>> Narrator: Live from Boston, Massachusets.
It's theCUBE, covering OpenStack Summit 2017.
Brought to you by the OpenStack Foundation,
Red Hat and additional ecosystem and support.
>> Hi, welcome back, I'm Stu Miniman, joined by my cohost
John Troyer and happy to welcome back to the program
Brian Stevens who's the CTO of Google Cloud.
Brian, thanks for joining us.
>> I'm glad to, it's been a few years.
>> All right, I wanted to bounce something off you.
We always talk about, you know, it's like open source.
You worked for in the past what is most considered
the most successful open source company
for monetizing open source, which is Red Hat.
We have posited at Wikibon that
it's not necessarily the company,
it's not only the companies that sell a product
or a solution that make money off it, but I said,
if it wasn't for things like Linux in general
and open source, we wouldn't have a company like Google.
Do you agree with that, you look at the market cap
of a Google, I said if we didn't have Linux
and we didn't have open source,
Google probably couldn't exist today.
>> Yeah, I don't think any of the hyper scale cloud companies
would exist without open source and Linux and Intel.
I think it's a big part of the stack, absolutely.
>> All right.
You made a comment at the beginning about what it means
to be an open source person working at Google.
The joke we all used to make was the rest of us are using
what Google did 10 years ago, it eventually goes
from that whitepaper all the way down to some product
that you used internally and then maybe gets spun off.
We wouldn't have Hadoop if it wasn't for Google.
Just some of the amazing things that have come out of
those people at Google.
But what does it mean to be open source
at Google and with Google?
>> You get both, right?
'Cause I think that's the fun part
is I don't think a week goes by where I don't get
to discover something coming out of
a resource group somewhere.
Now the latest is machine learning,
you know, Spanner because they'd learned
how do to distributed time synchronization
across geo data centers, like who does that, right?
But Google has both the people
and the desire and the ability to invest in
on the research side.
And then you marry that innovation
with everything that's happening in open source.
It's a really perfect combination.
And so instead of building these proprietary systems,
it's all about how do we actually
not just contribute to open source,
but how do we actually build that
interoperability framework, because you don't want cloud
to be an island, you want it to be really integrated
into developer tools, databases, infrastructure, et cetera.
>> And a lot of that sounds like it plays into
the Kubernetes story, 'cause, you know,
Kubernetes is a piece that allows some similarities
between wherever you place your data.
Maybe give us a little bit more about what Google,
you know, how do you decide what's internal,
I think about like the Spanner program,
which there's some other open source pieces coming up,
looks like they read the whitepaper
and they're trying to do some pieces.
You said less whitepapers, more code coming out of people,
what does that means?
>> It's not that we'll do less whitepapers.
'Cause whitepapers are great for research,
and Google's definitely a research strong
academic oriented company.
It's just that you need to go further as well.
So that was, you know, what I was talking about
like with GRPC, creating an Apache project
I think was the first time for streaming analytics, right,
was the first time that I think Google's done that.
Obviously, been involved for years at the Linux kernel,
compilers, et cetera.
I think it's more around
what do developers need, where can we actually contribute
to areas, because what you don't want,
what we don't want is you're on premise
and you're using one type of system,
then you move to Google Cloud and it feels
like there's impedance.
You're really trying to get rid of the impedance mismatch
all the way across the stack, and one of the best ways
you can do that is by contributing new system designs.
There's a little bit less of that happening
in the analytics space now though, I think the new ground
for that is everything that's happening in machine learning
with Tensor Flow et cetera.
>> Yeah, absolutely.
There was some mention in the keynote this morning,
all of the AI and ML, I mean, Google with Tensor Flow,
even Amazon themselves getting involved
more with open source.
You said you couldn't build the hyper scales without them,
but is that the, do they start with open source,
do you see, or?
>> Well, I think that most people are running
on a Linux backplane.
It's a little bit different in Google 'cause we got
an underlying provisioning system called the Borg.
And that just works, so some things work, don't change them.
Here is where you really want to
be open source first are areas that
are just under active evolution,
because then you actually can join
that movement of active evolution.
Developer tools are kind of like that.
Even machine learning.
Machine learning's super strategic
to just about every company out there.
But what Google did by actually open sourcing Tensor Flow
is now they created a canvas, that community,
we talk about that here, but for data scientists
to collaborate, and these are people that didn't do much
in open source prior, but you've given that ability
to sort of come up with the best ideas
and to innovate in code.
>> I wanted to ask a little bit about the enterprise, right.
We can all make jokes about enterprising is
what everybody should've been doing
10 years ago, and they're finally getting to.
But on the other hand, Red Hat,
very enterprise focused company.
OpenStack, service provider and very enterprise focused.
One of the things that Google Cloud is doing...
Well, I guess the criticism has typically been
how does Google as a company and as a culture
and as a cloud focused on the enterprise,
especially bringing advanced topics like machine learning
and things like that, which to a traditional IT person
are a little foreign.
