A healthcare data platform processes streaming clinical records using Google Cloud Pub/Sub and a worker pool running on Compute Engine Managed Instance Groups (MIGs). During sudden peak emergency intake windows, message backlogs accumulate rapidly, yet worker instances fail to scale out in time to prevent processing delays. An operational audit identifies two core constraints: the MIG autoscaler is currently driven by average CPU utilization—which remains low while tasks wait in queue—and recent manual attempts to add capacity hit an error indicating that the region's vCPU quota limit of vCPUs was reached. Which combination of architectural actions should the Cloud Architect implement to optimize capacity planning and resolve the workload scaling bottlenecks?
- Submit a regional vCPU quota increase request in advance for the target region, and reconfigure the MIG autoscaling policy to scale dynamically based on a Cloud Monitoring metric tracking Pub/Sub unacknowledged message count per instance.Answer
- BLower the target CPU utilization threshold from 80% to 20% on the existing MIG autoscaler, and provision additional HA VPN tunnels to increase network bandwidth during emergency windows.
- CMigrate the database layer to Cloud Spanner across multi-regions to bypass regional vCPU quota limits automatically, while retaining CPU-based autoscaling for the compute instances.
- DRe-architect the processing worker pool to run on Google Kubernetes Engine (GKE) Autopilot, relying on cluster autoscaling to automatically bypass GCP regional project quotas without requesting quota adjustments.