>> Narrator: Live from Las Vegas, it's theCUBE,
covering AWS re:Invent 2019.
Brought to you by Amazon Web Services
and Intel,
along with its ecosystem partners.
>> Welcome back inside The Sands here
as we continue our coverage here live on theCUBE
of AWS re:Invent 2019.
Absolutely jam-packed aisles, great educational sessions,
and one of the featured presenters now joins us as well.
>> Dave: Dave Vellante, John Walls, with Paul Savill,
who's the SVP of Core Network and Technology Solutions
at CenturyLink.
Paul, good to see you again.
>> Yeah, nice to see you, John.
>> You just finished up, right, we'll get into that
in just a little bit.
First off, just give me your impression
of what's going on here and the energy and the vibe
that you're getting.
>> Yeah, I think it's fantastic.
I mean, it's very high energy here.
You know, there's a lot of new things that are emerging
in terms of the applications that we're seeing,
the use cases for the cloud, and of course,
exciting stuff happening around edge compute
with the announcement of AWS, with the Outposts launch.
>> Dave: All right, so, we'll jump into edge, I mean,
everybody has a different idea, right, so, yours, I mean,
if you define the edge, at least, how do you see it?
>> Yeah, it's a very simple definition
of how we see the edge.
It's putting compute very close to the point of interaction,
and the interaction can be with humans
or the interaction can be with devices or other electronics
that need to be controlled or that need to communicate.
But the point is, getting that compute as close as possible
to it, from a performance standpoint, that's needed.
>> So, we heard that a lot from Andy Jassy,
I think, yesterday, right, bringing the compute to the data.
I mean, with all due respect, he was talking about it like
it was a new concept, right, we've been hearing this now
for quite some time.
Um, so,
talk more about how you see the edge evolving.
I mean, look, I give a lot of credit to Amazon
because, you know, they used to not talk about hybrid.
I predict, years, soon, that we talk about multicloud.
Guarantee it.
Right, because that's what customers are doing.
So, they respond to customers.
So, at the same time, I like their edge strategy
because it's all about developers,
it's about infrastructures, code, at the edge,
but you guys are about, you know, moving that data,
or not, necessarily, bringing the compute to that edge,
so, how do you see the edge evolving?
>> Paul: Yeah, so the reason this whole trend is happening
is because, what's happening with the new technologies
that are enabling a whole new set of applications out there.
Things like what's going on with artificial intelligence
and machine learning and virtual reality.
Those, robotics control, those things are basically driving
this need to place compute as close as possible
to that point of interaction.
The problem is that, when you do that, costs go up,
and that's the conundrum that we've kind of been in
because when compute gets housed at the customer premise,
you know, in a home, in a business, in an enterprise,
then that's the most expensive real estate that there is,
and you can't get the economies of scale that's there.
The only other choice to date has been the public cloud,
and that can be hundreds or thousands of miles away,
and these new applications that require really tight control
and interaction can't operate in that kind of environment,
and yet, it's too expensive to run those applications
at the very edge, at the premise itself.
So that's why this middle ground, now,
of placing compute nearby, where it can serve many locations
or be housed more cost effectively is coming into play.
>> Okay, so you got the speed of light problem.
>> Paul: Right.
>> So you deal with that latency
by making the compute proximate to the data.
>> Paul: Exactly.
>> But it doesn't have to be, like, right next to it.
>> Paul: Correct.
>> But, what are we talking distance-wise?
It has to be synchronous distance, or?
>> Paul: Yeah, when we think of the distance, we think
about it in terms of milliseconds of delay
from where the edge device, the thing that needs to interact
with the compute or the application needs to interact with,
and we have not seen any applications that,
from the customers talk to, that really get beyond
or need tighter than five milliseconds of delay.
Now, that's one way.
So, if we get into that range of placing compute
within five milliseconds of the edge interaction,
the device that it needs to interact with,
that is enough to meet some of the
most tightest requirements that we've seen
around robotics control, video analytics,
and other applications.
>> John: Okay, so I could ship code to the data,
but the problem is, if it needs to be real-time,
it's still too much, it's too much latency.
>> Right.
>> And that's the problem that you're solving.
>> That's right.
>> Okay. >> Correct.
>> So, with what you're talking about,
why milliseconds matter.
>> That's right.
>> So, give me some examples, if you will, then,
about why five matters more than ten
or five matters more than eight or twenty of whatever.
I mean, because we're talking
about such an infinitesimal difference, but yet,
it does matter in some respects.
>> Paul: It does, because, so, I'll give you an example
of robotics, for example, robotics control.
You know, that is one of the things
that requires the most tight latency requirement,
and it depends upon the robotics itself.
If it's a machining tool that's working on a lathe then
that doesn't require as tight of response time
to the controller as, say, a scanning device
that is real-time pushing things around very fast
and doing an optical read on it to make the decision
about how, about where it pushes the device next.
That type of interaction and control requires
a much tighter latency performance, and that's why you get,
you start to see these ranges.
