The default Kubernetes scheduler operates at the pod level, making placement
decisions independently for each pod without understanding relationships between
them. This model works well for stateless microservices but falls short for AI
workloads that require job-level scheduling, coordinated placement, and queue
management. This paper explains how Kubernetes scheduling works, contrasts it
with traditional HPC scheduling, and introduces the meta-schedulers and queue
managers that bridge the gap for AI workloads.