Troubleshoot from Kubernetes symptom to cloud dependency

GKE incidents cross scheduler, quota, IP, admission and provider infrastructure. Evidence must connect both control planes.

What changed?

Events, audit logs and release activity establish the timeline.

Where is pressure?

Metrics connect workload saturation with node and service health.

Who pays?

Cost allocation maps supported resource requests to workloads and namespaces.

Why is it Pending?

The scheduler reason points toward capacity, quota, policy or address exhaustion.

Troubleshoot from Kubernetes symptom to cloud dependency

Operations orientation is easiest to understand by separating the Kubernetes contract from the Google Cloud implementation. Cloud Logging and Monitoring integrate with Kubernetes events and metrics, while Managed Service for Prometheus preserves PromQL workflows. The Kubernetes objects stay familiar, but GKE supplies controllers, infrastructure and safe defaults around them. This is why a team can move from an on-premises cluster without rewriting every workload, while still needing to redesign networking, identity and operational ownership for the cloud environment.

A useful inspection step is `kubectl get events -A --sort-by=.lastTimestamp`. Read the output as evidence, not as a ritual: first confirm the desired object exists, then look at status conditions, events and the Google Cloud resource it represents. In production, capture the expected result in a runbook or automated check so an operator can distinguish slow reconciliation from a configuration error.

Production gotcha: A single dashboard rarely explains a cross-layer failure; follow the dependency chain from symptom to controller to cloud resource. The safe habit is to verify quotas, regional availability and feature support against current Google Cloud documentation before rollout. Observability signals come first.