DDDQN‐TS: A task scheduling and load balancing method based on optimized deep reinforcement learning in heterogeneous computing environment

International Journal of Intelligent SystemsVolume 37, Issue 11 p. 9138-9172 RESEARCH ARTICLE DDDQN-TS: A task scheduling and load balancing method based on optimized deep reinforcement learning in heterogeneous computing environment Changyong Sun, Changyong Sun orcid.org/0000-0003-3620-9175 State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this authorTan Yang, Corresponding Author Tan Yang tyang@bupt.edu.cn State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China Correspondence Tan Yang, State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Room 404, Scientific Research Building, Building 10, Xitucheng Road, Haidian District, 100876 Beijing, China. Email: tyang@bupt.edu.cnSearch for more papers by this authorYouxun Lei, Youxun Lei State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this author Changyong Sun, Changyong Sun orcid.org/0000-0003-3620-9175 State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this authorTan Yang, Corresponding Author Tan Yang tyang@bupt.edu.cn State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China Correspondence Tan Yang, State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Room 404, Scientific Research Building, Building 10, Xitucheng Road, Haidian District, 100876 Beijing, China. Email: tyang@bupt.edu.cnSearch for more papers by this authorYouxun Lei, Youxun Lei State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this author First published: 08 August 2022 https://doi.org/10.1002/int.22983Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Task scheduling and load balancing problem of heterogeneous computing environment (HCE) is getting more and more attention these days and has become a research hotspot in this field. The task scheduling and load balancing problem of heterogeneous environment, which refers to assigning a set of tasks to a specific set of machines with different hardware and different computing performance with the goal of minimizing task processing time and keeping load balance among machines, has been proved to be an NP-complete problem. The development of artificial intelligence provides new ideas to solve this problem. In this paper, we propose a novel task scheduling and load balancing method based on optimized deep reinforcement learning in HCE. First, we formulate task scheduling problem as a Markov decision process and then adopt a dueling double deep Q-learning network to search the optimal task allocation solution. Then we use two well-known large-scale cluster data sets Google Cloud Jobs data set and Alibaba Cluster Trace data set to validate our approach. The experimental results show that compared with other existing solutions, our proposed method can achieve much shorter task response time and better load balancing effect. CONFLICT OF INTEREST The authors declare no conflict of interest. Open Research DATA AVAILABILITY STATEMENT The data that support the findings of this study are openly available in Google Cloud Jobs (GoCJ) Data set at https://data.mendeley.com/datasets/b7bp6xhrcd, by Zhou.21 The data that support the findings of this study are openly available in Alibaba Cluster Trace v2018 at https://github.com/Alibaba/clusterdata/tree/master/cluster-trace-v2018, by Tong et al.11 Volume37, Issue11November 2022Pages 9138-9172 RelatedInformation