But as I said, we're not seeing anything below that kind of
five millisecond type of range from people.
>> John: So, the other thing that's changing,
and help me understand this,
is, yeah, okay, you're moving the compute closer
to the data, which increases cost, and I want to understand
how you're addressing that.
Well, maybe one of the ways you're addressing it is
'cause you're bringing the cloud model, the operating model,
to the data.
>> Paul: That's right.
>> So, patches, security patches, maintenance,
things like that are reduced.
Is that how you're addressing cost?
>> Paul: Yeah, that is part of it,
and that's why the AWS Outpost is very interesting
because it is really a complete instance of AWS
that is in a much smaller form-factor
that you can deploy very close to that point of interaction,
close to the customer premise,
and that enables customers to leverage the, pretty much
the full power of AWS in engaging with those devices
and coding to those devices
and dropping those applications close to those devices.
>> Now, you lose the multi-tenant aspect, is that right?
Or not necessarily--
>> Paul: Now, from our understanding of Outposts,
it's a single-tenant device coming out the gate,
but ultimately it's going to be a multi-tenant device.
>> John: Yeah, okay, so, near-term, it's easier to manage
but it's multi-instance, I guess.
>> Paul: Yes.
>> And then, over time, maybe you can share that resource.
It's still not going to--
>> Paul: The interesting thing is that,
even though it's a single-tenant device,
there are still many great use cases
because even a single-tenant device in one market
could serve multiple enterprise locations,
so it still has that, kind of, a sense of scale
because you can serve, as long as it's one enterprise,
you can serve many locations off of that one device.
>> John: Okay, so you don't get the
massive economies of scale, but you're opening up use cases
that never existed before.
>> Paul: That's right.
>> Dave: What do you do with, when the data supply
basically is, you know, all of a sudden we've got data scale
and edge devices creating that much more data.
All of the sudden, speed becomes a little more challenging
because you're taking in a lot more information,
trying to process it in different ways,
apps are feeding off of that,
so all the sudden, you have a much more complex challenge,
'cause it's not static, right.
This is a very dynamic environment.
>> Paul: That's right, yeah, and there's a very big trend
that's happening now, which is that, data is being created
at the edge and it's staying at the edge
for a whole number of reasons.
You know, in the old world,
you would pretty much collect data
and you would ship it off to the centralized data center
or to the public cloud to be housed there,
and that's, today, that's where 80% of data resides,
but there's a big shift happening where that data now needs
to reside at the deep edge because it needs
to have that fast interaction with something
that it's working with or because of government regulations
that are now coming in that are having
much stricter tolerances around, you have to know exactly
where your data is, and it can't cross state lines,
it can't, you know, get out of a certain security zone,
things like that are forcing companies, now, to keep
that massive amount of data, uh, in a very understood,
known, localized position.
>> So, you got to act on it in real time.
>> Yes.
>> Some of it'll go back to the cloud,
but are you seeing folks persist the data at the edge,
or not so much?
>> Persistent data?
>> Do people want to store it at the edge as well?
>> Yes, uh-huh,
yeah, people want to store it at the edge where it's going
to have a lot of interaction.
So, if you're running a, uh, if you're running
a chemical plant, you may not need to have access
to a lot of data outside that chemical plant,
but you're intensively analyzing that data
in the chemical plant, and you don't want to ship it
off some place centrally a thousand miles away
to be accessed from there.
It needs to be acted on locally,
and that's why edge compute,
this movement toward edge compute,
is really building and becoming stronger.
>> Talk about your tech, you know.
What's the real value of what you do?
You're obviously reducing latency,
you got to secure all this stuff,
but maybe double-click on that.
>> Paul: Yeah, so CenturyLink brings a number of tools
to help in this whole space.
So, first of all, the network that we provide
that can tie it all together, from the enterprise location,
to the edge location where compute can be housed,
all the way back to the public cloud core,
we have a network that spans the entire U.S.,
fiber all over the place,
and we can use those low-latency fiber optic connections
to chain those areas together in the most optimal fashion
to get the kind of performance that you need
to handle these distributed compute environments.
We also bring compute technology itself.
We have our own variety of edge compute
where we can build custom edge compute solutions
for customers that meet their very specific
spec requirements that can be dedicated to them.
We can incorporate AWS's compute technology as well,
and we have, um, we have IT services and skilled people,
thousands of employees that are focused on this space
that build these solutions together for customers
that tie together the public cloud resources,
the edge compute resources, the network resources,
the wireless connectivity capabilities that's needed
on customer premise, and the management solutions
to tie it all together in that very mixed environment.
>> John: We were just on a session with Teresa Carlson,
who runs pubic sector for AWS.
I was telling her that I sat in a session,
Marty Walsh, the mayor of Boston's got
this big smart city initiative going on.
I know that's one of the use cases you're working on.
Maybe talk about that a little bit,
and maybe some of the other interesting use cases
that you're seeing.
>> Yeah, that's right.
Definitely smart cities are a big use case, though, the one,
and we are actually actively working on a number of them.
