期刊
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
卷 10, 期 2, 页码 402-415出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTSP.2015.2500189
关键词
Cooling-aware; cost minimization; data center; distributed storage; renewable generation; stochastic optimization
资金
- U.S. National Science Foundation [1509040, 1508993, 1509005, 1423316, 1442686, 1202135]
- China Recruitment Program of Global Young Experts
- Program for New Century Excellent Talents in University
- Innovation Program of Shanghai Municipal Education Commission
- National Science and Technology Major Project of the Ministry of Science and Technology of China [2012ZX03001013]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1423316] Funding Source: National Science Foundation
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1508993, 1509005] Funding Source: National Science Foundation
While the quest of end users for fast and convenient Internet services grows steadily, energy-hungry data centers correspondingly expand in both numbers and scale - a fact that raises global warming and climate change concerns. In addition, high penetration of renewables, development of energy-efficient cooling facilities, and flexibility of distributed storage units, all call for a system-wide energy and workload management policy for future sustainable data centers. As implementing offline management policies is practically infeasible due to complexity and the lack of future information, real-time management schemes are considered here under a systematic framework. Leveraging stochastic optimization tools, a unified management approach is proposed allowing data centers to adaptively respond to intermittent availability of renewables, variability of cooling efficiency, information technology (IT) workload shift, and energy price fluctuations under long-term quality-of-service (QoS) requirements. Meanwhile, it is rigorously established that when storage devices have sufficiently high capacity, or, the difference between electricity purchase and selling prices is small, the proposed algorithm yields a feasible and near-optimal management strategy without knowing the distributions of the independently and identically distributed (i.i.d.) workload, renewable, and electricity price processes. Numerical results further demonstrate that the proposed algorithm works well not only for i.i.d. processes, but also in real-data scenarios, where the underlying randomness is highly correlated over time.
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