Abstract
This article advocates for the leveraging of machine learning to develop a workload manager that will improve the efficiency of modern data centres. The proposals stem from an existing tool that allows training deep reinforcement agents for this purpose. However, it incorporates several major improvements. It confers the ability to model heterogeneous data centres and then it proposes a novel learning agent that can not only choose the most adequate job for scheduling, but also determines the best compute resources for its execution. The evaluation experiments compare the performance of this learning agent against well known heuristic algorithms, revealing that the former is capable of improving the scheduling.
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Acknowledgment
This work has been supported by the Spanish Science and Technology Commission under contract PID2019-105660RB-C22 and the European HiPEAC Network of Excellence.
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Fomperosa, J., Ibañez, M., Stafford, E., Bosque, J.L. (2023). Task Scheduler for Heterogeneous Data Centres Based on Deep Reinforcement Learning. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2022. Lecture Notes in Computer Science, vol 13826. Springer, Cham. https://doi.org/10.1007/978-3-031-30442-2_18
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