Reinforcement learning for green and reliable data center

Qing-Shan JIA; Jingxian TANG; Junjie WU; Xiao HU; Yiting LIN; Heng XIA

doi:10.11959/j.issn.2096-6652.202036

您当前的位置：

首页 >

文章列表页 >

Reinforcement learning for green and reliable data center

Special Issue: Deep Reinforcement Learning | 更新时间：2024-06-05

- Reinforcement learning for green and reliable data center
- Chinese Journal of Intelligent Science and Technology Vol. 2, Issue 4, Pages: 341-347(2020)
- 作者机构：
  
  1. 清华大学自动化系智能与网络化系统研究中心，北京 100084
  2. 北京化工大学信息科学与技术学院，北京 100029
  3. 腾讯IDC平台部，广东深圳 518052
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202036
  CLC： TP29
- Published Online：2020-12，
  
  Published：15 December 2020
- 稿件说明：
移动端阅览
Qing-Shan JIA, Jingxian TANG, Junjie WU, et al. Reinforcement learning for green and reliable data center[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 341-347.
DOI：

Qing-Shan JIA, Jingxian TANG, Junjie WU, et al. Reinforcement learning for green and reliable data center[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 341-347. DOI： 10.11959/j.issn.2096-6652.202036.

摘要

数据中心的绿色可靠运行具有重大的社会经济价值。综述了面向数据中心绿色可靠运行的优化与控制方法，提出一种事件驱动的强化学习方法，用于提升运行能效；提出一种电池寿命预测方法，提升了预测精度。

Abstract

It is of significant social and economical impact to achieve green and reliable operation of data center.The optimization and control methods for green and reliable data center were reviewed briefly.An event-based reinforcement learning approach for improving the energy efficiency was developed.And a method to improve the accuracy of battery lifetime forecasting was developed.

关键词

Keywords

references

NICOLA J . How to stop data centres from gobbling up the world’s electricity [J ] . Nature , 2018 , 561 ( 7722 ): 163 - 166 .

ISO/IEC . Information technology - data ceners - key performance indicators - part 2:power usage effectiveness:30134-2:2016 [S ] . 2016 .

BULLARD N . Energy efficiency is a hot problem for big tech’s data centers [Z ] . 2019 .

SHEHABI A , SMITH S , SARTOR D , et al . United states data center energy usage report [R ] . 2016 .

张泉 , 李震 . 数据中心节能技术与应用 [M ] . 北京 : 机械工业出版社 , 2018 .

ZHANG Q , LI Z . Energy saving technology for data center and applicaitons [M ] . Beijing : China Machine Press , 2018 .

腾讯数据中心 . 致敬经典——腾讯第三代数据中心 [Z ] . 2020 .

Tencent Data Centers . To classic - three generations of Tencent data centers [Z ] . 2020 .

LI Y L , WEN Y G , GUAN K L , et al . Transforming cooling optimization for green data center via deep reinforcement learning [J ] . IEEE Transactions on Cybernetics , 2019 , 50 ( 5 ): 2002 - 2013 .

RAN Y Y , HU H , ZHOU X , et al . DeepEE:joint optimization of job scheduling and cooling control for data center energy efficiency using deep reinforcement learning [C ] // IEEE 39th International Conference on Distributed Computing Systems . Piscataway:IEEE Press , 2019 : 645 - 655 .

YI D L , ZHOU X , WEN Y G , et al . Toward efficient compute-intensive job allocation for green data centers:a deep reinforcement learning approach [C ] // IEEE 39th International Conference on Distributed Computing Systems . Piscataway:IEEE Press , 2019 : 634 - 644 .

PE/ESSB-Energy Storage ＆ Stationary Battery Committee . IEEE 1188-2005-IEEE recommended practice for maintenance,testing,and replacement of valve-regulated lead-acid (VRLA) batteries for stationary applications [S ] . 2006 .

贾庆山 , 杨玉 , 夏俐 , 等 . 基于事件的优化方法简介及其在能源互联网中的应用 [J ] . 控制理论与应用 , 2018 , 35 ( 1 ): 32 - 40 .

JIA Q S , YANG Y , XIA L , et al . A tutorial on event-based optimization with application in energy Internet [J ] . Control Theory and Applications , 2018 , 35 ( 1 ): 32 - 40 .

JIA Q S , WU J J , WU Z J , et al . Event-based HVAC control-a complexity-based approach [J ] . IEEE Transactions on Automation Science and Engineering , 2018 , 15 ( 4 ): 1909 - 1919 .

NEWMAN R H . Advantages and disadvantages of valve-regulated,lead/acid batteries [J ] . Journal of Power Sources , 1994 , 52 ( 1 ): 149 - 153 .

TANG J X , DU J H , LIN Y T , et al . Predictive maintenance of VRLA batteries in UPS towards reliable data centers [C ] // IFAC World Congress .[S.l.:s.n. ] , 2020 .

夏元清 , 高润泽 , 林敏 , 等 . 绿色能源互补智能电厂云控制系统研究 [J ] . 自动化学报 , 2020 , 46 ( 9 ): 1844 - 1868 .

XIA Y Q , GAO R Z , LIN M , et al . Green energy complementary based on intelligent power plant cloud control system [J ] . Acta Automatica Sinica , 2020 , 46 ( 9 ): 1844 - 1868 .

王飞跃 , 曹东璞 , 魏庆来 . 强化学习:迈向知行合一的智能机制与算法 [J ] . 智能科学与技术学报 , 2020 , 2 ( 2 ): 101 - 106 .

WANG F Y , CAO D P , WEI Q L . Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms [J ] . Chinese Journal of Intelligent Science and Technology , 2020 , 2 ( 2 ): 101 - 106 .

Views

270

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A path planning method for complex naval battle field based on an improved DQN algorithm

Research development on automated robotic peg-in-hole assembly

A cooperative multi-agent reinforcement learning algorithm based on dynamic self-selection parameters sharing

Output synchronization of heterogeneous multi-agent system:a reinforcement learning approach based on data

An overview of optimal consensus for data driven multi-agent system based on reinforcement learning

Related Author

Haitao YUAN

Jing BI

Zhou YU

Fangbo QIN

De XU

Yuan JIANG

Yang YU

Han WANG

Related Institution

Faculty of Information Technology, Beijing University of Technology

School of Automation Science and Electrical Engineering, Beihang University

The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences

University of Chinese Academy of Sciences

Qingdao Academy of Intelligent Industries

Postal code：100079
Tel：（010）53879206 Email：cjist@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：35685 Visits Today：160

⁰