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Learning-Based Cloud Server Configuration for Energy Minimization Under Reliability Constraint

文献类型：期刊论文

作者Cong, Peijin3; Zhou, Junlong1,3; Wang, Jiali3; Wu, Zebin3; Hu, Shiyan2
刊名IEEE TRANSACTIONS ON RELIABILITY
出版日期2024-03-01
卷号73期号:1页码:203-215
关键词Servers Cloud computing Reliability Energy consumption Quality of service Transient analysis Task analysis Cloud service energy efficiency multiserver reinforcement learning reliability
ISSN号0018-9529
DOI10.1109/TR.2023.3234036
英文摘要Cloud computing has attracted wide attention from both academia and industry, since it can provide flexible and on-demand hardware and software resources as services. Energy consumption of cloud servers is the main concern of cloud service providers since reducing energy consumption can bring them a lower operation cost (and hence a higher profit) and alleviate carbon footprints to the environment. Typically, the common power management techniques for enhancing energy efficiency would make cloud servers more vulnerable to soft errors and hence adversely impact the quality of services. Thus, reliability cannot be ignored in the design of methodologies for improving the energy efficiency of cloud servers. In this article, we aim to minimize the energy consumption of cloud servers under the soft-error reliability constraint by configuring the size and speed of servers. Specifically, we first derive the expected reliability based energy consumption of cloud servers to formulate the reliability-constrained energy minimization problem. We then leverage the reinforcement learning technique to obtain an optimal server configuration solution that maximizes system energy efficiency while maintaining the system reliability constraint. Finally, we perform extensive simulation experiments to analyze the relationship between system energy consumption and server configuration under varying arrival rates and execution requirements of service requests. Comparative experiments are also performed to validate the efficacy of the proposed learning-based server configuration scheme. Results show that compared to a benchmark method, the energy saved by the proposed scheme can reach up to 31.5%.
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001181551400074
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/38960]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhou, Junlong
作者单位1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100045, Peoples R China
2.Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, England
3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
推荐引用方式
GB/T 7714		 Cong, Peijin,Zhou, Junlong,Wang, Jiali,et al. Learning-Based Cloud Server Configuration for Energy Minimization Under Reliability Constraint[J]. IEEE TRANSACTIONS ON RELIABILITY,2024,73(1):203-215.
APA Cong, Peijin,Zhou, Junlong,Wang, Jiali,Wu, Zebin,&Hu, Shiyan.(2024).Learning-Based Cloud Server Configuration for Energy Minimization Under Reliability Constraint.IEEE TRANSACTIONS ON RELIABILITY,73(1),203-215.
MLA Cong, Peijin,et al."Learning-Based Cloud Server Configuration for Energy Minimization Under Reliability Constraint".IEEE TRANSACTIONS ON RELIABILITY 73.1(2024):203-215.

入库方式： OAI收割

来源：计算技术研究所

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