1. GKE Stateful Workloads Disaster Recovery for AI Systems


    To achieve disaster recovery for stateful workloads in Google Kubernetes Engine (GKE) designed for AI systems, we would look into implementing a strategy that involves backups and restores of the Kubernetes state. In this context, stateful workloads could refer to anything that maintains state within a GKE cluster, such as databases, persistent volumes, or configurations that AI systems depend on to function and retrieve historical data.

    To do this, we'll explore the use of GKE Backup and Restore feature, which is a key component in implementing a solid disaster recovery plan. This feature allows you to backup your clusters' state and restore it when needed, ensuring consistency and minimal downtime in case of a disaster.

    In the code below, we'll create a backup plan for the GKE cluster that regularly backs up both the Kubernetes resources and persistent volumes with Pulumi's Google Native provider, then demonstrate how to create a restore plan to recover from a backup.

    Before proceeding with the code, let's discuss some key resources that we will use:

    • google-native.gkebackup/v1.BackupPlan: This resource represents a backup plan for a GKE cluster. We will define what needs to be backed up (like entire namespaces, specific applications, or more) and establish how frequently the backups shall occur.

    • google-native.gkebackup/v1.Backup: Associated with a backup plan, this resource actually carries out the backup as defined in the plan.

    • google-native.gkebackup/v1.RestorePlan: Defines parameters and resources required to restore from a backup, including where to restore the resources.

    • google-native.gkebackup/v1.Restore: A restore operation using the previously defined restore plan.

    Let's look at how we can implement this with Pulumi in Python:

    import pulumi import pulumi_google_native as google_native # Replace these variables with appropriate values for your setup project_id = "your-google-cloud-project-id" gke_cluster_location = "us-central1" gke_cluster_id = "your-gke-cluster-id" # Define a GKE Backup Plan for the AI system's stateful resources backup_plan = google_native.gkebackup.v1.BackupPlan("aiSystemBackupPlan", project=project_id, location=gke_cluster_location, backup_plan_id="ai-system-backup-plan", backup_config=google_native.gkebackup.v1.BackupPlanBackupConfigArgs( all_namespaces=True, # Backup all resources in all namespaces include_volume_data=True, # Include data from persistent volumes in the backup ), backup_schedule=google_native.gkebackup.v1.BackupPlanBackupScheduleArgs( cron_schedule="0 2 * * *", # Run the backup daily at 2:00 AM ), retention_policy=google_native.gkebackup.v1.BackupPlanRetentionPolicyArgs( backup_retain_days=7, # Retain backups for 7 days ), cluster=gke_cluster_id, # ID of the cluster to back up description="Backup plan for stateful workloads of the AI system in GKE.", ) # Define a GKE Restore Plan, specifying where to restore the resources restore_plan = google_native.gkebackup.v1.RestorePlan("aiSystemRestorePlan", project=project_id, location=gke_cluster_location, restore_plan_id="ai-system-restore-plan", cluster=gke_cluster_id, description="Restore plan for stateful workloads of the AI system in GKE.", # Here you'd specify any specific restoration configuration needed for your workloads ) # To initiate a restore, you would create a `Restore` resource with a reference to a specific backup # from a `Backup` list and the `RestorePlan` created above. # The actual restore would only be performed in a disaster recovery scenario # and therefore is not included in this proactive setup script. # Export the backup plan ID and restore plan ID pulumi.export("backup_plan_id", backup_plan.backup_plan_id) pulumi.export("restore_plan_id", restore_plan.restore_plan_id)

    In the code above, we define both a backup plan and a restore plan for a GKE cluster. The backup plan is scheduled to run daily and retain backups for a week. In case a restore is needed, you will use the Restore resource, specifying which backup to use and which restoration plan to apply, to perform the actual data recovery process. The provided code sets up the disaster recovery strategy but does not trigger the restoration process itself. Restoration is typically an action taken in response to a disaster event.

    Remember, this is just a starting point for managing stateful disaster recovery, and the strategy should be tailored to individual business and workload needs. Testing the recovery procedure is an essential step to ensure it works as expected and meets the recovery time objectives (RTO) and recovery point objectives (RPO) requirements of the AI applications running on GKE.