Right-size your GKE workloads at scale

This tutorial shows you how to right-size your Google Kubernetes Engine (GKE) workloads with VPA recommendations and usage metrics.

Understand why resource rightsizing is important

Under-provisioning can starve your containers of the necessary resources to run your applications, making them slow and unreliable. Over-provisioning doesn't impact the performance of your applications but might increase your monthly bill.

The following table describes the implications of under-provisioning and over-provisioning CPU and memory:

Resource Provisioning status Risk Explanation
CPU Over Cost Increases the cost of your workloads by reserving unnecessary resources.
Under Performance Can cause workloads to slow down or become unresponsive.
Not set Reliability CPU can be throttled to 0 causing your workloads to become unresponsive.
Memory Over Cost Increases the cost of your workloads by reserving unnecessary resources.
Under Reliability Can cause applications to terminate with an out of memory (OOM) error.
Not set Reliability kubelet can stop your Pods, at any time, and mark them as failed.


In this tutorial, you will learn how to:

  • Deploy a sample application.
  • Export GKE recommendations metrics from Monitoring to BigQuery.
  • Use BigQuery and Looker Studio to view GKE container recommendations across projects.


In this document, you use the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

Set up your project

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. In the Google Cloud console, on the project selector page, click Create project to begin creating a new Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Google Cloud project.

  4. Enable the Resource Manager, Google Kubernetes Engine, Cloud Monitoring, BigQuery, Cloud Run, Cloud Build APIs.

    Enable the APIs

  5. In the Google Cloud console, on the project selector page, click Create project to begin creating a new Google Cloud project.

    Go to project selector

  6. Make sure that billing is enabled for your Google Cloud project.

  7. Enable the Resource Manager, Google Kubernetes Engine, Cloud Monitoring, BigQuery, Cloud Run, Cloud Build APIs.

    Enable the APIs

  1. Grant roles to your Google Account. Run the following command once for each of the following IAM roles: roles/serviceusage.serviceUsageAdmin, roles/container.clusterAdmin, roles/iam.serviceAccountAdmin, roles/iam.securityAdmin, roles/container.admin

    $ gcloud projects add-iam-policy-binding PROJECT_ID --member="user:EMAIL_ADDRESS" --role=ROLE
    • Replace PROJECT_ID with your project ID.
    • Replace EMAIL_ADDRESS with your email address.
    • Replace ROLE with each individual role.

Set up your environment

In this tutorial, you use Cloud Shell to manage resources hosted on Google Cloud. Cloud Shell is preinstalled with the software you need for this tutorial, including Docker, kubectl, gcloud CLI, and Terraform.

To use Cloud Shell to set up your environment:

  1. Launch a Cloud Shell session from the Google Cloud console by clicking Cloud Shell activation icon Activate Cloud Shell in the Google Cloud console. This launches a session in the bottom pane of Google Cloud console.

  2. Set environment variables:

    export REGION=us-central1
    export ZONE=us-central1-f
    export IMAGE=$REGION-docker.pkg.dev/$PROJECT_ID/main/vpa-recs-image:latest

    Replace PROJECT_ID with your Google Cloud project ID.

  3. Set the default environment variables:

    gcloud config set project $PROJECT_ID
    gcloud config set compute/region $REGION
    gcloud config set compute/zone $ZONE
  4. Clone the code repository.

    git clone https://github.com/GoogleCloudPlatform/kubernetes-engine-samples
  5. Change to the working directory.

    cd kubernetes-engine-samples/cost-optimization/gke-vpa-recommendations

(Optional) Set up the sample application

This is an optional section to deploy a sample application. To use an existing cluster, ensure that Cloud Monitoring is configured on your cluster.

To simulate a realistic environment, you will use a setup script to deploy Online Boutique.

The following steps install the sample application and modify the default configuration. For example, the instructions configure the Horizontal Pod Autoscaler (HPA) for some workloads and changes resource requests and limits.

  1. Run the setup script:


    The setup script does the following:

    • Creates a GKE cluster.
    • Deploys the Online Boutique sample application.
    • Updates Pod CPU and memory resource requests.
    • Configures a HorizontalPodAutoscaler resource for the adservice workloads to simulate a realistic environment.

    The setup script might take up to 10 minutes to complete.

