SPEAKER: Optimizing GKE clusters for cost
is not a one-time exercise.
It's a longer journey to shift team and organizational culture
while incrementally implementing best practices.
One of these best practices is to use monitoring systems
specific to GKE to help establish cost
optimization as a discipline.
Let's take a look at why we should do this
and how it can be done.
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As you may have seen in previous videos from the series,
there are a handful of critical areas
to practice cost optimization in a GKE environment.
We'll focus on three of these areas
and how they fit into a monitoring system for cost
optimization, specifically culture, bin packing, and app
right-sizing.
Let's start by discussing culture.
Teams who own GKE environments are not simply
trying to drive down costs while ignoring everything else.
They instead must balance cost-saving techniques
while ensuring the applications running on GKE
are still performant, stable, and continue to deliver value
to their users.
To effectively balance all of this,
teams have begun incorporating cloud financial management,
or FinOps, into their culture.
This is the practice of building systems for financial
accountability in the pay-as-you-go cloud world.
Now, put this into the context of Kubernetes.
Rapid adoption of Kubernetes in particular
means that many teams are still learning and establishing
their own best practices.
This is true when it comes to managing
the cost of running their clusters,
especially as they grow.
Continuous monitoring of these environments
enables the necessary cultural shift.
When teams have proper tuning and data at their disposal
to practice cost optimization, the awareness
shifts the discipline earlier in the software development
lifecycle.
So what specifically should we aim
to accomplish with monitoring here?
First, we'll want to focus on a cluster
administrator-centric monitoring system.
We can do so with a focus on cluster bin packing.
Bin packing is the ability to effectively schedule workloads
across nodes in a cluster, in such a way that ensures there
are little wasted resources--
CPU and memory, for example.
Efficient bin packing is achieved
with a real-time understanding of how much resources workloads
request, and how those workloads fit
into the various amounts of allocatable resources
that the VMs and the cluster have available.
Cluster administrators, when empowered
with the right monitoring data, can
improve how they support cluster tenants, while also
ensuring that provision infrastructure is not wasted.
We can even set up alerting policies
on the data collected for the most egregious cases
of overprovisioning to proactively kick off
the process of cost optimization.
Developers can also benefit from a different perspective--
a monitoring system that captures recommendations
for app right-sizing.
This enables developers to better understand
how much of the requested resources
are actually being utilized by their application.
With a deep understanding of scenarios
where they may be overprovisioned
in their workload requests, developers
can make informed decisions on how
they can easily reduce cost.
Now that we've looked at the importance of culture,
bin packing, and app right-sizing for GKE cost
optimization, the next step is setting up
the monitoring system.
Be sure to check out the paper linked in the description.
We publish an end-to-end guide on how
you can set up such a monitoring system with Google Cloud
Monitoring.
In the next video, we'll cover how cluster administrators can
set up monitoring for cluster bin packing
to optimize costs on GKE.
Stay tuned.
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