The question basically ate the whole session.
Start by clarifying requirements and scale, then design a high-level architecture that separates real-time consumption tracking from budget enforcement and billing. Focus on data consistency and idempotency in credit operations, and explain how the system integrates with job scheduling and fair-share policies.
Pro tip: Emphasize idempotency and exactly-once semantics for credit deductions, as duplicate charges or missed deductions can erode tenant trust and cause financial discrepancies. Use a ledger-based approach with unique transaction IDs to ensure correctness.
Ask about expected number of tenants, jobs per second, credit granularity, and whether prepaid/postpaid models are both needed. Establish consistency and latency requirements.
Propose a ledger-based system with append-only transactions for credits and debits, ensuring idempotency via unique keys. Include tenant balances and real-time consumption aggregates.
Use a stream processing pipeline (e.g., Kafka + Flink) to track GPU usage and update balances. Enforce budgets at job submission and during execution with pre-authorization and periodic checks.
Define APIs for credit checks and deductions that the scheduler calls. Support prepaid (deduct upfront) and postpaid (accumulate usage, bill periodically) models with fair-share policies.
Ensure idempotency, consistency (e.g., via distributed transactions or sagas), and observability (metrics, logging, tracing). Discuss failure recovery and reconciliation.
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