NIRAV MEHTA: Thank you for joining me.
My name is Nirav Mehta.
I am Director of Product Management for Google Cloud
Compute Engine.
I'd like only 20 minutes of your time today.
The first half, I want to share with you
some of our recent innovations, but also the core principles
that drive them.
And for the second half I'll be talking with Omer Hasan, who
will join me from AppLovin.
He's the VP of Operations.
I'm very excited to have him here for a short conversation.
So let's get right to it and start
with some of the investment themes and principles that
drive us.
First, Google Cloud Compute Engine
is all about offering you thoughtful and intuitive
choices.
What this means is it's easy for you
to select the right virtual machine type, the right storage
type simply by looking at how we've organized our choices
by workloads or by use case.
And I'll talk about that in a couple of minutes.
Second, package solutions that allow
you to very easily, in an opinionated way, deploy,
for example, SAP HANA, or VMware, Microsoft SQL Server.
We've thought through reference architectures
and common deployment templates to help
you get to outcomes quickly.
Third, and perhaps the most central tenet
that we have behind the platform engineering,
is simplicity at scale.
Many customers I talk with tell me
that Google Cloud is intuitive.
And as they use it more, they find it simpler,
and that we make complex things seem simple.
This is very important to us.
We take it to heart and want to ensure we maintain this
as we grow, add more features.
So it's a commitment to maintain this advantage,
and I'll talk about how we do it.
Security is paramount.
Not only making sure that we offer all the right security
controls and compliance controls,
but also that we put privacy in the hands of the customer,
or in the control of you who owns the data.
A simple example of this is our recent feature
for encrypting in-memory contents
within a virtual machine.
The encryption keys are controlled by the customer,
and turning the feature on is a matter
of simply checking one box.
So simple security that's comprehensive with the customer
remaining in control.
And lastly, cost optimization in all its forms,
not just offering the right price points, the right price
performance points, but also helping you reduce waste,
making machine learning based recommendations that tell you
what types you should be using, how you
should be adjusting your spend.
All of that comes together to make this a highly cost
optimized platform.
So let's talk about each of these,
starting with VM families.
With VM families, at the very highest level,
there are two categories-- workload-optimized
and general purpose.
For workload-optimized there are three categories-- compute
optimized for high performance computing,
and these are heavier on compute power, or memory optimized,
or the M series of VMs.
This is for the high-end databases like SAP HANA,
with very high configuration of RAM.
And lastly, there is accelerator optimized,
which is GPU-enabled VMs for machine
learning training inference.
I'll talk about a couple of these soon.
And looking at general purpose, we
have, again, three different categories,
ranging from price-optimized or cost-optimized,
to price performance-optimized for scale out.
Our cost optimized family is called the E-series,
or the Efficient series.
If price per VM or price per core
is the most important consideration, that's the one
you would pick.
If you want a balance and a feature
set that works for the long tail of general purpose
applications, you would go for the M series.
And the newest addition to the VM family
is the T-Series, or the Tau VMs.
These are optimized with a lean feature set,
eliminating waste, and offering only the right features
for scale out digital native applications at a very
attractive price point.
Specifically, we tested our Tau VMs against competitive options
from leading clouds.
And as you see here, we've achieved a price point
that's 42% better in price performance
than anything else from a leading cloud.
This includes anything with an Intel, AMD, or ARM processor.
We are committed to all these CPU types.
But what's interesting is with Tau VMs
we've achieved the leading price performance point with AMD VMs.
That means your developers don't have
to go port from an x86 application
to ARM or another type, and still
achieve this price performance.
Tau VMs are in preview and we are open for registration now.
An example of recent innovation for accelerator-optimized VMs
is our A2 VMs.
These are the first VMs to introduce the NVIDIA A100 GPU.
And I'm very proud that we have delivered the largest
single instance VM with up to 16 NVIDIA A100 GPUs among leading
clouds, so that you can bring your machine learning training
workloads, achieve the scale and performance you need,
and grow with Google Cloud.
Another thing that's new with virtual machines
is our spot VMs.
Spot VMs are a new and enhanced version
of our pre-emptible VMs, which allow
you to use our infrastructure when it is not
being used by us so that you can get very attractive price
points.
What's interesting here is we guarantee 60% minimum savings,
which we believe is one of the most compelling price
points in the market.