So I just am interested in kind of how you're viewing,
how do we approach the needs of the enterprise,
meet them where they are today,
while yet giving them an access to a whole set
of services and tools that are actually going to
take them into a business transformation stance?
>> Sure.
And that's because you end up as a public cloud provider
with the enterprise, you end up
having multiple conversations.
You certainly have one of your primary audiences,
the IT team, right.
And so you have to earn trust and help them understand
the tools and your strategy and
your commitment to enterprise.
And then you have CSOs, right, and the CEO,
that's worried about everything security
and risk and compliance, so it's a little bit different
than your IT department.
And then what's happening with machine learning
and some of the higher end services is now you're actually
building solutions for lines of business.
So you're not talking to the IT teams
with machine learning and you're not talking to the CSOs,
you're really talking around business transformation.
And when you're actually, if you're going into healthcare,
if you're going into financial, it's a whole different team
when you're talking about machine learning.
So what happens is Google's really got a segmented
three sort of discreet conversations
that happen at separate points of time,
but all of which are enterprise focused,
'cause they all have to marry together.
Even though there may be interest in machine learning,
if you don't wrap that in an enterprise security model
and a way that IT can sustain and enable
and deal with identity and all the other aspects,
then you'll come up short.
>> Yeah.
Building on that.
One of the critiques of OpenStack for years
has been it's tough.
I think about one of the critiques of Google is like,
oh well, Google build stuff for Google engineers,
we're not Google engineers, you know,
Google's got the smartest people and therefore
we're not worthy to be able to handle some of that.
What's your response to that?
How do you put some of those together?
>> Of course, Google's really smart,
but there's smart people everywhere.
And I don't think that's it.
I think the issue is, you know,
Google had to build it for themselves, right,
they'd build it for search and build it for apps
and build it for YouTube.
And OpenStack's got a harder problem
in a way, when you think about it,
'cause they're building it for everybody.
And that was the Red Hat model as well,
it's not just about building it for Goldman Sachs,
it's building it for every vertical.
And so it's supposed to be hard.
This isn't just about building a technology stack
and saying we're done, we're going to move on.
This community has to make sure that it works
across the industry.
And that doesn't happen in six years,
it takes a longer period of time to do that,
and it just means keeping your focus on it.
And then you deal with all the use cases over time
and then you build, that's what getting
to a unified commoditized platform delivers.
>> I love that, absolutely.
We tend to oversimplify things and, right,
building from the ground up some infrastructure stack
that can live in any data center is a big challenge.
I wrote an article years ago about Amazon hyperoptimizes.
They only have to build for one data center, it's theirs.
At Google, you understand what set of applications
you're going to be running, you build your applications
and the infrastructure supports it underneath that.
What are some of the big challenges you're working on,
some of the meaty things that are exciting you
in the technology space today?
>> In a way, it's similar.
In a way, it's similar, it's just that at least our stack's
our stack, but what happens is then
we have to marry that into the operational environments,
not just for a niche of customers,
but for every enterprise segment that's out there.
What you end up realizing is that it ends up becoming
more of a competency challenge than a technology issue
because cloud is still, you know, public cloud
is still really new.
It's consolidating but it's still relatively new
when you start to think about these journeys
that happen in the IT world.
So a lot of it for us is really that technical enablement
of customers that want to get to Google Cloud,
but how do you actually help them?
And so it's really a people and processes
kind of conversation over how fast is your virtual machine.
>> One of the things I think is interesting
about that Google Cloud that has developed
is the role of the SRE.
And Google has been,
has invented that, wrote the book on it, literally,
is training others, has partnerships to help train others
with their SREs and the CRE program.
So much of the people formerly known as sysadmins,
in this new cloud world, some of them are architects,
but some of them will end up being operators and SREs.
How do you see the balance in this upscaling of
kind of the architecture and the traditional infrastructure
and capacities and app dev versus operations,
how important is operations in our new world?
>> It's everything.
And that's why I think people, you know...
What's funny is that if you do this
code handoff where the software developers build code
and then they hand it to a team to run and deploy.
Developers never become great at building systems
that can be operationally managed and maintained.
And so I think that was sort of the aha moment,
as the best I understand the SRE model at Google
is that until you can actually deliver code
that can be maintained or alive, well then
the software developer owns that problem.
The SRE organization only comes in at that point in time
where they hand up their, and they're software developers.
They're every bit as skilled software developers
as the engineers are that are building the code,
it's just that's the problem they want to decode,
which I think is actually a harder problem
than writing the code.
'Cause when you think about it for a public cloud,
its like, how do you actually make change, right,
but keep the plane flying?
And to make sure that it works with everything
in an ecosystem.
At a period of time where you never really
had a validation stage, because in the land of
delivering ISV software, you always have
the six month, nine month evaluation phase
to bring in a new operating system or something else,
or all the ecosystem tests around that.
Cloud's harder, the magic of cloud is
you don't have that window, but you still have to guarantee
the same results.
