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Influence of cooling architecture on data center power consumption

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  • Moazamigoodarzi, Hosein
  • Tsai, Peiying Jennifer
  • Pal, Souvik
  • Ghosh, Suvojit
  • Puri, Ishwar K.

Abstract

Almost thirty percent of the power consumed by data centers (DCs) is attributable to the cooling of IT equipment (ITE). There are opportunities to reduce a DC's energy budget by considering alternatives to traditional cooling methods, which experience inherent airflow deficiencies due to hot air recirculation and cold air bypass. Minimizing these two air distribution problems results in more effective cooling, but the two effects are manifest differently in the three conventional DC cooling architectures, i.e., (a) room-based, (b) row-based, and (c) rack-based cooling. Despite the intuitive logic that predicts improved cooling air distribution within row- and rack-based architectures that include shorter airflow pathlengths compared to room-based systems that have longer paths, the mechanism through which improvements translate into energy savings is not well understood. Therefore, we present methodologies that resolve the characteristic airflow and temperature distributions for three cooling architectures using computational fluid dynamics. These results inform thermodynamics models of the power consumptions that are required to cool these three architectures. The analysis reveals that row- and rack-based architectures reduce cooling power by much as 29% over a room-based architecture. Adding an enclosure within row- and rack-based architectures to separate the hot and cold airflows provides further 18% reduction in cooling power. This analysis facilitates better DC design from a cooling power consumption perspective.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:525-535
    DOI: 10.1016/j.energy.2019.06.140
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    References listed on IDEAS

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    1. