Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach

doi:10.1109/TNSM.2022.3210204

科研成果详情

题名	Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach
作者	Lou，Jiong 1,2; Tang，Zhiqing 2; Jia，Weijia 2,3
发表日期	2023-06-01
发表期刊	IEEE Transactions on Network and Service Management
卷号	20 期号:2 页码:961-973
摘要	Energy-efficient task scheduling in data centers is a critical issue and has drawn wide attention. However, the task execution times are mixed and hard to estimate in a real-world data center. It has been conspicuously neglected by existing work that scheduling decisions made at tasks' arrival times are likely to cause energy waste or idle resources over time. To fill in such gaps, in this paper, we jointly consider assignment and migration for mixed duration tasks and devise a novel energy-efficient task scheduling algorithm. Task assignment can improve resource utilization, and migration is required when long-running tasks run in low-load servers. Specifically: 1) We formulate mixed duration task scheduling as a large-scale Markov Decision Process (MDP) problem; 2) To solve such a large-scale MDP problem, we design an efficient Deep Reinforcement Learning (DRL) algorithm to make assignment and migration decisions. To make the DRL algorithm more practical in real scenarios, multiple optimizations are proposed to achieve online training; 3) Experiments with real-world data have shown that our algorithm outperforms the existing baselines 14% on average in terms of energy consumption while keeping the same level of Quality of Service (QoS).
关键词	data center deep reinforcement learning Energy-efficient task scheduling
DOI	10.1109/TNSM.2022.3210204
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-85139431387
引用统计
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/11584
专题	北师香港浸会大学
通讯作者	Tang，Zhiqing
作者单位	1.Shanghai Jiao Tong University,Department of Computer Science and Engineering,Shanghai,200240,China 2.Institute of Artificial Intelligence and Future Networks,Beijing Normal University,Zhuhai Campus,Zhuhai,519087,China 3.BNU-HKBU United International College Zhuhai,Guangdong Key Laboratory of Ai and Multi-Modal Data Processing,Zhuhai,519087,China
推荐引用方式 GB/T 7714	Lou，Jiong,Tang，Zhiqing,Jia，Weijia. Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach[J]. IEEE Transactions on Network and Service Management, 2023, 20(2): 961-973.
APA	Lou，Jiong, Tang，Zhiqing, & Jia，Weijia. (2023). Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach. IEEE Transactions on Network and Service Management, 20(2), 961-973.
MLA	Lou，Jiong,et al."Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach". IEEE Transactions on Network and Service Management 20.2(2023): 961-973.