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

• URL: http://arxiv.org/abs/2105.07526v1
• Date: Sun, 16 May 2021 21:56:31 GMT
• Title: DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling
• Authors: Yuping Fan and Zhiling Lan
• Abstract summary: We present a reinforcement learning based HPC scheduling framework named DRAS-CQSim to automatically learn optimal scheduling policy. DRAS-CQSim encapsulates simulation environments, agents, hyper parameter tuning options, and different reinforcement learning algorithms, which allows the system administrators to quickly obtain customized scheduling policies.
• Score: 0.9529163786034884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For decades, system administrators have been striving to design and tune cluster scheduling policies to improve the performance of high performance computing (HPC) systems. However, the increasingly complex HPC systems combined with highly diverse workloads make such manual process challenging, time-consuming, and error-prone. We present a reinforcement learning based HPC scheduling framework named DRAS-CQSim to automatically learn optimal scheduling policy. DRAS-CQSim encapsulates simulation environments, agents, hyperparameter tuning options, and different reinforcement learning algorithms, which allows the system administrators to quickly obtain customized scheduling policies.

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