s leveraging serverless: what capability does the platform offers, what SLAs it has, what kind of performance it delivers.

When it comes to portability, meaning, “can I run this Azure function outside of Azure?” this has been an explicit goal of ours. It’s open source, so I can actually take it and put that runtime on anything. I can run it on other clouds, for example. But you don’t have the ‘as-a-service’ aspect of it at that point.

If you take a look at serverless — the pure, canonical definition that we’ve seen the industry adopt — it is event-driven, meaning it auto-scales and then it’s micro-billed. But if you’re going run it on your own infrastructure, micro-billing doesn’t apply, and serverless doesn’t necessarily apply, because you’re going to have to manage the infrastructure underneath it and write the scaling logic.

We believe in, actually, a broader definition, and that is that serverless is typically micro-billed, auto scaled (apps). But auto-scaled and event-driven generally apply to only short-lived pieces of code. Functions typically run for less than a second.

Mark Russinovich, CTO, Microsoft Azure (GeekWire Photo / Todd Bishop)

We believe in serverless expanding, then including long-running code. Long-running code where the platform is doing auto-scaling, turning things off and turning things on, as it learns and understands the application’s requirements.

If you take a look at Azure Container Instances, that’s an example of us stretching the definition of serverless. You can take a container, a Docker container, and deploy it into Azure. You don’t specify any virtual machines. You pay only for the time that that container is running and it could be a long-running container, not spinning up in response to some event.

GeekWire: What do you consider long-running?

Russinovich: Minutes, hours.

GeekWire: In what cases would you want to do something built around events or functions that runs for hours?

Russinovich: Well if you’ve got background aspects of the application, you don’t want to specifically dedicate virtual machines to it, you want to spin these things up really quickly and have them run where you’re only paying for the resources as you need them. And you’re not worried about infrastructure underneath.

Does Azure support an an open serverless model that could run across different clouds with the understanding that I’m going to manage the hardware?

Russinovich: For Azure functions, that runtime is in Github. Somebody can take it and run it wherever, and then run their own thing.

GeekWire: Do you think serverless is the model for edge? Is it the way that edge would be deployed?

Russinovich: No, not necessarily. The definition of serverless is a little bit [pauses] not well-defined.

Edge computing will allow expensive manufacturing devices to process more data analysis directly on the machine. (Wikimedia Photo / CC 3.0)

When I take a look at the edge platform that we see developing, there’s a spectrum of devices and capabilities — from the tiny little device that is capable of running just a small piece of code, to PC-class devices, to edge deployments like Azure Stack, which is a very rich, highly available, functional system with PaaS (platform-as-a-service) services. If you take a look at the computation that people will be pushing out, it’s not just serverless. There’s going be long-running machine-learning modules, there’s going to be Docker containers executing long-running code that’s part of the application, and there’s going to be parts of the application that are functions.

If you take a look at where functions are fitting into cloud application architecture today, a lot of times they serve as glue between the application and the outside world, or glue between different components in the application. But there’s long-running aspects to the application that functions are supporting.

GeekWire: (Long, rambling and, in hindsight, quite-convoluted question.)

Russinovich: So you’re saying, why (do) serverless on the edge?

GeekWire: Yeah.

Russinovich: If you take a look at on the edge, there’s events that you want to respond to. Having custom pieces of code to respond to those events, they’re effectively very specialized microservices, event-driven microservices. So it’s a nice way to decompose an architecture into, “Here’s the long-running stuff that’s just going to be continuously doing things,” and “Here’s the stuff that occasionally will get executed, and here’s a tiny piece of code responsible for just that event.”

But I think (serverless is) more of a developer architecture, or application architecture, benefit. Especially when you’re out on the edge and you’re not taking advantage of the microbilling and the elasticity that you get in the cloud, because out on the edge, you’ve got a fixed source of hardware resources. There, it’s just the convenience of event driven architectures.

GW: With the fixed set of resources, does serverless give you more bang for your buck that way, than if you ran a more traditional application architecture?

Russinovich: It definitely can. There’s considerations as you design your architecture, given there’s a certain amount of RAM, for example, on the device. I could deploy a bunch of Docker containers and (for) each one specify RAM and they’re going to be basically assigned that RAM regardless if whether they need to use it or not.

With functions, I can have them spin up and spin down and then share the resources: when one’s not active, another one can use it. I still have to be careful as I design my application to make sure that for the throughput and latency requirements of the application on the edge device, that I don’t run into a bottleneck on something where lots of functions want to or need to run at the same time, and now I don’t have enough resources to run them.

An example of a dashboard used to track edge computing. (Microsoft Photo)

I think that we’re at the first steps of this. Right now, if you take a look at the edge computing, we’re going from a world where, “it’s a fixed function. I wrote this piece of code, it is burned into the thing basically, and I never touch it,” to one that’s now, “I’ve decomposed your application, I’m pushing it out and it’s a dynamically updatable piece of hardware.”

(And then in the future) to, “I want to actually take a modern microservices application architecture and run them, and I want to do so reliably and I want to do so across a fleet of devices that might have different resources, levels of resource, but I want to have a high degree of confidence that when I take this application and push it out there, it’s actually going to behave OK.”

We’re starting this journey down that (path). It’s going take a while to get that level of sophistication to show up, but I think that there’s a really, definitely promising future there as edge becomes more and more strategic in the ability to have rich applications out on the edge.

GeekWire: I was going to ask: is anybody actually doing this?

Russinovich: We’ve seen a tremendous amount of interest in this as devices are getting more and more intelligent. A big driver of this actually is machine learning on the edge. AI on the edge. We’re seeing a lot of interest in that. This is why you’re seeing us being able to train a model in the cloud, take that model, and push it out to the edge. Or push it out to a mobile device and run it there locally.

The model is part of a larger application typically. And I want to compose that model as a self contained unit, but then have it interact with other things. For example, in an event-driven architecture, the model could be doing something like, “I’m detecting objects. And there are certain objects when I see them I want to take certain actions on.”

Now that becomes an event: “I see this object, I signal a generated event and then that causes a function that’s responsible. Hey, you see this object? Execute this piece of code.”

GeekWire: What’s a real-world application for this kind of scenario?

Russinovich: There are lots of retail scenarios where you want it. Actions to be triggered based on things that are happening in the warehouse environment and the store environment, at the checkout line; those are definitely places that we’ve seen customers going and taking advantage of this, or wanting to.

GeekWire: Will edge computing mostly be event driven in your opinion, or would there be a reason to run a more traditional approach?

Russinovich: I think that they’ll be both. Like image detection; there’s object detection that I want on the edge. There will be something continuously running that we’ll be looking at, generating and capturing images and processing them. And then there’ll be some events generated off of what is the environment is doing. So there will be both long-running things and event-driven pieces of the architecture.

GeekWire: Do you think there will be (a industry-standards approach to serverless)?

Russinovich: I don’t know. People have talked about (the fact that) at some point it might get standardized. I think we’re so early in this.

You don’t want to standardize before everybody has finished evolving and innovating, because that just stifles it. Nobody knows what is the core that we should be standardizing on yet.

It’s hard to say how that’s going to evolve. Because if you take a look at the way that we’ve evolved serverless, Azure Functions, there are big differences between it and (Amazon Web Services’) Lambda. The bindings that we’ve got are something that you don’t see in other functions’ runtimes.

Now, will everybody decide that they agree with that and that they all want that, and we standardize on something like that? That remains to be seen. And I don’t think (Microsoft is) done with those kind of innovations either yet.

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