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Difficulty: MediumCapacity Planning and Infrastructure Workload Scaling Optimization

A healthcare provider processes nightly patient diagnostic imaging datasets using batch processing worker VMs in Google Cloud Compute Engine. During peak overnight runs, the workload rapidly scales from 20 instances to 500 n2−standard−8n2-standard-8 instances to ensure completion before morning clinical operations. During a scaling dry run, instance creation failed after reaching 100 instances due to regional resource limits. Which strategy should the cloud architect implement to ensure reliable infrastructure scaling and capacity for these batch workloads?

  1. Submit a regional vCPU quota increase request in advance for the target region and configure Compute Engine capacity reservations for the required peak compute resources.Answer
  2. B
    Deploy the workload without requesting quota adjustments, relying on Compute Engine autoscaling to dynamically bypass project limits during burst scaling events.
  3. C
    Re-architect the batch processing workload to run on a Google Kubernetes Engine cluster with Autopilot to bypass Google Cloud regional quota requirements.
  4. D
    Migrate the incoming diagnostic image files from Cloud Storage to Cloud Spanner to support the API read throughput required during VM initialization.

Answer

The cloud architect should submit a regional vCPU quota increase request in advance for the target region and configure Compute Engine capacity reservations for the peak baseline instances.
Proactively requesting a regional vCPU quota increase ensures that the GCP project limits accommodate the 500 n2−standard−8n2-standard-8 instances (4,0004,000 vCPUs). Combining this with Compute Engine capacity reservations guarantees that the physical compute capacity is reserved and ready in the specified zone during the nightly batch window.

Step-by-Step Solution

1
Identify the cause of the provisioning failure during the workload scale-up.
The failure was caused by exceeding default project regional vCPU resource quotas when attempting to provision 500 n2−standard−8n2-standard-8 instances (4,0004,000 vCPUs total).
Google Cloud enforces regional quotas on compute resources to prevent accidental overallocation.
2
Evaluate capacity planning requirements for large-scale compute workloads.
A quota increase allows the project to request the required vCPU ceiling, while Compute Engine capacity reservations ensure physical hardware availability in the selected zone.
Having sufficient quota does not guarantee hardware availability during sudden capacity spikes unless capacity is explicitly reserved.
3
Select the correct GCP capacity optimization approach.
Combining regional quota increases with zonal reservations guarantees both administrative permission and physical capacity for peak workload execution.
This directly fulfills both scaling permission and resource availability requirements.

Key Concept

Capacity Planning and Regional Quota Management
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Topics

Capacity Planning and Infrastructure Workload Scaling Optimization