Schedule Workstation operations using Cloud Scheduler and Cloud Run

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This tutorial shows how to use Cloud Scheduler and Cloud Run to automatically perform operations like

• Scheduling automatic Quick start pool size increases and decreases.
• Automatically starting workstations on a regular schedule.

This tutorial helps you increase and decrease Quick start pool size to match typical business hours.

Objectives

1. Write and deploy a Cloud Run Service that updates the Quick start pool size for a workstation configuration.
2. Configure a Cloud Scheduler job that schedules the service created in Step 1 to run 09:00-17:00, Monday-Friday to match PST business hours.

Costs

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

• Cloud Scheduler
• Cloud Run

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

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, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

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

4. Enable the Cloud Run, Cloud Scheduler, Cloud Workstations APIs.

   Enable the APIs

5. Install the Google Cloud CLI.

6. To initialize the gcloud CLI, run the following command:

   gcloud init

7. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

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

9. Enable the Cloud Run, Cloud Scheduler, Cloud Workstations APIs.

   Enable the APIs

10. Install the Google Cloud CLI.

11. To initialize the gcloud CLI, run the following command:

    gcloud init

Prepare the environment

Set the following environment variables, which are used by the automated scripts that you create later.

1. Set the PROJECT_ID and REGION variables you plan to use:

   PROJECT_ID=$PROJECT_ID
   REGION=$REGION
   

   Replace $REGION with the region name that you plan to use—for example, us-central1.

   For more information about available regions, see Cloud Workstations locations.

Application architecture

This solution includes the following Google Cloud components:

• Cloud Run to update the Quick start pool size of the WorkstationConfig.
• Cloud Scheduler jobs to make calls on a set schedule to update the WorkstationConfig.

Create a Cloud Run service

This first step is to set up a simple web server to listen to the HTTP requests you receive on port 8080. Since the application is containerized, you can write your server in any language.

To write the web server listener application in Python, do the following:

1. Create a new directory named workstation-config-updater and change directory into it:

   mkdir workstation-config-updater
   cd workstation-config-updater
   

2. Create a file named app.py and paste the following code into it:

   import os, subprocess
   from flask import Flask, request, abort
   
   app = Flask(__name__)
   
   @app.route("/", methods=["POST"])
   def update():
       app.logger.info("Update request received.")
       data = request.json
       cluster = data["cluster"]
       region = data["region"]
       pool_size = data["pool-size"]
   
       path = os.path.join(app.root_path, "update_config.sh")
       o = subprocess.run(
           [path, cluster, region, pool_size],
           stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True
       )
       app.logger.info("Sending response:", o.stdout)
       return o.stdout
   
   if __name__ == "__main__":
       app.run(host="0.0.0.0", port=8080, debug=True)
   

   This code creates a basic web server that listens on the port defined by the PORT environment variable and executes the script update_config.sh.

3. Create a file named update_config.sh and paste the following code into it:

   #!/bin/bash
   
   set -e
   
   if [ $# -ne 3 ]
   then
      echo "Usage: update_config.sh CLUSTER REGION POOL_SIZE"
      exit 1
   fi
   
   CLUSTER=$1
   REGION=$2
   POOL_SIZE=$3
   
   # list workstation configs
   echo "Attempting to list workstation configs in cluster $CLUSTER and region $REGION ..."
   for CONFIG in $(gcloud  workstations configs list --cluster $CLUSTER --region $REGION --format="value(NAME)"); do
       echo "Attempting to update Quick Pool Size to $POOL_SIZE for config $CONFIG ..."
       # update the workstation config pool-size
       RET=$(gcloud workstations configs update $CONFIG --cluster $CLUSTER  --region $REGION --pool-size=$POOL_SIZE)
       if [[ $RET -eq 0 ]]; then
           echo "Workstation config $CONFIG updated."
       else
           echo "Workstation config $CONFIG update failed."
       fi
   done
   
   

   This script uses gcloud commands to list all WorkstationConfig in a given cluster and update its Quick start Pool Size to POOL_SIZE.

4. Create a file named Dockerfile and paste the following code into it:

   FROM google/cloud-sdk
   
   RUN apt-get update && apt-get install -y python3-pip python3
   
   # Copy local code to the container image.
   ENV APP_HOME /app
   WORKDIR $APP_HOME
   COPY . ./
   
   RUN /bin/bash -c 'ls -la; chmod +x ./update_config.sh'
   
   # Install production dependencies.
   RUN pip3 install Flask gunicorn
   
   # Run the web service on container startup
   CMD exec gunicorn --bind :8080 --workers 1 --threads 8 app:app
   

   This code containerizes the application to make it ready to be deployed on Cloud Run.

Deploy to Cloud Run

To deploy to Cloud Run, run the following command:

gcloud run deploy --source . --project $PROJECT_ID --region $REGION

1. When you are prompted for the service name, press Enter to accept the default name workstation-config-updater.

2. If you are prompted to enable the Artifact Registry API or to allow creation of Artifact Registry repository, press y.

3. When you're prompted to allow unauthenticated invocations, press n.

4. Wait until the deployment is complete.

5. When the service URL is displayed in the following format, copy it:

SERVICE_URL=$SERVICE_URL

Configure service account to invoke Cloud Run

The workstation-config-updater service that you deployed does not allow unauthenticated invocations.

Cloud Scheduler requires a service account that has the appropriate credentials to call the workstation-config-updater service.

Set up the service account

1. If you don't already have a service account that you want to use for Cloud Scheduler jobs, create a new service account.

   gcloud iam service-accounts create $SERVICE_ACCOUNT_NAME \
       --description="$DESCRIPTION" \
       --display-name="$DISPLAY_NAME"
   

2. Add the requisite IAM role binding to allow your service account to invoke Cloud Run.

   gcloud run services add-iam-policy-binding workstation-config-updater \
       --member=serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com \
       --region $REGION \
       --role=roles/run.invoker
   

Create a Cloud Scheduler configuration with authentication

1. Create a job and specify the URL that you copied from Deploy to Cloud Run:

   gcloud scheduler jobs create http workstation-pool-increaser-cron \
       --http-method=POST \
       --location=us-central1 \
       --schedule="0 9 * * 1-5" \
       --time-zone="America/Los_Angeles" \
       --headers "Content-Type=application/json" \
       --message-body='{"cluster":"$CLUSTER", "region":"$REGION", "pool-size": "2"}' \
       --uri=$SERVICE_URL \
       --oidc-service-account-email=$SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com
   

   This command schedules a job to increase the Quick start pool size for all WorkstationConfigs in WorkstationCluster $CLUSTER to 2 at 9 am PST from Monday to Friday.

   For more information, see Configuring Job Schedules.

2. Similarly, to reduce the pool size for your workstation configuration to 0 at the end of a work day run the following

   gcloud scheduler jobs create http workstation-pool-decreaser-cron \
       --http-method=POST \
       --location=$REGION \
       --schedule="0 17 * * 1-5" \
       --time-zone="America/Los_Angeles" \
       --headers "Content-Type=application/json" \
       --message-body='{"cluster":"$CLUSTER", "region":"$REGION", "pool-size": "0"}' \
       --uri=$SERVICE_URL \
       --oidc-service-account-email=$SERVICE-ACCOUNT@$PROJECT_ID.iam.gserviceaccount.com
   

Optional: Verify the jobs

To make sure that your jobs are working as expected, you can verify the jobs.

1. Go to the Cloud Scheduler page in the Google Cloud console.

   Go to Cloud Scheduler

   workstation-pool-increaser-cron should appear on the list of jobs.

2. In the row for workstation-pool-increaser-cron job, click Actions > Force a job run.

   The first job created in a project can take a few minutes to run.

3. In the Status of last execution column, a Success status indicates that you have successfully run your job.

To verify that the Workstation configurations are updated, do the following:

1. Go to the Workstation Configurations page in the Google Cloud console.

   Go to Workstation Configurations

2. Verify the Quick start pool size is 2.

3. View logs for your Cloud Run service.

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.

Remove your test project

While Cloud Run does not charge when the service is not in use, you might still be charged for storing the container image in Artifact Registry. You can delete your container image or delete your Google Cloud project to avoid incurring charges. Deleting your Google Cloud project stops billing for all the resources used within that project.

Caution: Deleting a project has the following effects:
• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.
1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

Delete Cloud Scheduler jobs

To delete individual Cloud Scheduler resources,

1. Go to the Cloud Scheduler page in the Google Cloud console.

   Go to Cloud Scheduler

2. Click the checkboxes next to your jobs.

3. Click the Delete button at the top of the page and confirm your delete.

What's next

• Explore reference architectures, diagrams, and best practices about Google Cloud. Take a look at our Cloud Architecture Center.