Duration Aware Scheduling for ASR Serving Under Workload Drift

Darshan Makwana, Yash Jogi, Harsh Kotta, Aayush Kubba

Keywords: automatic speech recognition, inference serving, scheduling, latency optimization, vLLM, Whisper, workload drift

TL;DR: We observe that audio duration strongly predicts ASR processing time, enabling zero-overhead SJF/HRRN scheduling in vLLM that reduces median latency up to 73% while HRRN bounds tail-latency increase to 24%

Abstract: Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to head-of-line blocking under workload drift. We show that audio duration is an accurate proxy for job processing time in ASR models such as Whisper, and use this insight to enable duration-aware scheduling. We integrate two classical algorithms, Shortest Job First (SJF) and Highest Response Ratio Next (HRRN), into vLLM and evaluate them under realistic and drifted workloads. On LibriSpeech test-clean, compared to baseline, SJF reduces median E2E latency by up to $73\%$ at high load, but increases $90$th-percentile tail latency by up to $97\%$ due to starvation of long requests. HRRN addresses this trade-off: it reduces median E2E latency by up to $28\%$ while bounding tail-latency degradation to at most $24\%$. These gains persist under workload drift, with no throughput penalty and $<0.1$\,ms scheduling overhead per request.

Submission Number: 126