I would say the smart city use cases tend
to move very slowly because you're talking
about municipalities and a long decision-making cycle.
I'll tell you, though, we've seen--
>> There's a 50 year plan we put forth.
>> John: So, yeah. >> Paul: True, yeah.
>> Paul: So, uh, but the use cases
that we're really seeing the most traction with
are interestingly, is, uh, robotics, is a really big one,
and video analytics is another big one.
So, we're actually deploying edge use case solutions
right now in those scenarios.
The robotics one is a great one
because those devices need to be, those robotic devices,
need to be controlled
within a really tight millisecond tolerance
and, but the compute needs to be housed in a very,
it's much more reliable, economic location.
The video analytics piece is a really interesting one
that we're seeing very, very, uh, big demand for
because retailers have now reached the point
where the technology, where they can do things like,
they can figure out, by doing video analytics,
whether somebody is acting suspiciously in the store,
and we're hearing that they can, they think they can
now cut thievery out of retail locations dramatically
by using video analytics.
And when you talk about big savings to the bottom line
of a company, that makes a big savings to them.
So those are very two good use cases we're seeing
that are real today.
>> You know, one of the other things you were talking
about earlier was about the disappearance of compute divide.
>> Paul: That's right.
>> So, where to go?
I mean, what's the (laughs)?
>> Paul: Yeah, well, I tell you what, I like to say that,
in the old days, you know,
if you've been around long enough,
like I know you're old enough, because--
>> Wait a minute. (laughs)
>> I was a kid watching you on T.V. when I was a little kid.
>> [Paul} He knows I'm from Tulsa.
(laughs)
>> Dave: Way back.
Guilty.
>> Paul: When we got out of college--
>> How's that make you feel?
>> Oh, really old.
(laughs)
>> When we got out of college, John,
everything was in a mainframe, right.
It was essentially, you had, when you went to work,
you had a terminal, and everything was housed centrally.
Then we went to distributed where, client-server model,
where everybody was working on desktops,
and a lot of the compute was on the desktops,
and very little went back to a mainframe.
Then we made the shift to the cloud,
where we pushed as much in the centralized location
as we can, to, and so we shifted way back to centralized.
That's the compute divide I'm talking about.
That big shift decentralized to centralized,
decentralized.
Now, we're actually moving to a new world where
that pendulum swing, that compute divide, is disappearing
because compute isn't most economically stored
in any one location, it's everywhere.
It's going to be at the IoT edge,
it's going to be at the premise,
it's going to be in market locations that were centralized,
it's going to be in the public cloud core,
it's going to be all around us,
and that's what I mean by the disappearance
of the compute divide.
>> And, you know, I want to come back on that.
You talk about a pendulum.
A lot of people talk about the pendulum swings,
mainframe and distributed.
Now, a lot of people say the pendulum's been swinging back,
but you just described it differently.
It's a ubiquitous matrix now where compute is everywhere.
>> That's where you hear this term "fog computing".
The idea of the fog, now.
It's not the cloud that you can see off in the distance.
It's just everywhere, it surrounds you,
and that's how compute, we can start to think
about how compute-- >> John: I think I first heard
that term like, I don't know, eight years ago.
I'm like, "What the heck is this?"
It was ahead of its time,
but now it's really starting to show.
I mean, this is sort of new expansion of what we know
as cloud, sort of redefining.
>> Paul: Yes, exactly.
>> And edge 5G, that's, you know, another big piece of it.
You know, Amazon's obviously excited about that
with, I think they call it "Wavelength", right?
What do you see for 5G?
How's that going to affect this whole equation?
>> Yeah, I think 5G is going to have a number
of edge applications,
and it's primarily going to be around the mobile space.
You know it's, the advantage of it is
that it increases bandwidth, and it supports mobility, uh,
and it allows for a little bit higher resilience
because they can take the part of the spectrum
and make sure that they're carving it out
and dedicating it for particular applications
that are there.
But I'd tell you that the, um, you know 5G gets a lot
of attention in terms of being,
how edge compute is going to rollout,
but we're not seeing that at all.
Edge compute is available today,
and that, we're providing those edge compute solutions
through out fiber optic networks.
What we're seeing is that, every enterprise
that we're talking to,
wants fiber into their enterprise location
because once you have fiber there,
that's going to be the most secure, reliable,
and scalable solution.
Fiber can effectively scale as big as any customer
could ever consume the bandwidth,
and they know that once they get fiber into their location
that they're good for the future
because they can totally scale with that.
And that's how we're deploying edge solutions today.
>> Dave: Well, Paul, I know you got a plane to catch
and you got to go, but after that age comment,
we're going to keep you for another hour.
>> Paul: Oh, okay, no. (laughs)
>> Dave: Hey, thanks again.
>> Hey, it was great seeing you again.
>> Dave: Always good to see you.
Thanks for joining us.
>> All right, Paul, hang on, hang on.
We're about to say goodbye to Paul.
Now we will.
AWS re:Invent 2019 coverage continues right here
on theCUBE.
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