  2. Verify that the sample application is ready:

    kubectl get deployment

    The output is similar to the following:

    NAME                    READY   UP-TO-DATE   AVAILABLE   AGE
    adservice               2/2     2            2           4m54s
    cartservice             1/1     1            1           4m55s
    checkoutservice         1/1     1            1           4m56s
    currencyservice         1/1     1            1           4m55s
    emailservice            1/1     1            1           4m56s
    frontend                1/1     1            1           4m55s
    loadgenerator           1/1     1            1           4m55s
    paymentservice          1/1     1            1           4m55s
    productcatalogservice   1/1     1            1           4m55s
    recommendationservice   1/1     1            1           4m56s
    redis-cart              1/1     1            1           4m54s
    shippingservice         1/1     1            1           4m54s

Create a repository

Create the repository to store the metric exporter image.

  1. Create a new Docker repository:

    gcloud artifacts repositories create main --repository-format=docker \
        --location=$REGION \
        --description="docker repository" 
  2. Setup authentication to Docker repositories:

    gcloud auth configure-docker $REGION-docker.pkg.dev
  3. Deploy the image by running the following command:

    gcloud builds submit metrics-exporter --region=$REGION --tag $IMAGE

Deploy the application

In the following section, you use Terraform to perform the following tasks:

  • Create a Service Account and assign the permissions required to manage and interact with Google Cloud resources.
  • Grant the monitoring viewer, BigQuery data editor, BigQuery data owner, BigQuery job user, and Cloud Run invoker roles to the Service Account.
  • Deploy a Cloud Run job that pulls a Docker image from the Artifact Registry and runs it with the specified configuration.
  • Create a Cloud Scheduler job that triggers the Cloud Run service daily.
  • Create a BigQuery dataset, table and view to store metrics data and recommendations.

Configure Terraform

  1. Set configuration environment variables:

    export TF_VAR_BIGQUERY_DATASET=gke_metrics_dataset
    export TF_VAR_BIGQUERY_TABLE=gke_metrics
    export TF_VAR_METRIC_WINDOW=259200

    This command includes the following:

    • TF_VAR_BIGQUERY_DATASET and TF_VAR_BIGQUERY_TABLE: hold the GKE metric data.
    • TF_VAR_RECOMMENDATION_WINDOW_SECONDS: the timeframe for VPA recommendations. Defaults to 1,209,600 seconds, or 14 days.
    • TF_VAR_RECOMMENDATION_DISTANCE: the interval at which VPA recommendation data points are returned. Defaults to 86,400 seconds, or every 1 day.
    • TF_VAR_LATEST_WINDOW_SECONDS: the timeframe for obtaining the most recent requested and limit resource values. Defaults to 600 seconds, or 10 minutes.
    • METRIC_WINDOW: establishes the timeframe for GKE usage and utilization metrics. Defaults to 25,9200 seconds, or 3 days.
    • METRIC_DISTANCE: the interval at which data points are returned. Defaults to 600 seconds, or every 10 minutes.

    Adjust these values based on the needs of your workloads. Example, for batch workloads which run once a month, update TF_VAR_RECOMMENDATION_WINDOW_SECONDS and METRIC_WINDOW to 2592000 seconds (30 days).

Deploy the Terraform configuration

  1. Initialize, validate and apply your configuration:

    terraform -chdir=terraform init
    terraform -chdir=terraform validate
    terraform -chdir=terraform apply -var project_id=$PROJECT_ID -var region=$REGION -var image=$IMAGE

    This command provides an execution plan and asks for your approval before making any changes. Review the plan, and if everything is as expected, type yes to proceed.

    After the apply command completes successfully, your resources are created and managed by Terraform.

  2. Manually run the Cloud Scheduler job:

    gcloud scheduler jobs run recommendation-schedule --location ${REGION}

Verify the Deployment

  1. Select the Logs tab on the workload-recommendations details page.

  2. Verify the metrics logs are being processed in the Cloud Run console:

    Go to Cloud Run

    The logs show metrics being written to BigQuery. The output should be similar to the following:

    INFO - Building Row
    INFO - Successfully wrote 12 rows to BigQuery table [PROJECT_ID].gke_metric_dataset.gke_metrics.
    INFO - Run Completed

    If output does not match, wait five minutes and then run the command gcloud scheduler jobs run recommendation-schedule --location $REGION.

