基于可再生能源消纳的数据中心负荷调度研究综述

计算机工程与科学 ›› 2023, Vol. 45 ›› Issue (5): 790-801.

基于可再生能源消纳的数据中心负荷调度研究综述

陈冬林1,2,马一帆1,2，邹安琪1,2

(1.武汉理工大学经济学院，湖北武汉 430070；2.湖北省电子商务大数据工程技术研究中心，湖北武汉 430070)

收稿日期:2022-04-28 修回日期:2022-09-20 出版日期:2023-05-25 发布日期:2023-05-16
基金资助:
国家自然科学基金（72271192）；国家重点研发计划（2018YFB1404303）；武汉理工大学研究生自主创新研究基金（225215004）

A survey of data center load scheduling based on renewable energy consumption

CHEN Dong-lin1,2,MA Yi-fan1,2，ZOU An-qi1,2

(1.School of Economics,Wuhan University of Technology,Wuhan 430070;
2.Hubei Provincial Research Center for Electronic-Business Big Data Engineering Technology,Wuhan 430070，China）

Received:2022-04-28 Revised:2022-09-20 Online:2023-05-25 Published:2023-05-16

摘要/Abstract

摘要： 在“碳达峰碳中和”的社会环境和“碳成本”的经济环境下，可再生能源的消纳成为数据中心运营商负荷调度决策的重要影响因素。分析了可再生能源消纳与数据中心负荷调度协同的价值潜力、模式及挑战；介绍了数据中心负荷调度优化目标和算法；分别从负荷空间转移、负荷时间转移、负荷时空转移3个顶层设计方面对基于可再生能源消纳的数据中心负荷调度策略进行分析、总结和对比。

关键词: 数据中心, 负荷调度, 可再生能源消纳, 优化

Abstract: In the social environment of “carbon peak and carbon neutralization” and the economic environment of “carbon cost”, renewable energy with the attribute of zero carbon + low cost has become an important decision-making factor for data center operators to carry out multi-objective management through load scheduling under various internal and external constraints. This paper analyzes the potential and mode of cooperation between renewable energy and data center operation and management, and introduces the data center load scheduling optimization method based on renewable energy consumption. Finally, this paper analyzes, summarizes and compares the data center load scheduling methods based on renewable energy consumption from three aspects: space-based scheduling optimization, time-based scheduling optimization and space-time scheduling optimization.

Key words: data centers, workload migration, renewable energy consumption, optimization

陈冬林, 马一帆, 邹安琪, . 基于可再生能源消纳的数据中心负荷调度研究综述[J]. 计算机工程与科学, 2023, 45(5): 790-801.

CHEN Dong-lin, MA Yi-fan, ZOU An-qi, . A survey of data center load scheduling based on renewable energy consumption[J]. Computer Engineering & Science, 2023, 45(5): 790-801.