This is the foundation question and I spent probably too long here.
Start by clarifying the scope and assumptions (e.g., expected job duration, scale, and reliability requirements). Then walk through the lifecycle in chronological order: submission, queuing, dispatch, execution, and status/result retrieval. Emphasize trade-offs and design choices at each stage, such as queueing strategy, worker selection, and status update mechanisms.
Pro tip: Show awareness of real-world constraints like GPU scarcity, job preemption, and cost optimization. Mention how you would handle failures and ensure idempotency, as these are critical in production systems.
Describe how a client submits a job via an API endpoint, including authentication, request validation, and job ID generation. Mention any initial checks like resource quotas or parameter validation.
Explain where the job waits: a durable queue (e.g., Kafka, SQS) or database-backed queue. Discuss prioritization, fairness, and how jobs are persisted to survive failures.
Cover how a scheduler picks jobs and assigns them to available GPU workers, considering factors like GPU type, locality, and current load. Mention worker registration and heartbeat mechanisms.
Describe how the worker executes the job, reports progress, and handles failures (e.g., retries, timeouts). Include how intermediate status is updated.
Explain how the client polls or subscribes for status updates and retrieves results (e.g., via webhooks, polling, or streaming). Discuss result storage and access control.
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Strict priority was the obvious wrong answer and I knew that going in, so I jumped to weighted fair queuing pretty fast.
Start by clarifying the tiers, their SLOs, and the workload characteristics. Then propose a hierarchical scheduling architecture that combines strict priority for latency-sensitive tiers with fair-share or weighted fair queuing for lower tiers, and explicitly address starvation prevention through mechanisms like aging, borrowing, or reserved capacity. Finally, discuss trade-offs and how you would validate the design under sustained load.
Pro tip: Emphasize that starvation prevention is not just about fairness but also about meeting business SLIs for all tiers—show you understand that even best-effort tiers may have contractual or reputational implications. Mention concrete metrics like tail latency and starvation time bounds.
Ask about the number of tiers, their SLOs (e.g., latency, throughput), workload patterns, and whether tiers are internal or external. Confirm if preemption is allowed and what the cost of starvation is.
Propose a hierarchical scheduler: e.g., strict priority for the highest tier, weighted fair queuing (WFQ) or deficit round robin (DRR) for others. Explain how weights map to SLOs and capacity.
Introduce mechanisms like aging (promoting long-waiting requests), borrowing (allowing lower tiers to use idle capacity from higher tiers), or reserved minimum shares. Ensure no tier is permanently blocked.
Discuss how the design handles bursts, overload, and failures. Compare strict priority vs. fair sharing in terms of SLO attainment and complexity.
Propose simulation or load testing to verify SLOs and starvation bounds. Define metrics like per-tier latency percentiles, starvation duration, and fairness index to monitor in production.
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Frame the answer around a layered admission control system where quotas are enforced as early as possible (edge/gateway) using distributed counters and token buckets, with the scheduler only seeing traffic that has already passed quota checks. Emphasize that fairness is a scheduling concern only for admitted work, and that rejecting excess upstream protects the scheduler and improves overall system health.
Pro tip: Mention that quota enforcement must be fast, local, and eventually consistent—use approximate counters with periodic reconciliation rather than synchronous global locks, because a slow quota check is as bad as no quota check. Also note that you should return clear 429s with Retry-After headers so clients can back off intelligently.
Identify the quota dimensions (per-tier, per-customer, maybe per-endpoint) and decide where to enforce them—ideally at the API gateway or a dedicated admission service before requests reach the scheduler. Explain that enforcing at admission prevents wasted scheduler cycles.
Select an algorithm like token bucket or sliding window with distributed counters (e.g., Redis or a local sidecar with gossip). Discuss trade-offs between strict global limits and approximate local limits with periodic sync.
Describe how to allow bursts for higher tiers (e.g., larger bucket sizes) and how to ensure lower tiers aren't starved. Mention that the scheduler can then apply fair queuing only among admitted requests, simplifying its job.
Explain what happens if the quota service is unavailable—fail open or closed? Include metrics, logging, and alerting on quota rejections and near-limit usage. Also mention client feedback via 429 and Retry-After.
Discuss how to scale the quota enforcement horizontally, handle hot customers, and adjust quotas dynamically. Mention that quotas can be enforced at multiple layers (edge, service mesh) for defense in depth.
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This is where the question got interesting.
Start by framing the decision as a policy-driven trade-off between cluster utilization, job priority, and preemption cost. Then walk through a concrete decision framework: evaluate preemption cost (checkpoint availability, restart time), job priority and SLA, and resource fit. Finally, discuss checkpointing vs. kill-restart with attention to overhead, fault tolerance, and implementation details like checkpoint frequency and storage.
Pro tip: Emphasize that preemption decisions should be automated via a scheduler policy (e.g., priority preemption with checkpoint-aware eviction) rather than manual, and that checkpointing is usually worth the overhead for long-running jobs but not for short ones—quantify the trade-off with restart time vs. checkpoint time.
Establish clear job priority levels (e.g., high, medium, low) and preemption rules: only preempt lower-priority jobs, and prefer jobs that are cheapest to preempt. Consider fair-share or SLA-based policies.
For each running job, estimate the cost to preempt: does it have recent checkpoints? How long to restart from scratch? How much progress would be lost? Prefer jobs with low restart cost or recent checkpoints.
If the job supports checkpointing and the checkpoint overhead is small relative to restart time, checkpoint then preempt. If checkpointing is expensive or the job is short, kill and restart later. Consider checkpoint frequency and storage I/O.
Signal the job to checkpoint if needed, wait for completion (with timeout), then release resources. If timeout, force kill. Log the preemption event for auditing and future policy tuning.
Track metrics like preemption frequency, checkpoint overhead, and job restart times. Use this data to adjust preemption thresholds and checkpoint strategies to minimize overall cluster waste.
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Frame the problem as a scheduling and resource management challenge, then propose a solution that combines gang scheduling with topology-aware placement and preemption. Emphasize the trade-offs between utilization, fairness, and job completion time, and how your design minimizes fragmentation and idle waiting.
Pro tip: Show awareness that perfect solutions are impossible; instead, focus on minimizing fragmentation and idle time through policies like backfilling and preemption, and mention how you would measure and iterate on these metrics in production.
Clarify the workload characteristics (job sizes, durations, GPU types, network topology) and the goals (maximize utilization, minimize fragmentation, ensure fairness).
Propose gang scheduling to allocate all GPUs for a job atomically, and consider backfilling to fill idle resources with smaller jobs that can complete before the large job's start time.
Use topology-aware placement to keep multi-GPU jobs within a single node or rack when possible, and employ bin-packing or best-fit algorithms to reduce fragmentation.
Introduce preemption or checkpointing for low-priority jobs to free resources for large jobs, and use reservation or queueing mechanisms to avoid indefinite waiting.
Track metrics like GPU utilization, job wait time, and fragmentation, and use them to tune scheduling parameters or switch strategies dynamically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Per-customer quota cap within the enterprise tier.
Start by framing the problem as multi-tenant resource isolation and fairness, then propose a layered defense: admission control, per-tenant quotas, and priority scheduling. Finally, describe the offending customer's experience, emphasizing transparency and graceful degradation rather than silent failure.
Pro tip: Mention that you would instrument and alert on per-tenant usage patterns to detect anomalies before they impact others, and that you'd communicate proactively with the offending customer about their quota limits and options.
Implement per-tenant rate limits and admission control at the API gateway to reject or queue excess requests before they consume backend resources.
Enforce hard quotas on compute, memory, and concurrency per tenant, and use resource isolation (e.g., cgroups, separate queues) to prevent noisy neighbors.
Use weighted fair queuing or priority-based scheduling to ensure lower-tier customers still get their guaranteed share of resources.
Apply backpressure to the offending tenant by throttling or queuing their jobs, while allowing critical or higher-priority jobs to proceed.
The offending customer experiences slower processing, explicit rate-limit errors, or queued jobs with estimated wait times, plus proactive notifications and dashboards showing quota usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that multi-minute provisioning lag fundamentally shifts autoscaling from a reactive to a predictive problem, requiring queue-aware and preemption-aware policies. Then propose a two-tier strategy: short-term reactive scaling for burst absorption and long-term predictive scaling based on workload forecasts, with preemption used to reclaim capacity from low-priority jobs when demand spikes.
Pro tip: Emphasize that the goal is not to eliminate lag but to make the system resilient to it by decoupling job admission from resource availability—e.g., using a queue with deadlines and preemption to maintain utilization while meeting SLAs.
Quantify the provisioning lag (e.g., 2-5 minutes) and analyze workload patterns (arrival rate, job duration, priority mix) to understand the impact on queueing and utilization.
Implement priority queues with deadlines and preemption, where high-priority jobs can preempt lower-priority ones, and low-priority jobs fill gaps to maintain utilization.
Use historical data and workload forecasts to pre-provision GPU capacity ahead of demand, reducing reliance on reactive scaling that suffers from lag.
Establish clear preemption rules: which jobs can be preempted, how to checkpoint and resume them, and how to avoid thrashing by limiting preemption frequency.
Continuously monitor queue lengths, provisioning latency, and job success rates; adjust scaling thresholds and preemption aggressiveness based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer from me: tag jobs with hardware requirements at submission, tag GPUs with their SKU, and filter eligible GPUs before running the scheduling algorithm.
Start by framing the problem as a constraint-based scheduling challenge, where each job declares hardware requirements and the scheduler matches them to available GPU SKUs. Then describe a two-phase approach: first filtering nodes by compatibility (e.g., GPU model, memory, driver version), then scoring and selecting among compatible nodes based on utilization and other policies. Finally, discuss how to handle dynamic changes and failures to ensure jobs are rescheduled appropriately.
Pro tip: Emphasize that compatibility should be expressed as a declarative, extensible set of labels or taints/tolerations rather than hardcoded logic, so new GPU SKUs can be added without scheduler changes. Also mention that preemption and backfilling can improve utilization while respecting compatibility constraints.
Explain how jobs specify their hardware needs (e.g., GPU model, memory, compute capability) and how nodes are labeled with their GPU SKU attributes. This creates a declarative contract for compatibility.
Describe the filtering phase where the scheduler excludes nodes that don't match the job's requirements. This can be done via label selectors, taints/tolerations, or custom predicates.
After filtering, the scheduler scores remaining nodes based on factors like current utilization, locality, and fairness, then picks the best fit. This optimizes resource usage while respecting constraints.
Discuss how the scheduler reacts to node failures, GPU errors, or changes in job requirements. This includes rescheduling jobs to compatible nodes and possibly preempting lower-priority jobs.
Mention the importance of observability (metrics on scheduling latency, utilization per SKU) and the ability to extend the compatibility model as new hardware is added.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cache key is a hash of prompt plus all generation parameters.
Start by clarifying the system context and requirements, then propose a caching/deduplication layer with a well-defined cache key and eviction policy. Discuss correctness risks like stale results and non-determinism, and privacy risks like cross-user data leakage, and explain how to mitigate them with TTLs, scoping, and encryption.
Pro tip: Emphasize that caching is a trade-off between latency/cost and correctness/privacy; show you can quantify the impact and propose safeguards like per-user cache scoping and audit logs.
Ask about the expected scale, latency requirements, and whether prompts may contain sensitive data. Determine if the system is multi-tenant and what compliance requirements exist.
Define a cache key that includes the prompt, parameters, model version, and user/tenant ID to avoid cross-contamination. Choose a storage layer (e.g., Redis, Memcached) with appropriate TTL and eviction policies.
Use a request coalescing mechanism to deduplicate identical in-flight requests. For caching, check the cache before processing and store results after completion, ensuring atomicity and consistency.
Address risks like stale results (use TTL and invalidation), non-deterministic outputs (include model version and temperature in key), and cache poisoning (validate inputs and outputs).
Scope caches per user/tenant, encrypt sensitive data at rest, and implement access controls. Consider data retention policies and the right to be forgotten (e.g., cache invalidation on user deletion).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Distributed lock or a compare-and-swap on job state before dispatch.
Start by explaining the general architecture for scheduler high availability, such as active-passive or leader election with a distributed consensus system. Then, dive into the specific mechanisms that prevent duplicate job dispatch, like distributed locks, idempotency, and exactly-once semantics. Emphasize trade-offs between consistency, availability, and complexity.
Pro tip: Mention that true exactly-once dispatch is impossible in distributed systems, so you design for at-least-once with idempotent workers and deduplication. This shows you understand the theoretical limits and practical workarounds.
Describe how the scheduler itself is made highly available, e.g., using a leader election protocol (Raft, Paxos) with a distributed store like etcd or ZooKeeper, or a multi-active design with partitioning.
Explain the protocol for dispatching jobs: the scheduler assigns a job to a worker by writing to a durable queue or database, and the worker acknowledges completion. This ensures the job state is persisted before dispatch.
Detail mechanisms to prevent a job from being dispatched twice after a crash: distributed locks, leases with timeouts, idempotent job execution, and deduplication using unique job IDs.
Describe how the system recovers from a scheduler crash: a new leader is elected, it reads the persisted state, and resumes dispatching jobs that are not yet completed, while ensuring no duplicates via the mechanisms above.
Discuss the trade-offs between consistency, availability, and latency, and clarify the guarantees provided (e.g., at-least-once with idempotency vs. exactly-once).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.