MRSch: Multi-Resource Scheduling for HPC
Boyang Li, Yuping Fan, Matthew T. Dearing, Zhiling Lan, Paul M. Rich, William Allcock, Michael E. Papka
Abstract
Emerging workloads in high-performance computing (HPC) are embracing significant changes, such as having diverse resource requirements instead of being CPU -centric. This advancement forces cluster schedulers to consider multiple schedulable resources during decision-making. Existing scheduling studies rely on heuristic or optimization methods, which are limited by an inability to adapt to new scenarios for ensuring long-term scheduling performance. We present an intelligent scheduling agent named MRSch for multi-resource scheduling in HPC that leverages direct future prediction (DFP), an advanced multi-objective reinforcement learning algorithm. While DFP demonstrated outstanding performance in a gaming competition, it has not been previously explored in the context of HPC scheduling. Several key techniques are developed in this study to tackle the challenges involved in multi-resource scheduling. These techniques enable MRSch to learn an appropriate scheduling pol-icy automatically and dynamically adapt its policy in response to workload changes via dynamic resource prioritizing. We compare MRSch with existing scheduling methods through extensive trace-base simulations. Our results demonstrate that MRSch improves scheduling performance by up to 48 % compared to the existing scheduling methods.