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Article

RLSchert: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction

1
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
2
Supercomputing Center, University of Science and Technology of China, Hefei 230026, China
*
Author to whom correspondence should be addressed.
Academic Editors: Antonio J. Pena and Pedro Valero-Lara
Appl. Sci. 2021, 11(20), 9448; https://doi.org/10.3390/app11209448
Received: 9 September 2021 / Revised: 1 October 2021 / Accepted: 7 October 2021 / Published: 12 October 2021
(This article belongs to the Special Issue State-of-the-Art High-Performance Computing and Networking)
The job scheduler plays a vital role in high-performance computing platforms. It determines the execution order of the jobs and the allocation of resources, which in turn affect the resource utilization of the entire system. As the scale and complexity of HPC continue to grow, job scheduling is becoming increasingly important and difficult. Existing studies relied on user-specified or regression techniques to give fixed runtime prediction values and used the values in static heuristic scheduling algorithms. However, these approaches require very accurate runtime predictions to produce better results, and fixed heuristic scheduling strategies cannot adapt to changes in the workload. In this work, we propose RLSchert, a job scheduler based on deep reinforcement learning and remaining runtime prediction. Firstly, RLSchert estimates the state of the system by using a dynamic job remaining runtime predictor, thereby providing an accurate spatiotemporal view of the cluster status. Secondly, RLSchert learns the optimal policy to select or kill jobs according to the status through imitation learning and the proximal policy optimization algorithm. Extensive experiments on real-world job logs at the USTC Supercomputing Center showed that RLSchert is superior to static heuristic policies and outperforms the learning-based scheduler DeepRM. In addition, the dynamic predictor gives a more accurate remaining runtime prediction result, which is essential for most learning-based schedulers. View Full-Text
Keywords: high-performance computing; RLSchert; scheduling; deep reinforcement learning; remaining runtime prediction high-performance computing; RLSchert; scheduling; deep reinforcement learning; remaining runtime prediction
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MDPI and ACS Style

Wang, Q.; Zhang, H.; Qu, C.; Shen, Y.; Liu, X.; Li, J. RLSchert: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction. Appl. Sci. 2021, 11, 9448. https://doi.org/10.3390/app11209448

AMA Style

Wang Q, Zhang H, Qu C, Shen Y, Liu X, Li J. RLSchert: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction. Applied Sciences. 2021; 11(20):9448. https://doi.org/10.3390/app11209448

Chicago/Turabian Style

Wang, Qiqi, Hongjie Zhang, Cheng Qu, Yu Shen, Xiaohui Liu, and Jing Li. 2021. "RLSchert: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction" Applied Sciences 11, no. 20: 9448. https://doi.org/10.3390/app11209448

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