Second, and more importantly, this pricing is predictable.
Quite often for this type of VM you will see in the market
the prices vary every five minutes.
In our case, these virtual machines
are much more predictable in pricing.
The variation is not more frequent than once a month.
Third, Kubernetes Engine also supports smart VMs,
so you can take advantage of the price point
from the Kubernetes clusters.
And in doing so, we have made sure
that the preemption, when it does happen, is very graceful.
And lastly, you can combine this price benefit
with custom machine types.
Custom machine types allow you to pick the exact point
for the number of cores and memory,
so that you don't have any waste that is often caused by having
predefined machine sizes.
Moving on to block storage, again, a simple set of choices.
At the very left, you are optimized for cost.
We call it the persistent disk efficient category.
The efficient category will be released next year.
In the middle, as you try to seek
a balance of price and performance,
we have PD balanced.
And then, as you go to the right,
you have the more performance-intensive storage
options, PD performance and PD extreme.
For example, PD extreme would be used for the highest end SAP
HANA databases, delivering up to 1,200k IOPS.
So we talked about compute and storage.
Let me address simplicity at scale
with two features that were released recently.
And we are continuously enhancing those features
with new capabilities.
First is VM Manager.
This is a suite of tools that allow you to do three things--
patch management, config management,
and inventory management for operating systems.
Combined together, this allows very easy visibility
into the posture of your operating system compliance.
Quickly identify where you are out of compliance,
and then launch from the dashboard,
or in an automatable manner, the remediation
to bring it back to compliance.
This is very important for managing VM fleets at scale.
Having this sort of burden and toil
removed by having it managed by the platform
saves a lot of time and costly labor.
Similar to VM Management, we have this feature
called managed instance groups.
But in this case, instead of managing the VMs,
you're actually managing a group of VMs
as a composite application, which is much more intuitive.
If this is what you want to do, you
can achieve that with Google Cloud.
For example, as shown here, we can auto heal applications.
So for example, some of the VMs in a group stop working.
The rest of the VMs will take over the load.
And when the VMs are healed, they're put back into the group
and they start managing the load with the rest of the group.
We can also achieve autoscaling and talk about that,
and things like auto updating.
It's very important that the managed instance groups are
stateful, so you can deliver a stateful application, not just
stateless applications.
We support that.
So let's talk about autoscaling.
With managed instance groups you can scale up
and scale down as needed.
Most often customers in the past have been
using scheduled autoscaling.
So if you're expecting a peak at 9:00 AM, then
a little bit in advance of that, schedule autoscaling.
And then later in the day have an autoscale down.
With Google Cloud you can do another thing in addition
to schedule auto scale.
You can use machine learning to have the platform recommend
or automatically scale up and scale down the managed instance
groups based on past patterns of traffic.
This is very useful when you're in a business where
you don't quite know how your traffic might evolve over time.
And so using machine learning at scale,
we are able to drive autoscaling with managed instance groups.
So in summary, I've covered a few of these core principles
that drive us.
Hopefully you see how our investments are really
driven by all the principles that I've shared here.
Over time I hope to come back to you and share much more.
And now I am very excited to have with me Omer Hasan,
VP of Operations at AppLovin.
AppLovin is a leading marketing platform
that helps mobile app developers grow their applications.
Omer, welcome, and thank you for being with us.
OMER HASAN: Glad to be here, Nirav.
NIRAV MEHTA: So Omer, let's start with your migration
to Google Cloud.
You achieved one of the faster migrations,
closing down several data centers,
and coming over to Google Cloud in a very short time.
I'd like to have you tell us a little bit about how
you went about it.
What were some of the key tools, practices
that you used in achieving the speed of migration
that you did?
OMER HASAN: Yeah, sure.
Our journey did start over on on-prem data centers.
And we found, as our business continued to grow,
they no longer could scale with us.
And we looked at Google Cloud for that,
and we leveraged tools like Google Cloud Interconnect
to help with our migration initially,
and then tools like Terraform and Chef
to build up our infrastructure.
We found that Terraform was really simple
with Google Cloud, and leveraged a lot of the APIs underneath.
And we were able to build our data centers really
fast and efficiently.
NIRAV MEHTA: What was the rough timeframe?
And I think you did quite a few data centers within a few days.
