Reducing the Cooling Power of Data Centers by Intelligently Assigning Tasks

doi:10.1109/JIOT.2017.2783329

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Reducing the Cooling Power of Data Centers by Intelligently Assigning Tasks
	Yang, Liyao 1; Deng, Yuhui 1,3; Yang, Laurence T.2; Lin, Ruihong 1
刊名	IEEE INTERNET OF THINGS JOURNAL
	2018-06-01
卷号	5 期号:3 页码:1667-1678
关键词	Data center dynamic programming Internet of Things (IoT) routing
ISSN号	2327-4662
DOI	10.1109/JIOT.2017.2783329
英文摘要	The explosive growth of Internet of Things is generating massive data which are normally stored in data centers. The power consumption has become a very important challenge of designing modern data centers due to the explosive growth of data. The power consumed by cooling system accounts for about half of the total power consumption. Reducing the peak inlet temperature of racks residing in data centers can effectively decrease the temperature requirement of supplied cold air, thus cutting down the cooling cost. Task distribution in data centers has a significant impact on this inlet temperature. Many investigations have been conducted on achieving an optimal task distribution in terms of the air organization [e.g., genetic algorithm (GA)]. However, the existing methods can be easily trapped into a local optimum. This paper constructs a power model to correlate the task assignment, heat recirculation, inlet temperature, and cooling cost in the homogeneous and heterogeneous data centers with under-floor air supply. Furthermore, genetic simulated annealing algorithm is proposed and designed to enhance the traditional GA and assign tasks in the data centers according to the corresponding air organization by integrating the advantages of simulated annealing, thus minimizing the inlet temperature and reducing the cooling cost. Experimental results indicate that the proposed approach can effectively decrease the temperature requirement of supplied cold air and reduce the power consumption of the cooling system in contrast to the traditional GA and ant colony algorithm, especially when the data centers are with medium utilization.
资助项目	National Natural Science Foundation of China[61572232] ; National Natural Science Foundation of China[61272073] ; Science and Technology Planning Project of Nansha[2016CX007] ; Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences[CARCH201401] ; Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences[CARCH201705]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000435182100033
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/5190]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Deng, Yuhui
作者单位	1.Jinan Univ, Dept Comp Sci, Guangzhou 510632, Guangdong, Peoples R China 2.St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS B2G 2W5, Canada 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Liyao,Deng, Yuhui,Yang, Laurence T.,et al. Reducing the Cooling Power of Data Centers by Intelligently Assigning Tasks[J]. IEEE INTERNET OF THINGS JOURNAL,2018,5(3):1667-1678.
APA	Yang, Liyao,Deng, Yuhui,Yang, Laurence T.,&Lin, Ruihong.(2018).Reducing the Cooling Power of Data Centers by Intelligently Assigning Tasks.IEEE INTERNET OF THINGS JOURNAL,5(3),1667-1678.
MLA	Yang, Liyao,et al."Reducing the Cooling Power of Data Centers by Intelligently Assigning Tasks".IEEE INTERNET OF THINGS JOURNAL 5.3(2018):1667-1678.