참고 답변
Capacity planning combines telemetry, trend analysis, and business context. I ingest time-series metrics—CPU, memory, IOPS, network egress—into Prometheus and visualise them in Grafana, applying Holt-Winters forecasting to identify 30-, 60-, and 90-day thresholds. Next, I overlay business events: marketing campaigns, end-of-quarter financial closes, or product launches that historically spike traffic. I then run “what-ifs” in CloudWatch Metric Math to simulate load increments and validate autoscaling policies. Budget constraints matter, so I model right-sizing scenarios—spot instances, reserved capacity, or on-prem expansion—presenting ROI comparisons to finance. Finally, I bake capacity checkpoints into the release cycle so the forecast evolves with every new microservice. This data-driven, iterative method prevents surprise shortages while optimising spend.