OMER HASAN: Yeah, sure.
We built out our infrastructure first on Google Cloud.
And when it came to migration day,
we were confident enough to do five data center
migrations in a single day.
NIRAV MEHTA: Wow, very good.
I'm sure a lot of planning, but also a lot of skills,
thoughtful training that went into it.
How did you go about guiding your teams
to embrace a new concept because they were working
with on-premise data centers?
What were some of the observations there?
OMER HASAN: Yeah, sure.
Part of that was working with Google's PSO team,
along with just reading a lot of documentation
and understanding the best practices of it.
From there, we started to automate and build out
our Terraform scripts and modules.
And it was easy from there.
NIRAV MEHTA: So the support for Terraform,
having the right libraries was important to you.
You found the Google support to be complete enough that you
could just be productive?
OMER HASAN: Absolutely.
The Google team was very responsive.
And if we needed a new feature, it was built out within a week.
NIRAV MEHTA: Good.
Good to know.
With respect to once you arrived on Google Cloud
and how you scaled up, what benefits you saw,
can you start with maybe, what were
some of the first observations you had in, hey, this
is working a lot better than it used to?
OMER HASAN: Yeah, the impact was immediate.
We saw that in our KPIs internally.
We saw latency drop by 25%.
We were just able to handle a lot more.
We saw, in addition to that, a material impact
to the business.
NIRAV MEHTA: What about SLAs?
How have your SLAs to the business changed
as a result of this move?
OMER HASAN: We were able to tighten up our SLAs.
Given that things were performing faster,
we were scaled out more, and doing this
all in the midst of a growing business.
And I'm pretty proud to say that we were able to just
do more, and cheaper.
NIRAV MEHTA: That's great.
Yeah, that's what we like to hear.
Let's talk about the network resilience,
the overall resilience of the platform.
Where did you see advantages in terms of eliminating
single points of failure?
OMER HASAN: Yeah, sure.
When we were on-prem we would have NAT-based load balancers.
And we found with moving into Google Cloud we didn't quite
need that anymore.
And by eliminating that single point of failure, and really
a choke point, we felt more confident
in our infrastructure.
And particularly in failover scenarios
where traffic increased by a lot, or significantly,
we had an ease of mind there.
NIRAV MEHTA: Great.
Yeah, good to know.
I think the collection of different availability features
come together to support your applications.
And knowing how you've used those features to deliver scale
to your business is very useful to us.
It realizes the promise of what we like to deliver.
I want to also talk about things that you're
doing that are above the raw virtual machine layer.
Tell us about any services you're using,
higher-order services in Google Cloud beyond virtual machines.
OMER HASAN: Yeah, sure.
We're using Dataproc today.
NIRAV MEHTA: What are you using that for?
OMER HASAN: We're using that for our machine
learning algorithms, and being able to do that at scale.
In fact, it was actually one of the first services
we used at Google.
And as we were growing and scaling our business
and doing more ML, we found that doing that on-prem
would just be a CapEx feat.
And it would really take a long time to build.
And so when we saw Dataproc it felt like a natural fit.
And able to scale out, and not only
scale out, but also lower our SLAs,
and do that in a cost-efficient manner.
NIRAV MEHTA: Yeah, and a lot more elastic, I imagine.
Because the nature of these workloads is they
go up and down unpredictably.
OMER HASAN: Correct.
NIRAV MEHTA: So that's what you meant by CapEx,
and not having to spend on those peaks--
OMER HASAN: Exactly.
NIRAV MEHTA: Makes a lot of sense.
So as I reflect back, you migrated
almost the entire platform over to Google Cloud,
achieved a larger regional footprint, better SLAs,
better performance, cost savings.
We couldn't ask for more.
Thank you so much, Omer.
That is an impressive journey.
The speed with which you migrated over to Google Cloud,
how you scaled, how you took advantage of all our principles
to improve your resilience, offer better SLAs,
better performance, these are all the things
we love to see in our customer.
So thank you for taking the time to join me
and sharing your experiences with the audience.
To all of you out there, it's a real pleasure to be with you.
Thank you for the time you took.
Hopefully you get a good glimpse of what
drives our roadmaps, what we've delivered recently.
I hope to come back and share much more with you over time,
and also bring more conversations with customers.
Thank you.