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For decades, system administrators have been striving to design and tunecluster scheduling policies to improve the performance of high performancecomputing (HPC) systems. However, the increasingly complex HPC systems combinedwith highly diverse workloads make such manual process challenging,time-consuming, and error-prone. We present a reinforcement learning based HPCscheduling framework named DRAS-CQSim to automatically learn optimal schedulingpolicy. DRAS-CQSim encapsulates simulation environments, agents, hyperparametertuning options, and different reinforcement learning algorithms, which allowsthe system administrators to quickly obtain customized scheduling policies.

DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling