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](https://docs.cloud.google.com/products/calculator). New Google Cloud users might be eligible for a [free trial](https://docs.cloud.google.com/free).
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](https://docs.cloud.google.com/workstations/docs/tutorial-schedule-workstation-operations-using-cloud-scheduler-cloud-run#clean-up). ## Before you begin
## 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](https://docs.cloud.google.com/workstations/docs/locations). ## Application architecture This solution includes the following Google Cloud components: - [Cloud Run](https://docs.cloud.google.com/run/docs/overview/what-is-cloud-run) to update the **Quick start pool size** of the `WorkstationConfig`. - [Cloud Scheduler jobs](https://docs.cloud.google.com/scheduler/docs) to make calls on a set schedule to update the `WorkstationConfig`. ![System architecture diagram that shows scheduling Workstation operations using Cloud Scheduler and Cloud Run](https://docs.cloud.google.com/static/workstations/images/architecture-workstation-operations-scheduler.png) ## 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](https://docs.cloud.google.com/run/docs/reference/container-contract#port) 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](https://docs.cloud.google.com/iam/docs/service-accounts-create). 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 $REGION \ --role=roles/run.invoker ### Create a Cloud Scheduler configuration with authentication 1. Create a job and specify the `URL` that you copied from [Deploy to Cloud Run](https://docs.cloud.google.com/workstations/docs/tutorial-schedule-workstation-operations-using-cloud-scheduler-cloud-run#deploy-to-cloud-run): gcloud scheduler jobs create http workstation-pool-increaser-cron \ --http-method=POST \ --location=us-central1 \ --schedule="0 9 * * 1-5" \ --time-zone="America/Los_Angeles" \ --headers "Content-Type=application/json" \ --message-body='{"cluster":"$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` $CLUSTER to 2 at 9 am PST from Monday to Friday. For more information, see [Configuring Job Schedules](https://docs.cloud.google.com/scheduler/docs/configuring/cron-job-schedules). 2. Similarly, to reduce the pool size for your workstation configuration to 0 at the end of a work day run the following gcloud scheduler jobs create http workstation-pool-decreaser-cron \ --http-method=POST \ --location=$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](https://console.cloud.google.com/cloudscheduler)
`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](https://console.cloud.google.com/workstations/configurations)
2. Verify the **Quick start pool size** is 2. 3. View [logs](https://docs.cloud.google.com/run/docs/logging) 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 To avoid additional charges to your Google Cloud account, delete all the resources you deployed with this quickstart. ### Delete your repository Cloud Run doesn't charge you when your deployed service isn't in use. However, you might still be [charged for storing the container image in Artifact Registry](https://cloud.google.com/artifact-registry/pricing). To delete Artifact Registry repositories, follow the steps in [Delete repositories](https://docs.cloud.google.com/artifact-registry/docs/manage-repos#delete) in the Artifact Registry documentation. ### Delete your service Cloud Run services don't incur costs until they receive requests. To delete your Cloud Run service, follow one of these steps: ### Console To delete a service: 1. In the Google Cloud console, go to the Cloud Run **Services** page: [Go to Cloud Run](https://console.cloud.google.com/run/services) 2. Locate the service you want to delete in the services list, and click its checkbox to select it. 3. Click **Delete**. This deletes all revisions of the service. ### gcloud To delete a service, run the following command: ``` gcloud run services delete SERVICE --region REGION ``` Replace the following: - SERVICE: name of your service. - REGION: Google Cloud region of the service. ### Delete your test project Deleting your Google Cloud project stops billing for all resources in that project. To release all Google Cloud resources in your project, follow these steps: > [!CAUTION] > **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](https://console.cloud.google.com/iam-admin/projects) 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](https://console.cloud.google.com/cloudscheduler)
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](https://docs.cloud.google.com/architecture).