One of the things that we did around that was
we took the page out of the SRE playbook,
which is how does Google do it, and what we realized
is that, even though public cloud's moved the layers up,
enterprises still have the same issue.
Because they're deploying critical applications
and workloads on top.
How do they do that and how do they keep
those workloads running and what are their mechanisms
for managing availability, service level objectives,
share a few dashboards, and that's why we created
the CRE team, which is customer reliability engineering,
which is a playbook of SRE, but they work
directly with end users.
And that's part of the how do we help them
get to Google Cloud, part of it's like really understanding
their application stacks and helping them build
those operational procedures, so they become SREs
if you will.
>> Brian, one of the things I, if you look at OpenStack,
it's really, it's the infrastructure layer that it handles,
when I think about Google Cloud,
the area that you're strongest and, you know,
you're welcome to correct me, but it's really
when we talk about data, how you use data, how analytics,
your leadership you're taking in the machine learning space.
Is it okay for OpenStack to just handle those lower levels
and let other projects sit on top of it?
And curious as to the developing or where Google Cloud sits.
>> I think that was a lower level aha moment for me,
even prior to Google, was it was,
I did have a lens and it was all about infrastructure.
And I think the infrastructure is every bit as important
as it ever was.
But the fact that some of these services
that don't exist in the on-premise world
that live in Google Cloud are the ones
that are transformative change, as opposed to just
giving you operational, easing the operational burden,
easing the security burden.
But it's some of these add-on services
that are the ones that really changed here,
bring around business transformation.
The reason we have been moving away from Hadoop
as an example, not entirely but just because Hadoop's
a batch oriented application.
>> Could go to Spark, Flink, everything beyond that.
>> Sure, and also now when you get to real time
and streaming image, you can have adjusted data pipelines,
data come from multiple sources.
But then you can action on that data instantly,
and a lot of businesses require, or ours certainly does
and I think a lot of our customers' businesses do,
the time to action really matters,
and those are the types of services that, at least at scale,
don't really exist anywhere else and machine learning,
the ability of our custom ASICs to support machine learning.
But I don't think it's a one versus the other,
I think that brings about how do you allow enterprises
to have both.
And not have to choose between public cloud and on premise,
or doing (mumbles) services or (mumbles) services,
because if you ask them, the best thing they can have
is actually how do you marry the two environments together
so they don't look, again, back to
that impedance differences.
>> Yeah, and I think that's a great point,
we've talked OpenStack is fitting into that hybrid
or multi-cloud world a bunch.
The challenge I guess we look at is some of those
really cool features that are game changers
that I have in public cloud that I can't do
in my own data center, how do we bridge that?
Started to see the reach or the APIs that do that,
but how do you see that playing out?
>> Because you don't have to bring them in.
Because if you think about the fabric of IT,
the fabric of IT is that Google's data center
in that way just becomes an extension of the data center
that a large enterprise is already using anyway.
So it's through us.
So they aren't going to the lines of distinction,
only we and sort of the IT side see that.
There isn't going to be seen,
as long as they have an existing platform
and they can take advantage of those services,
and it doesn't mean that their workload
has to be portable and the services have to exist
in both places, it's just a data extension
with some pretty compelling services.
>> I think back, you know, Hadoop was
let me bring the compute to the data
'cause the data's big and can't be moved.
Look at edge computing now, I'm not going to be able
to move all that data from the edge,
I don't have the networking connectivity.
There's certain pieces which we'll come back to,
you know, a core public cloud, but
I wonder if you can comment on some of those
edge pieces, how you see that fitting in?
We've talked a little bit about it here at OpenStack,
but 'cause you're Google.
I think it's the evolution.
When we look at, we just even see the edge of our network,
the edge of our network is in, it's 173 countries
and regions globally.
And so that edge of the network is
full compute and cashing.
And so even for us, we're looking at
what sort of compute services do you bring
to the edge of the network.
We're like, low latency really matters
and proximity matters.
The easiest obvious examples are gaming,
but there's other ones as well, trading.
But still though, if you want to take advantage
of that foundation, it shouldn't be one
that you have to dive into the specificities
of a single provider, you'd really want
that abstraction layer across the edge,
whether that's Docker and a defined set of APIs
around data management and delivery and security,
that probably gives you that edge computing sell,
and then you really want to build around that on
Google's edge, you want to build
around that on a telco's edge.
So I don't think it really becomes necessarily around
whether it's centralized or it's the edge,
it's really what's that architecture to deliver.
>> All right.
Brian, I want to give you the opportunity, final world,
things either from OpenStack, retrospectively
or Google looking forward that you'd like
to leave our audience with.
>> Wow, closing remarks.
You know, I think the continuity here is open source.
And I know the backdrop of this is OpenStack,
but it's really around open source is
the accepted foundation and substrate
for IT computing up the stack, so I think
that's not changing, the faces may change
and what we call these projects may change,
but that's the evolution and I think
there's really no turning back on that now.
>> Brian Stevens, always a pleasure
to catch up with you, we'll be back
with lots more coverage here with theCUBE,
thanks for watching.
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