| Title | Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach |
| Creator | |
| Date Issued | 2023-06-01 |
| Source Publication | IEEE Transactions on Network and Service Management
 |
| Volume | 20Issue:2Pages:961-973 |
| Abstract | 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). |
| Keyword | data center deep reinforcement learning Energy-efficient task scheduling |
| DOI | 10.1109/TNSM.2022.3210204 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85139431387 |
| Citation statistics | |
| Document Type | Journal article |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/11584 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author | Tang，Zhiqing |
| Affiliation | 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 |
| Recommended Citation 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. |
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