A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling
Joint Authors
Che, Haiying
Bai, Zixing
Zuo, Rong
Li, Honglei
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-31
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
With more businesses are running online, the scale of data centers is increasing dramatically.
The task-scheduling operation with traditional heuristic algorithms is facing the challenges of uncertainty and complexity of the data center environment.
It is urgent to use new technology to optimize the task scheduling to ensure the efficient task execution.
This study aimed at building a new scheduling model with deep reinforcement learning algorithm, which integrated the task scheduling with resource-utilization optimization.
The proposed scheduling model was trained, tested, and compared with classical scheduling algorithms on real data center datasets in experiments to show the effectiveness and efficiency.
The experiment report showed that the proposed algorithm worked better than the compared classical algorithms in the key performance metrics: average delay time of tasks, task distribution in different delay time levels, and task congestion degree.
American Psychological Association (APA)
Che, Haiying& Bai, Zixing& Zuo, Rong& Li, Honglei. 2020. A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling. Complexity،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1141246
Modern Language Association (MLA)
Che, Haiying…[et al.]. A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling. Complexity No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1141246
American Medical Association (AMA)
Che, Haiying& Bai, Zixing& Zuo, Rong& Li, Honglei. A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling. Complexity. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1141246
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141246