Compute Engine Right Sizing Custom Machine Types Committed Use

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An enterprise runs a suite of steady-state microservices on Google Compute Engine virtual machines (n2-standard-8 machine types with 8 vCPUs and 32 GB memory each) across a single region. The workloads run continuously 24/7 with predictable demand.

Cloud Monitoring telemetry shows that across all instances, actual average CPU utilization remains steady at 25% to 30%, while memory utilization remains consistently high at approximately 24 GB. The infrastructure team wants to eliminate resource over-provisioning and secure the highest possible continuous cost reductions over a 3-year commitment period.

Which combination of actions should the DevOps engineer implement to optimize workload efficiency and ongoing costs?

Retain the existing n2-standard-8 machine types to maximize Sustained Use Discounts (SUDs) across the billing account, and rely on automated disk snapshot schedules to reduce storage overhead.

Reconfigure the VMs using custom N2 machine types (such as 4 vCPUs and 24 GB memory) to match actual resource needs, and purchase a 3-year resource-based Committed Use Discount (CUD) for the baseline vCPUs and memory.

Downscale all instances to standard e2-standard-2 machine types and enable extreme persistent disks (pd-extreme) to offload memory paging to high-IOPS storage.

Convert the instances to Spot VMs running the n2-standard-8 profile and purchase a 1-year flexible Committed Use Discount to cover the Spot capacity.

Professional Cloud DevOps Engineer

Compute Engine Right Sizing Custom Machine Types Committed Use

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