gang scheduling

Choose the control plane from job admission, placement, recovery and service needs, with a concrete partial-allocation failure.

See how partial allocation can strand two training jobs. Separate group admission, real placement, readiness and recovery instead of assuming atomic pod startup.

Build a durable, bounded scheduling loop with reservations and checkpoint-aware preemption, and avoid universal queue-wait formulas.

Count feasible placements for the waiting job, including its per-node and topology requirements. Then compare waiting, reservations and checkpoint/restart consolidation using their actual disruption costs.

Trace the job from quota reservation through admission to physical placement. Count eligible node shapes, not just free GPUs, and use controller and scheduler evidence to choose the least disruptive fix.

Follow preemption through checkpoint success or timeout, a safe stop barrier and requeue. See why an expired lease is not enough to free a GPU, and how observed checkpoint age changes victim cost.

Find concrete failure paths in a small serving architecture. Rank correctness, admission and placement fixes before proposing additional infrastructure.

Compare three GPU placement heuristics with executable traces. Show how admission and free-space shape change, then add workload and topology constraints.