Research on energy efficiency optimization strategy of data center

doi:10.12267/j.issn.2096-5931.2021.04.004

Abstract

Abstract: The growth of the number and scale of data centers makes the energy consumption problem of data centers more and more prominent. Many governments have formulated corresponding policies and regulations to promote the energy saving of data centers. The energy consumption of data center is mainly composed of power supply and distribution system energy consumption, refrigeration system energy consumption and it equipment energy consumption. This paper first analyzes the energy consumption composition and energy efficiency index of data center, then analyzes the influencing factors of energy efficiency of data center from three aspects of power supply and distribution system, refrigeration system and it equipment, summarizes the current mainstream energy efficiency optimization strategies of data center, and on this basis, puts forward the data center as a This energy efficiency optimization strategy fully investigates the matching of data center service performance and energy consumption, and can effectively realize the global energy efficiency optimization of data center.

Key words: data center, energy efficiency optimization, strategy

QIAN Shengpan, QIU Ben, LI Zhe, WANG Shaopeng. Research on energy efficiency optimization strategy of data center[J]. Information and Communications Technology and Policy, 2021, 47(4): 19-26.

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http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2021.04.004

http://ictp.caict.ac.cn/EN/Y2021/V47/I4/19

References 18

[1]	华北电力大学. 点亮绿色云端———中国数据中心能耗与可再生能源使用潜力研究[EB / OL]. [2021-01-10].https:/ / www. docin. com / p-2254747295. html.
[2]	王继业, 周碧玉, 张法, 等. 数据中心能耗模型及能效算法综述[J]. 计算机研究与发展, 2019, 56(8):1587-1603.
[3]	The Green Grid. PUE 指标的综合论述[R], 2012.
[4]	丘文博. 数据中心供电系统能效分析[J]. 科技传播,2014 (13):247-248.
[5]	张成. 负载率-提高UPS 系统运行效率的关键因素[C] / / 中国电源学会现代数据中心基础设施建设技术年会. 中国电源学会, 2011.
[6]	朱永忠. 数据中心制冷技术的应用及发展[J]. 工程建设标准化, 2015(8):62-66.
[7]	邵格格, 曾兴旺, 胡传振, 等. 数据中心机房制冷技术浅析[J]. 电信快报, 2019, 574(4):34-38.
[8]	谢丽娜, 郭亮. 对液冷技术及其发展的探讨[J]. 信息通信技术与政策, 2019(2):29-32.
[9]	秦冰月, 杨欣, 闫涛, 等. 基于热环境评价指标的数据中心冷通道封闭机房气流组织研究[J]. 暖通空调,2020, 50(8):123-128.
[10]	Khalaj A H , Scherer T , Siriwardana J , et al. Multiobjective efficiency enhancement using workload spreading in an operational data center [ J ]. Applied Energy, 2015, 138(15):432-444.
[11]	Lee Y , Zomaya A . Energy efficient utilization of resources in cloud computing systems [ J]. Journal of Supercomputing, 2012, 60(2):268-280.
[12]	Ge R , Feng X , Cameron K W . Performanceconstraineddistributed DVS scheduling for scientific applications on power-aware clusters [ C ] / /Supercomputing, 2005. Proceedings of the ACM / IEEE SC 2005 Conference. ACM, 2005.
[13]	Hsu C H , Deng Q , Mars J , et al. SmoothOperator: reducing power fragmentation and improving power utilization in large-scale datacenters [C]. the Twenty- Third International Conference, 2018.
[14]	陈修敏, 张九根. 数据中心机房空调系统设计及气流优化分析[J]. 流体机械, 2014,42(11):79-82.
[15]	阮顺领, 卢才武. 虚拟化数据中心全局能耗优化调度方法[J]. 计算机工程, 2013,39(12):304-307.
[16]	傅烈虎, 曹播. 全面深入解析数据中心PUE[J]. 电气应用, 2012 (3):20-23.
[17]	梁毅, 曾绍康, 梁岩德, 等. 一种基于周期性特征的数据中心在线负载资源预测方法[J]. 计算机工程与科学, 2020,42(3):381-390.
[18]	丁麒钢. 数据中心节能建设的逻辑与方法[J]. 智能建筑, 2009(10):31-36.