Kubernetes Incident Response Simulator
Automated detection, triage, and recovery in under forty two seconds
The Problem
The production payment pod hit an out-of-memory crash in a twelve-node EKS cluster. Liveness probes were failing, the Horizontal Pod Autoscaler could not scale because the node pool was already at capacity, and alert fatigue from over two hundred non-critical alerts was drowning out the actual P0 signal. The on-call engineer had to manually SSH into nodes, figure out which pod was failing, restart it by hand, and then verify the fix. That whole cycle averaged about forty five minutes per incident.
The Approach
I built a five-phase automated runbook. Detection uses Prometheus alerting rules with severity-based routing so only P0 and P1 alerts actually page the on-call. Triage runs OPA policies to classify each incident and suppress the noise. When the Horizontal Pod Autoscaler hits a resource ceiling, Karpenter handles just-in-time node provisioning to bring up capacity. The container restart phase uses exponential backoff with circuit breakers to avoid crash loops. Finally, verification runs synthetic transaction tests against the service to confirm it is healthy before the incident gets closed.
The Result
Mean time to recovery dropped from forty five minutes of manual firefighting to forty two seconds of fully automated response. Ninety four percent of incidents now resolve with zero human intervention. Alert noise dropped by roughly eighty five percent after the severity classification layer filtered out non-critical signals.
Try It Yourself
Interact with the simulation below. This replicates the actual engineering scenario described above.