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Workload management for air-cooled data centers: An energy and exergy based approach

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  • Gupta, Rohit
  • Moazamigoodarzi, Hosein
  • MirhoseiniNejad, SeyedMorteza
  • Down, Douglas G.
  • Puri, Ishwar K.

Abstract

The energy required to cool an air-cooled data center (DC) contributes significantly to the cost of operation, which is further exacerbated due to a poor choice of cooling architecture and ineffective IT workload management. Although existing algorithms reduce energy consumption, they do not minimize thermodynamic irreversibility by design. We provide a tradeoff approach that simultaneously minimizes power usage effectiveness PUE and maximizes the exergy efficiency η2nd. The temperature field is predicted inside a contained single-rack DC that is equipped with a rack-mountable cooling unit (RMCU) based on a mechanical resistance model for the fluid flow. This thermal model informs a multi-objective optimization framework based on a genetic algorithm to determine the optimal decision variables and tradeoffs for PUE and η2nd. We investigate the interrelated effects of (1) guidelines that ensure the reliability of the IT equipment, (2) overall network traffic load, (3) spatial IT load distribution, (4) changes in cooling system variables, and (5) multi-objective optimization. Results for the single rack system are presented in a scalable dimensionless form that is applicable for a multi-rack DC containing RMCUs. By considering the first and second laws of thermodynamics, this novel approach improves workload scheduling from both energy and exergy perspectives.

Suggested Citation

  • Gupta, Rohit & Moazamigoodarzi, Hosein & MirhoseiniNejad, SeyedMorteza & Down, Douglas G. & Puri, Ishwar K., 2020. "Workload management for air-cooled data centers: An energy and exergy based approach," Energy, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315930
    DOI: 10.1016/j.energy.2020.118485
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    References listed on IDEAS

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    1. Ebrahimi, Khosrow & Jones, Gerard F. & Fleischer, Amy S., 2014. "A review of data center cooling technology, operating conditions and the corresponding low-grade waste heat recovery opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 622-638.
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    7. Yan Bai & Lijun Gu, 2017. "Chip Temperature-Based Workload Allocation for Holistic Power Minimization in Air-Cooled Data Center," Energies, MDPI, vol. 10(12), pages 1-19, December.
    8. Moazamigoodarzi, Hosein & Tsai, Peiying Jennifer & Pal, Souvik & Ghosh, Suvojit & Puri, Ishwar K., 2019. "Influence of cooling architecture on data center power consumption," Energy, Elsevier, vol. 183(C), pages 525-535.
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    Cited by:

    1. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    2. Leyla Amiri & Edris Madadian & Navid Bahrani & Seyed Ali Ghoreishi-Madiseh, 2021. "Techno-Economic Analysis of Waste Heat Utilization in Data Centers: Application of Absorption Chiller Systems," Energies, MDPI, vol. 14(9), pages 1-11, April.
    3. Zhang, Yingbo & Shan, Kui & Li, Xiuming & Li, Hangxin & Wang, Shengwei, 2023. "Research and Technologies for next-generation high-temperature data centers – State-of-the-arts and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    4. Ji, Haoran & Chen, Sirui & Yu, Hao & Li, Peng & Yan, Jinyue & Song, Jieying & Wang, Chengshan, 2022. "Robust operation for minimizing power consumption of data centers with flexible substation integration," Energy, Elsevier, vol. 248(C).
    5. 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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