This was basically six questions disguised as one.
Start by clarifying requirements and scale, then propose a layered architecture: a global control plane for inventory, scheduling, and policy, and per-cluster data planes for execution. Walk through each concern (telemetry, quotas, queueing, preemption, fairness, fault tolerance, cost) and explain how they interact, highlighting trade-offs and failure modes.
Pro tip: Emphasize that cost-aware placement must be balanced with data gravity and job deadlines; propose a scoring function that weights cost, latency, and reliability, and mention using spot instances with checkpointing for fault tolerance.
Ask about scale (number of GPUs, jobs, tenants), workload types (training vs inference), latency sensitivity, budget constraints, and compliance needs. This shapes the design and shows you avoid over-engineering.
Propose a centralized control plane that aggregates GPU inventory and telemetry from all clusters (on-prem and cloud) via agents. Use a consistent data model and expose APIs for querying and managing resources.
Design a scheduler that enforces quotas, supports priorities, and implements fair-share (e.g., weighted fair queueing or dominant resource fairness). Include job queueing, preemption, and gang scheduling for distributed jobs.
Describe mechanisms for handling node failures, preemption, and cloud outages (e.g., checkpointing, retries, replication). For placement, use a cost model that considers instance pricing, data transfer, and availability, and allow policies to prefer on-prem or cloud based on cost and latency.
Summarize key trade-offs (e.g., centralized vs decentralized scheduling, strict quotas vs burst capacity) and suggest how the system could evolve (e.g., adding ML-based prediction for demand).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.