BOOSTEDSOSA: Accelerated Inferencing for Low Variance Stochastic Online Scheduling

2026-08-26

AI Summary

针对随机在线调度中用户估计运行时间引入的高方差问题，提出BOOSTEDSOSA架构，利用机器学习预测器和时间感知训练策略减少预测误差，提高调度效率。

Abstract

Heterogeneous scheduling in stochastic, online envi- ronments, such as high-performance computing (HPC) systems, presents a significant challenge. Stochastic Online Scheduling Accelerators (SOSAs) offer a promising solution, but their effectiveness is compromised by a reliance on runtime estimates provided by users. These estimates introduce substantial vari- ance into the scheduling process (mean MAE in hundreds of Core-Days), thereby weakening the competitiveness of Stochastic Online Scheduling algorithms as their competitive-ratio bound increases with runtime variability. To address this limitation, we introduce BOOSTEDSOSA, a dual-FPGA ML-assisted Scheduling architecture that integrates a Machine Learning predictor for expected processing times, with a novel temporal-aware training policy. The predictor estimates job runtimes using only scheduler parameters available at submission time, enabling its use in existing HPC systems. Using historical real-world HPC job data (from the Argonne Leadership Comput- ing Facility, MIT Supercloud and UIUC Blue Waters workload datasets), we show that the predictor reduces MAE by up to 63.85% compared to user runtime estimates, and the additive training policy reduces MAE by up to 71.88% compared to a static model. End-to-end, BOOSTEDSOSA achieves an average 17x speedup over an AVX-optimized software baseline and processes up to 1,711 jobs/seconds

Problem

• Stochastic Online Scheduling
• HPC Systems
• Runtime Estimates
• Variance
• Competitive-Ratio

Innovation

• Dual-FPGA ML-assisted Scheduling
• Machine Learning predictor
• Temporal-aware training policy
• Stochastic Online Scheduling