Energy Saving with Zero Hot Spots: A Novel Power Control Approach for Sustainable and Stable Data Centers

Danyang Li, Yuqi Zhang, Jie Song, Hui Liu and Jingqing Jiang
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Danyang Li: Software College, Northeastern University, Shenyang 112000, China
Yuqi Zhang: Software College, Northeastern University, Shenyang 112000, China
Jie Song: Software College, Northeastern University, Shenyang 112000, China
Hui Liu: School of Metallurgy, Northeastern University, Shenyang 112000, China
Jingqing Jiang: College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao 028000, China

Sustainability, 2022, vol. 14, issue 15, 1-17

Abstract: Data centers with high energy consumption have become a threat to urban sustainability on electric energy. In contrast, hot spots in a data center are another threat to server stability, which leads to unsafe data storage and service provisioning to urban lives. However, state-of-the-art works cannot ensure sustainability and stability together because they fail to consider them holistically. For example, some existing works eliminate the hot spots by increasing cooling power, which results in lower sustainability. In contrast, others reduce energy consumption by saving the cooling power, which harms stability. Therefore, to balance the hot spot elimination and energy saving through power control remains challenging, this paper proposes a novel power control approach for energy saving with zero hot spots in data centers. Power control works when hot spots appear, or consumed energy is excess. Specifically, we formulated a total consumption minimization problem to characterize and analyze the optimal set points for power control, where the number of hot spots is zero and the energy consumption is low. Adding the interactional penalty models can determine the power control approach when the objective function obtains the optimal solution. We propose a Modified Differential Evolution algorithm (MDE) to solve the function quickly and accurately. It adopts adaptive parameters to reduce the computing time. Meanwhile, it avoids optimal local solutions by changing mutation operations. Further, simulation experiments using our optimal solution demonstrate that energy consumption saves about 13% on average with zero hot spots, compared with three typical approaches.

Keywords: data center; hot spot elimination; energy consumption; differential evolution algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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