Kubernetes Incident Response Simulator
Automated detection, triage, and recovery in under 42 seconds
The Problem
The production payment pod hit an out-of-memory crash in a 12-node EKS cluster. Liveness probes failed, Horizontal Pod Autoscaler could not scale because the node pool was at capacity, and over two hundred non-critical alerts created notification fatigue. The on-call engineer had to manually SSH into nodes, identify the failing pod, restart it, and verify recovery. That manual cycle averaged forty-five minutes per incident.
The Approach
I built a 5-phase automated runbook. Detection uses Prometheus alerting rules with severity-based routing so only P0 and P1 alerts page the on-call engineer. Triage runs OPA policies to classify each incident and suppress non-critical noise. When the HPA hits a resource ceiling, Karpenter provisions compute nodes on demand. Container restarts use exponential backoff with circuit breakers to prevent crash loops. Finally, synthetic transaction tests verify health before closing the incident.
The Result
Mean time to recovery dropped from forty-five minutes of manual triage to forty-two seconds of automated remediation. Ninety-four percent of incidents resolve without human intervention. Alert noise dropped by eighty-five percent after severity-based classification.
Try It Yourself
Interact with the simulation below. This replicates the actual engineering scenario described above.