What is Google Kubernetes Engine?
Kubernetes is an open-source, extensible, and portable platform for managing containerized workloads and services. But turns out, Kubernetes is not that simple to manage, from installation to provisioning, to upgrades, SLAs, and scaling. Here Google Kubernetes Engine (GKE) comes into the picture.
Using Google Kubernetes Engine for cluster creation can help you abstract the complexities of a Kubernetes implementation. But, it can cost a lot if you don’t follow the best practices of cloud cost management and monitoring practices. In this blog, we will discover how you can use GKE and how to optimize its cost.
Features & Benefits of GKE:
Before moving forward, let’s understand the benefits and use cases of Google Kubernetes Engine (GKE).
Load Balancing: Load balancing for compute engine instance google cloud offers server-side load balancing so you can distribute incoming traffic across multiple virtual machine instances load balancer can scale your application to support heavy traffic detection and automatically remove unhealthy virtual machine instances using health checks and route traffic to the closest virtual machine this is a managed service which means its component is redundant and highly available so if a load balancing component fails it is restarted or replaced automatically and immediately.
Auto Scaling: GKE’s cluster auto scaler automatically resizes the number of nodes in a given node pool based on the demand of your workloads you don’t need to manually add or remove nodes or overprovision your node pools instead you specify a minimum and maximum size for the node pool and the rest is automatic.
Auto Upgrade: Auto updates will help you keep the nodes in your cluster up to date with the cluster control plane version when your control plane is updated with auto-update you don’t have to manually track and update your nodes when the control plane is updated on your behalf it also provides better security by automatically ensuring that security updates are applied and kept up to date with the latest Kubernetes features.
GKE Logging and Monitoring
Google Kubernetes Engine includes native integration with cloud monitoring and cloud logging when you create a gk cluster running on the google cloud operations for GKE is enabled by default and provides a monitoring dashboard specifically tailored for Kubernetes with cloud operations for GKE you can control whether or not cloud logging collects application logs you also have the option to disable the cloud monitoring and cloud logging integration altogether.
Getting started with GKE
To use Google Kubernetes Engine make sure you already have a GCP account, create your project, and make sure to enable the Artifact Registry and Google Kubernetes Engine APIs. And then open your Cloud Shell from Console and set the PROJECT_ID environment variable.
To set PROJECT_ID use command:
export PROJECT_ID=YOUR_PROJECT_ID
gcloud config set project $PROJECT_ID
Then create a repository with the following command, just make sure to choose your region (replace YOUR_REGION) according to your convenience:
gcloud artifacts repositories create hello-repo \
--repository-format=docker \
--location=YOUR_REGION \
--description="Docker repository"
Now, download or create a source code and Dockerfile. Then build and tag the Docker image for hello-app:
docker build -t YOUR_REGION-docker.pkg.dev/${PROJECT_ID}/hello-repo/hello-app:v1 .
Run docker images command to verify that the build was successful:
Finally, let’s push the Docker image to Artifact Registry run these commands to configure and push the docker image to the repository:
gcloud auth configure-docker us-west1-docker.pkg.dev
docker push us-west1-docker.pkg.dev/${PROJECT_ID}/hello-repo/hello-app:v1
Understanding GKE Pricing
Till here we understood how to use GKE now, let’s jump into its pricing.
| Service | Description | Price (USD) |
|---|---|---|
| Cluster operation mode | Standard mode or Autopilot mode | $0.10 per cluster per hour |
| Cluster management fees | The cluster management fee applies to all GKE clusters irrespective of the mode of operation, cluster size, or topology. | $0.10 per cluster per hour |
| Multi-cluster ingress pricing | Multi-cluster ingress standalone pricing is based on the number of Pods considered Multi-Cluster Ingress backends. | $3.00 per backend Pod per month (730 hours) |
Having an understanding of how GKE is priced for different services will hep you in managing GCP costs and building a sustainable cloud strategy that aligns with your business objectives. For more information, check out our definitive guide on GKE pricing
Google Kubernetes Engine Cost Optimization Strategies
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.
- 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.
- Bin Packing: We 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 few 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.
- App Right-sizing: 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 the right type of kubernetes cluster networking model, where they may be overprovisioned in their workload requests, developers can make informed decisions on how they can easily reduce costs.
GKE FinOps Implementation
The best long-term strategy is to establish a FinOps practice within your organization. Economize is committed to making your cloud spending simpler and noise-free to help engineering teams like yours understand and optimize it. By fostering collaboration between finance and engineering teams, you can ensure that financial accountability is embedded in your cloud operations. This approach will drive greater cost efficiency and resource utilization across your Google Kubernetes Engine (GKE) environment.
Conclusion
In summary, Google Kubernetes Engine (GKE) offers a robust and flexible solution for managing containerized applications in the cloud. However, the true potential of GKE can only be realized through diligent cost management and optimization practices. With the right tools and practices in place, your organization can harness the full capabilities of GKE while maintaining control over expenditures, ultimately driving business value and achieving your operational goals.
FAQs:
Q: How to reduce the cost of GKE?
A: To reduce GKE costs, implement strategies such as effective bin packing to optimize resource usage, utilize auto-scaling to adjust nodes based on demand, and conduct regular audits to identify and eliminate underutilized resources.
Q: What makes the Google Kubernetes Engine an effective solution?
A: GKE simplifies the management of Kubernetes by automating tasks like upgrades and scaling, providing built-in monitoring and logging, and enabling seamless integration with other Google Cloud services, which enhances both efficiency and performance.
Q: What are the advantages of the Google Kubernetes Engine?
A: The advantages of GKE include automated load balancing, auto-scaling, easy integration with Google Cloud services, robust security features, and a simplified user experience that allows teams to focus on application development rather than infrastructure management.
Q: How much does GKE cost per hour?
A: GKE costs approximately $0.10 per cluster per hour, with additional charges for cluster management and other services, such as multi-cluster ingress, which is billed based on the number of backend pods used.
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