Coordinated Operation for Data Center and Power System in the Context of Energy Internet (I): Energy Demand Management Model of Data Center

被引:0
|
作者
Ding Z. [1 ]
Cao Y. [1 ]
Zhang S. [1 ]
Wang P. [1 ]
Liu J. [1 ]
Cheng M. [2 ]
Mao H. [2 ]
机构
[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Changping District, Beijing
[2] Alibaba Group., Hangzhou, 310000, Zhejiang Province
基金
中国国家自然科学基金;
关键词
Data center; Energy internet; Energy power system; Power consumption; Spatio-temporal flexibility;
D O I
10.13334/j.0258-8013.pcsee.210813
中图分类号
学科分类号
摘要
With the development of cloud computing technology and the promotion of the new infrastructure strategy, data centers are acting as the linkage between digital & information networks and traditional industries. In the context of energy Internet, exploring internal operation flexibility of data centers is an important research field to accomplish the deep integration of data network and energy network, and thus boost system flexibility. On the basis of relevant literature hitherto, the potential and significance of collaborative optimization between data center and power system were analyzed in detail in this series of articles. In this first paper, the basic characteristics and spatio-temporal scheduling flexibility of two types of workloads in data center were analyzed based on the actual operation situation. The data center energy consumption models explored in existing literature were classified into two types. On this basis, the potential flexibility, including temporal flexibility, spatial flexibility and multi-energy conversion flexibility of data centers, was further analyzed. In addition, the trend of energy management in data centers was inferred and explained considering relevant policies at present. © 2022 Chin. Soc. for Elec. Eng.
引用
收藏
页码:3161 / 3176
页数:15
相关论文
共 105 条
  • [1] WANG Jiajun, LU Zhihui, WU Jie, Et al., Cloud computing technology development analysis and applications discussion, Computer Engineering and Design, 31, 20, pp. 4404-4409, (2010)
  • [2] CI Song, LIU Qianwei, KANG Chongqing, Et al., Fundamental exploration into ICT-energy fusion, Proceedings of the CSEE, 41, 7, pp. 2289-2296, (2021)
  • [3] LI Chao, Fuxi 2.0:the core scheduling system of Alibaba facing challenges of big data and cloud computing
  • [4] Disaster recovery and your data center's redundancy [EB/OL]
  • [5] JIA Xiaojing, Google cloud computing platform technology architecture and the impact of its cost[C], 2010 Second World Congress on Software Engineering, pp. 17-20
  • [6] ALZAIN M A, SOH B, PARDEDE E., A new approach using redundancy technique to improve security in cloud computing[C], Proceedings Title:2012 International Conference on Cyber Security, Cyber Warfare and Digital Forensic(CyberSec), pp. 230-235
  • [7] Data resiliency in Microsoft 365
  • [8] ZHANG Zhuo, LI Chao, TAO Yangyu, Et al., Fuxi:a fault-tolerant resource management and job scheduling system at internet scale, Proceedings of the VLDB Endowment.40th International Conference on Very Large Data Bases, pp. 1393-1404, (2014)
  • [9] DONG Ziqian, LIU Ning, ROJAS-CESSA R., Greedy scheduling of tasks with time constraints for energy- efficient cloud-computing data centers, Journal of Cloud Computing, 4, 1, (2015)
  • [10] ADNAN M A, SUGIHARA R, GUPTA R K., Energy efficient geographical load balancing via dynamic deferral of workload, 2012 IEEE Fifth International Conference on Cloud Computing, pp. 188-195, (2012)