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>> Hi, everyone welcome back
to theCUBE's main stage coverage of DockerCon 2022.
We got a great guest from Intel here,
Ajay Mungara Senior Director of Edge Software
and AI at Intel talking about cloud native
and AI workloads at The Edge
and building a better developer ecosystem
for The Edge which we all know
those where the actions going cloud native,
compute data, data as code.
These are things we've been talking about,
so Ajay, welcome to theCUBE.
>> Thank you, John.
I'm really happy to be here in DockerCon
and everything we do Docker makes it better.
>> Well, you guys have done a lot in your career
and looking at your background,
The Edge was manufacturing the old school IOT stuff.
Now that's converged completely
in with cloud native IP technologies.
Everything's kind of happening now at The Edge.
This is where the problems are now shifting
in solving because of the goodness of the cloud
and what that's done for cloud operations
which essentially distributed computing is making The Edge
the battleground for where the innovation's happening.
Could you just share with us your view of why The Edge
is so important and why it's different
than what we've been seeing in pure cloud on
and on premise data centers?
>> Yeah, you know 75% of the data
that is getting generated off late is happening at The Edge.
Okay, so there's a lot of value,
there's a lot of value that's getting generated at The Edge
because most of the compute we want to move it
where closest to the data because of latency issues,
bandwidth issues, security issues all of those things
is getting people to move compute storage
data towards more at The Edge.
There's also one big shift from a developer point
of view where 51% of all of the developers
in the world have deployed in somewhere
the other cloud native Docker
based solutions out there, okay.
What we are seeing is the combination
of cloud computing, networking, edge computing
all of that coming together.
And that is where it is pushing the envelope
from The Edge perspective.
And one of the big drivers is AI at The Edge as well, right.
The Edge inference workloads that is really happening
with camera as one of the sensors
is really driving that compute.
And your question about what's so different about it.
The challenges at The Edge are compounded
because it's bringing together the operational technology,
the information technology processes
and cloud computing environments
along with networking all together.
So when a developer wants to build a solution for The Edge
they have to figure out what part
of that workload sits in the cloud,
how they're going to move that workload towards The Edge
using some form of networking.
How are they going to protect the data in transport
as well as at rest,
because Edge devices can get stolen, you know.
So there is all of these challenges about like
how do you like figure out the trade offs between price,
performance, functionality, power, heat, size, weight
everything matters when you talk about The Edge.
So anyway, that is why we see those differences.
>> It's interesting you know you do a little go back
in history and distribute computing,
the movies still the same.
Remember back in the day when I was breaking
into the business memory was the bottleneck
and storage was the resource.
And you had to swap out memory,
and as a developer you had to deal with that.
Then memory became abundant and storage was the problem.
Now you got networking is the latency problem.
So again, these are a challenges
that developers have to weave through,
I was going to ask the question of why is The Edge important
for the and what's in it for the developer,
why should they care about The Edge?
And I think what you were saying is
there's design decisions going on around how to code,
can you elaborate on what's in it for the developer?
Why should they care about The Edge?
>> Developers have to really care about The Edge is
because when you are really building a solution
you cannot move the data
and make all the decisions at the cloud
because it's late, right, sometimes latency,
your bandwidth costs, your solution costs
are going to get increased.
And because of security and privacy concerns
sometimes you have to make those decisions
at The Edge itself.
You will have to figure out only take the data strategically
to the cloud where it makes sense, okay.
And that is the reason why developers have no choice
but they have to focus on the combination
of cloud networking and edge,
and that's where we are seeing a large scale set
of deployments that are happening today.
>> Yeah, and I can see the business value too
which is one of the big themes that DockerCon this year
is tracks on that people talking about that.
Are you seeing trends like headless retail,
which is basically, it's not Shopify managed service,
it's more of you build your own stack
and you put the head on there which is the application
and business model.
>> Right.
>> So again, that's an example.
There's also the manufacturing, there's automotive
all kinds of use cases
where there's money making opportunities, right.
So there's business value there,
so the developer's going to be pulled to The Edge
'cause they're in the front lines now.
So this is about making The Edge ready,
and I want to hear your thoughts on what Intel's doing
to make that developer environment ready for The Edge
because we know the developer on the front lines today
and that front line vanguard will be The Edge.
What's it look like?
>> Exactly, right, so what we have done
is we have created this environment for developers
which we call it as IntelDevCloud.
And in this dev cloud which is Kubernetes based environment
where we support all of the Docker workloads
and it's based off of Red Hat OpenShift.
And we thought about this a little differently.
What we did is it's a cloud environment
where you could use a browser to do all
of your development build test and all of that.
But we also took a whole range of these edge devices
and we made it available in the cloud.
So as a developer,
you don't have to have an edge device sitting at your desk.
You have an edge device
or a plethora of edge devices sitting in the cloud.
So you have one environment where you have cloud,
you have network, and you have all these edge nodes.
So you could start building your solution,
you could start building your cloud native
or edge native solutions, test it, benchmark it,
and figure out how and what type of combination
that you actually need for your final solution
as you said in retail, in smart cities, in healthcare,
any of these vertical markets
and get your solution closer to being a deployment ready.
>> Yeah, and I love your description by the way
it's called a container playground.
I mean, it's just comes across as fun.
And I think this idea of having these nodes available
you guys bring a lot of expertise at the table.
That's almost like your local host for Edge devices, right?
You can work with it in a safe environment,
am I getting that right.
>> You're getting that right,
and in fact, during the pandemic
when we are all working remote, right,
nobody has access to these labs
where you have all these Edge devices available to you,
you could actually play
with all these network simulators everything.
Now with dev all these developers spread all over the world,
you don't have access to as many of those edge devices.
So now with browser, with this container playground,
you could develop any of your Docker composed,
Docker based container workloads
and try it on all of these edge devices which may range
from an Intel's point of view,
CPUs, VPUs, GPUS, anything, right.
>> We know there's a lot of compute at The Edge
which always ever helps in Intel
but your north star is about making it easier
for the developers as you guys invest cloud network
and The Edge and the cloud native world, that's the goal.
How do you do that?
And what should the developers optimize for
it sounds like they're going to learn with this playground
that you have the dev cloud.
What are you seeing
that they're going to learn to optimize for?
Is it like I use the oldest school example
of memory optimization, swapping memory out
and that kind of thing
but what's the new issues that need to be optimized
for your developer.
>> If you're a developer you got to optimize
for your edge AI workloads, right,
so that means AI inference workloads.
You have to look at like saying
that how can I take like a model that is developed
in a some type of a cloud environment,
like a TensorFlow model or a Pieto model,
bring it down to The Edge.
And then you have to do inference workloads.
You need to understand to do this inference,
what type of compute you need,
what type of storage do you need?
What type of memory do you need?
And we give you those options
where you could optimize those type of inference AI,
inference workloads, you could actually do that.
Then you also can decide like what type
of decisions you want to make at The Edge
what decisions you want to make at the cloud.
We give you those options
and flexibility for you to build those solutions.
>> Great.
>> One last point I'll make is there's a lot
of legacy applications that have been developed
which is traditional embedded applications.
We are also want to teach developers
how to take these applications and containerize them.
How to take advantage of the cloud native DevOps
type of paradigms that it would make your life easier
when it comes to scaling your solution,
deploying your solution worldwide.
>> All right, Ajay, thanks so much for coming
on theCUBE DevCloud, a container playground.
Now back to you at the main stage at DockerCon.
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