View the container recommendation in BigQuery

  1. Go to the BigQuery page in the Google Cloud console:

    Go to BigQuery

  2. Verify the data is visible in the gke_metrics table and the container_recommendations view. Depending on the number of workloads, it might take a few minutes to write all metrics to BigQuery.

  3. In the query editor, select all rows in the container_recommendations view:

    SELECT * FROM `PROJECT_ID.gke_metrics_dataset.container_recommendations`

    This program extracts the following metrics from cloud monitoring:

    • Workload details: the project ID, cluster name, controller and container name.

    • CPU/memory usage and utilization: the amount of CPU and memory that is being used by the workload, as well as the percentage of CPU and memory that is being utilized.

    • Requested and limits: the amount of CPU and memory that was requested for the workload, as well as the maximum amount of CPU and memory that is allowed for the workload.

    • CPU and memory workload recommendations: recommendations for how much CPU and memory should be allocated to the workload to ensure that it runs smoothly, based on VPA recommendations for Deployments and on actual usage and target utilizations for non-Deployment objects.

Visualize recommendations in Looker Studio

Looker Studio is a free, self-service business intelligence platform that lets you build and consume data visualizations, dashboards, and reports. With Looker Studio, you can connect to your data, create visualizations, and share your insights with others.

Use Looker Studio to visualize data in the BigQuery container_recommendations view:

  1. Open the Workload Rightsizing dashboard template
  2. Click Use my own data.
  3. Select your project.
  4. For Dataset, select gke_metric_dataset.
  5. For Table, select container_recommendations.
  6. Click Add.
  7. Click Add to Report.

Looker Studio template details

The Looker Studio template details page provides the following information:

  • GKE Workload Rightsizing Overview: provides an overview of your clusters, including:
    • The number of Best Effort and Burstable workloads that are at risk of reliability and performance issues.
    • Potential CPU and memory resource savings. Positive values indicate over-provisioning, while negative values indicate under-provisioning.
  • Workload Recommendations: Provides recommendations for workload CPU and memory requests and limits.
  • GKE Workloads at Risk: Shows workloads that are at the greatest risk of experiencing reliability and performance issues.
  • History - Workload Rightsizing - How are we doing?: Provides an historical view of how well workload rightsizing and reducing the number of Best Effort workloads has been implemented.

CPU requested and limit container recommendation

If the workloads CPU requested and limit values are equal, the QoS is considered Guaranteed, and the CPU recommendation is set to the maximum within the window period of 14 days. Otherwise, the 95th percentile of the CPU requested recommendation within 14 days is used.

When the CPU request and limit values are equal, the recommendation for CPU limit is set to the maximum CPU request VPA recommendation for Deployment objects only and the CPU usage with a target utilization of 70%. If the request and limit of the workload are not identical, the existing limit ratio is used.

Memory requested and limit container recommendation

Memory recommendations use the maximum VPA recommendation for Deployments objects only and the maximum memory usage with a target utilization of 80% to ensure the workloads reliability. You can update the target utilization values in the container_recommendation view's query.

It is best practice to use the same amount of memory for requests and limits because memory is an incompressible resource. When memory is exhausted, the Pod must be taken down. To avoid having Pods taken down and destabilizing your environment, you must set the requested memory to the memory limit.

Prioritizing recommendations

A priority value is assigned to each row to surface workloads which require immediate attention based on the recommendations. The units of CPU and memory are different. To normalize the units, the E2 machine type on-demand price ratio between predefined CPU and memory is used as an approximation to convert memory units to CPU units.

The priority is calculated using the following formula:

priority = (CPU requested - CPU recommendation) + ((memory requested -
memory recommendation) / (vCPUs on-demand pricing /memory on-demand pricing ))

For Autopilot, the total resources requested by your deployment configuration should be within the supported minimum and maximum values.

View VPA recommendations for multiple projects

To view VPA container recommendations across multiple projects, use a new project as a scoping project.

When deploying this project in your production environment, add all projects you want to analyze to the new project's metrics scope.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

The easiest way to avoid billing is to delete the project you created for the tutorial.

Delete a Google Cloud project:

gcloud projects delete PROJECT_ID

What's next