Abstract
Job scheduling is a key function of cluster computing. Efficient job scheduling can improve hardware resource utilization and promote the execution efficiency of jobs. Conventional scheduling work is dominated by heuristic algorithms. The scheduling efficiency of the heuristic algorithm is not optimal. In this paper, we improved the deep reinforcement learning algorithm for the cluster scheduling, which named DeepCM. Test results on the simulation data shows that the DeepCM is capable of improving the performance for job scheduling on the cluster. The slowdown could be improved from 2.248 to 2.235 in a environment of 3 machines. The fusion of internal baseline and external baseline could reduce the variations of the performance on different jobsets. The experimental results demonstrate that the deep reinforcement learning get improved scheduling efficiency in cluster computing. The performance advantage is more obvious when the load gets heavier.
Supported by National key R & D program high performance computing project (2017YFB0203501).
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Yao, Z., Chen, L., Zhang, H. (2021). Deep Reinforcement Learning for Job Scheduling on Cluster. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12894. Springer, Cham. https://doi.org/10.1007/978-3-030-86380-7_50
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