Title: Cloud workflow scheduling algorithm based on reinforcement learning

Authors: Delong Cui; Zhiping Peng; Wende Ke; Xiaoyu Hong; Jinglong Zuo

Addresses: College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming, 525000, China ' College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming, 525000, China ' College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming, 525000, China ' School of Computer, Dongguan University of Technology, Dongguan, 523000, China ' College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming, 525000, China

Abstract: How to fairly schedule the multiple workflow with multiple priorities submitted at different times has become an increasing concern in workflow management system (WMS). To solve the problem, a novel workflow scheduling algorithm based on reinforcement learning is proposed in this study. In our scheme, we first define some basic concepts of reinforcement learning in cloud computing, such as state space, action space and immediate reward. Then single DAG and multiple DAG cloud workflow scheduling algorithm based on reinforcement learning are designed respectively. Reinforcement learning sets up a policy to maximise the cumulative rewards in the long-term through the repetition of trial-and-error interactions in cloud computing environment. Finally, we analyse algorithm performance by using queuing theory. We use real cloud workflow to test the proposed scheme. Our results, on the one hand, demonstrate the proposed scheme can reasonably schedule multiple DAGs with multiple priorities and improve utilisation rate of resources better and, on the other hand, show optimisation object function achieves fair workflow scheduling in cloud computing environment.

Keywords: multiple DAGs; reinforcement learning; workflow scheduling; cloud computing.

DOI: 10.1504/IJHPCN.2018.091889

International Journal of High Performance Computing and Networking, 2018 Vol.11 No.3, pp.181 - 190

Available online: 10 May 2018 *

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