This is basically a full distributed systems design question dressed up as an ML infra problem.
Start by clarifying requirements and scale (job types, SLAs, cluster sizes), then design a layered architecture: API for submission, a scheduler with queues and priorities, and a resource manager for placement and preemption. Walk through the end-to-end flow of a job, highlighting trade-offs in bin-packing, fairness, and fault tolerance, and conclude with utilization tracking and monitoring.
Pro tip: Emphasize that ML training jobs are often long-running and gang-scheduled, while inference jobs are latency-sensitive; design separate queues and preemption policies for each, and discuss how to avoid resource fragmentation with bin-packing strategies like best-fit with anti-affinity.
Ask about job types (training vs inference), expected scale (jobs per day, cluster size), SLAs, and constraints (e.g., GPU types, data locality). This ensures the design meets actual needs.
Define a REST/gRPC API for submitting jobs with resource requests, priority, and constraints. Describe queueing with multiple priority levels and fairness policies (e.g., weighted fair queueing).
Explain how to match jobs to nodes using bin-packing algorithms (e.g., best-fit, first-fit) considering CPU, GPU, memory, and topology. Discuss handling heterogeneous resources and avoiding fragmentation.
Describe placing jobs across clusters based on capacity, cost, and data locality. Cover preemption policies (priority-based, checkpointing) and fault tolerance (retries, replication, health checks).
Outline metrics to track (GPU/CPU utilization, queue wait times, job success rates) and how to expose them for autoscaling and capacity planning. Mention dashboards and alerts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.