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Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center

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  • Yan Bai

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Lijun Gu

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Xiao Qi

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Improving the energy efficiency of data center has become a research focus in recent years. Previous works commonly adopted the inlet temperature constraint to optimize the thermal environment in the data center. However, the inlet temperature-aware method cannot prevent the servers from over-cooling. To cope with this issue, we propose a thermal-aware workload allocation strategy with respect to the chip temperature constraint. In this paper, we conducted a comparative evaluation of the performance between the chip and inlet temperature-aware workload allocation strategies. The workload allocation strategies adopt a POD-based heat recirculation model to characterize the thermal environment in data center. The contribution of the temperature-dependent leakage power to server power consumption is also considered. We adopted a sample data center under constant-flow and variable-flow cooling air supply to evaluate the performance of these two different workload allocation strategies. The comparison results show that the chip temperature-aware workload allocation strategy prevents the servers from over-cooling and significantly improves the energy efficiency of data center, especially for the case of variable-flow cooling air supply.

Suggested Citation

  • Yan Bai & Lijun Gu & Xiao Qi, 2018. "Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center," Energies, MDPI, vol. 11(3), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:669-:d:136543
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    References listed on IDEAS

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    1. Habibi Khalaj, Ali & Scherer, Thomas & Siriwardana, Jayantha & Halgamuge, Saman K., 2015. "Multi-objective efficiency enhancement using workload spreading in an operational data center," Applied Energy, Elsevier, vol. 138(C), pages 432-444.
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    Cited by:

    1. Yeliang Qiu & Congfeng Jiang & Yumei Wang & Dongyang Ou & Youhuizi Li & Jian Wan, 2019. "Energy Aware Virtual Machine Scheduling in Data Centers," Energies, MDPI, vol. 12(4), pages 1-21, February.
    2. Jin, Chaoqiang & Bai, Xuelian & Yang, Chao & Mao, Wangxin & Xu, Xin, 2020. "A review of power consumption models of servers in data centers," Applied Energy, Elsevier, vol. 265(C).
    3. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Down, Douglas G. & Puri, Ishwar K., 2021. "Energy, exergy and computing efficiency based data center workload and cooling management," Applied Energy, Elsevier, vol. 299(C).

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