Pod startup optimization is no longer enough; restart latency is now a core bottleneck for reliability and cost, especially in AI/ML and batch workloads. This session connects start-up and restart as one lifecycle problem. We begin with a practical taxonomy (container restart vs Pod recreation vs rollout/eviction), then break down where restart time is lost: re-scheduling, image pull, init re-execution, and control-plane churn.

The main case study is JobSet in-place restart, where prototype results show recovery improving from 2m10s to 10s at 5,000-node scale. We map this to new Kubernetes capabilities such as per-container restart policies/rules and restart-all-containers. Finally, we provide a production checklist across kubelet tuning, workload controller design, probes/signals, and observability so teams can safely reduce restart time without harming SLOs.