Duration Aware Scheduling for ASR Serving Under Workload Drift
This work addresses latency issues in large-scale ASR serving pipelines for users under realistic and drifted workloads, presenting an incremental improvement over existing scheduling methods.
The paper tackles the problem of high latency in ASR serving due to FCFS scheduling under workload drift by proposing duration-aware scheduling using SJF and HRRN, resulting in up to 73% reduction in median latency with SJF and up to 28% reduction with HRRN while bounding tail latency degradation.
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.