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A review of power consumption models of servers in data centers

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  • Jin, Chaoqiang
  • Bai, Xuelian
  • Yang, Chao
  • Mao, Wangxin
  • Xu, Xin

Abstract

This study provides an overview of power consumption models of servers in data centers. The server is the basic unit of both power and heat flow paths; therefore, its power consumption model can be used for both energy management and thermal management. Investigations of server power trends were carried out according to the data from the Standard Performance Evaluation Corporation (SPEC). It is found that a heavier workload can be handled without consuming more energy, and the difference between the peak power and idle power of the servers is not consistent from generation to generation. Furthermore, the existing power consumption models are categorized as additive models, baseline power + active power (BA) models, and other models based on calculation formula and other factors. Specifically, there are four forms of BA models: linear regression models, power function models, non-linear models and polynomial models. Besides, these models have been compared in terms of accuracy. It can be found that the polynomial model and the linear regression model perform better in terms of accuracy. Additionally, the model applications are summarized. Considering server architecture upgrades and technological innovation, the establishment of the new model and its application scenarios are discussed. Moreover, in-depth and accurate power consumption models must be extensively researched and applied to effectively improve data centers, including information technology (IT) equipment and cooling equipment, in terms of overall energy performance.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:265:y:2020:i:c:s0306261920303184
    DOI: 10.1016/j.apenergy.2020.114806
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    Cited by:

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    6. Chen, Xiaoyuan & Zhang, Mingshun & Jiang, Shan & Gou, Huayu & Zhou, Pang & Yang, Ruohuan & Shen, Boyang, 2023. "Energy reliability enhancement of a data center/wind hybrid DC network using superconducting magnetic energy storage," Energy, Elsevier, vol. 263(PA).
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    9. Mehmet Türker Takcı & Tuba Gözel, 2022. "Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    10. Xiao, Jiang-Wen & Yang, Yan-Bing & Cui, Shichang & Wang, Yan-Wu, 2023. "Cooperative online schedule of interconnected data center microgrids with shared energy storage," Energy, Elsevier, vol. 285(C).
    11. Jin, Chaoqiang & Bai, Xuelian & Zhang, Xin & Xu, Xin & Tang, Yu & Zeng, Chao, 2022. "A measurement-based power consumption model of a server by considering inlet air temperature," Energy, Elsevier, vol. 261(PA).
    12. He, Wei & Ding, Su & Zhang, Jifang & Pei, Chenchen & Zhang, Zhiheng & Wang, Yulin & Li, Hailong, 2021. "Performance optimization of server water cooling system based on minimum energy consumption analysis," Applied Energy, Elsevier, vol. 303(C).
    13. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Zou, Zhice & Shen, Boyang & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu, 2022. "Energy-saving superconducting power delivery from renewable energy source to a 100-MW-class data center," Applied Energy, Elsevier, vol. 310(C).
    14. Mahbod, Muhammad Haiqal Bin & Chng, Chin Boon & Lee, Poh Seng & Chui, Chee Kong, 2022. "Energy saving evaluation of an energy efficient data center using a model-free reinforcement learning approach," Applied Energy, Elsevier, vol. 322(C).
    15. 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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