Learning outcomes
- Explain the operating model behind Continuous Batching and Admission Control.
- Evaluate trade-offs and failure modes for Continuous Batching and Admission Control.
- Apply Continuous Batching and Admission Control to a production decision.
Mental model
Continuous batching chooses work at each engine step; admission control limits what may enter before overload destroys every request's objective.
Learning outcomes
- Explain the mechanism and ownership boundaries behind Continuous Batching and Admission Control.
- Compare the main design alternatives and their operational trade-offs.
- Diagnose common failures and select evidence for a production decision.
Theory
Separate queued, admitted, running, and preempted state. Budget tokens and cache, prioritize classes explicitly, cap per-tenant concurrency, reject early when deadlines cannot be met, and observe starvation.
Trade-offs
Larger dynamic batches raise throughput but may delay short interactive requests. Aggressive admission maximizes offered load while creating unbounded queues and timeout waste.
Failure modes and misconceptions
No queue deadline; first-come fairness across unlike workloads; admitting beyond cache capacity; retry storms; hidden preemption; and reporting rejected work as availability failure.
Decision scenario
Interactive chat and batch summarization share one engine. Design admission classes and scheduling policies that prevent either workload from starving the other.
How does admission control differ from the scheduler that forms an execution batch?
Primary sources
vllm-v0102-schedulersre-book
Evidence assessment
Theory and decision mastery
Decision scenario
A production team must adopt Continuous Batching and Admission Control while meeting quality, latency, security, and operating constraints.
Which decision process is most defensible?
Relationships
Continuous Batching and Admission Control builds on Inference Engine Architecture.
Continuous Batching and Admission Control informs governed production decisions and review evidence.
Primary sources
- vLLM V1 scheduler at v0.10.2 — vLLM Project, verified 2026-07-21
- Site Reliability Engineering — Google, verified 2026-07-21