Rightsizing an underutilized VM fleet safely

WHAT THIS TESTS This probes whether you optimize cost using statistically sound telemetry and risk awareness, not a knee-jerk reaction to one low average number.

A GOOD ANSWER COVERS Start by collecting at least two to four weeks of metrics so you capture weekly and seasonal patterns, and look at high percentiles like P95 or P99 rather than the mean, because a 15 percent average can hide regular spikes to 90 percent. Measure every dimension: CPU, memory, network throughput, and disk IOPS, since the binding constraint is often memory or IO, not CPU, and a memory-bound box looks idle on CPU. Identify the right instance family, perhaps moving from compute-optimized to general purpose or to a smaller size or a burstable type. Roll out incrementally, resizing a canary subset behind a load balancer, watching latency and error rates, and keeping a fast rollback. Automate detection with native advisors like AWS Compute Optimizer or Azure Advisor.

COMMON WRONG ANSWERS Resizing the whole fleet at once based on average CPU. Ignoring memory and IO entirely. Forgetting burst credits on T-family or B-series instances, where sustained load drains credits and throttles. Not accounting for headroom needed for failover when other nodes die.

LIKELY FOLLOW-UPS How do you handle stateful nodes. How would you automate this continuously. What about reserved capacity already purchased for the old size.

ONE CONCRETE EXAMPLE A fleet averaging 15 percent CPU shows P99 memory at 88 percent during nightly reports. You keep memory headroom, switch from a large compute-optimized type to a memory-balanced smaller type, canary two nodes for a week, then complete the migration, cutting spend roughly forty percent with stable latency.