GCP - Professional Cloud DevOps Engineer Study Guide

5: Optimizing performance and cost

This chapter closes the DevOps engineering surface with the performance and cost dimension: locating bottlenecks across the application, infrastructure, pipeline, and observability layers; reading Cloud Trace, logs, and metrics against Google Cloud recommender output; instrumenting applications for both latency and cost attribution; right-sizing Compute Engine, GKE, and Cloud Run workloads; designing autoscaling policies that track demand; layering caching with Memorystore and Cloud CDN; tuning GKE resource requests, limits, and pod autoscalers; and applying FinOps discount instruments across the fleet.