• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2021, Vol. 43 ›› Issue (01): 9-16.

• 高性能计算 • 上一篇    下一篇

数据中心功耗削峰电池的可用性分析

路煜,张路,侯小凤,郑文立,李超   

  1. (上海交通大学电子信息与电气工程学院,上海 200240)

  • 收稿日期:2020-04-01 修回日期:2020-06-09 接受日期:2021-01-25 出版日期:2021-01-25 发布日期:2021-01-22

Availability analysis of datacenter peak power shaving battery

LU Yu,ZHANG Lu,HOU Xiao-feng,ZHENG Wen-li,LI Chao   

  1. (School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

  • Received:2020-04-01 Revised:2020-06-09 Accepted:2021-01-25 Online:2021-01-25 Published:2021-01-22

摘要: 研究表明,数据中心后备电池在削峰方面展现出了很大的潜力。使用电池进行削峰,可以使数据中心的电能使用效率大幅提升,从而节约大量的数据中心电力基础设施建设费用。但是,由于削峰会加速电池老化,在一个数据中心的寿命周期内往往需要更换数次电池,这使得电池费用成为数据中心成本的重要组成部分,在更为先进的分布式备电系统下电池成本所占比例更大。所以,如何更经济地使用电池成为节约成本的关键性问题。提出了一种预测电池可用性的收益模型,可以评价传统意义上的老化电池是否还具有实用价值,以及平衡老化电池使用中的性能降低和备电可靠性;并提出了一种优化的电池控制方式,实现了数据中心备电成本的降低。



关键词: 数据中心, 削峰, 服务器性能, 电池老化, 电池可用性预测

Abstract: Research shows that datacenter backup batteries have shown great potential in peak power shaving. The use of batteries for peak power shaving can greatly improve the power usage efficiency of datacenter, thereby saving a lot of construction cost of datacenter power infrastructure. However, due to the accelerated battery aging phenomenon, the battery often needs to be replaced several times during the life cycle of a data center. This makes the battery cost an important part of the cost of the data center. Under the more advanced distributed backup system, the battery cost accounts for a greater proportion. Therefore, how to use batteries more economically has become a key issue for cost saving. In this paper, a revenue model for predicting battery availability is proposed, which can evaluate whether the aging battery in the traditional sense still has practical value, and how to balance the performance degradation and backup reliability problem in the aging battery use. This paper also proposes an optimized battery control method, which reduces the cost of data center backup power.


Key words: data center, peek power shaving, server performance, battery aging, availability predication