GARLSched: Generative adversarial deep reinforcement learning task scheduling optimization for large-scale high performance computing systems

Efficient task scheduling has become increasingly complex as the number and type of tasks proliferate and the size of computing resource grows in large-scale distributed high-performance computing (HPC) systems. At present, deep reinforcement learning (DRL) methods have achieved certain success in s...

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Veröffentlicht in:	Future generation computer systems Jg. 135; S. 259 - 269
Hauptverfasser:	Li, Jingbo, Zhang, Xingjun, Wei, Jia, Ji, Zeyu, Wei, Zheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2022
Schlagworte:	Deep reinforcement learning Distributed systems Expert guidance High performance computing Task scheduling Task scheduling Deep reinforcement learning Expert guidance High performance computing Distributed systems
ISSN:	0167-739X, 1872-7115
Online-Zugang:	Volltext
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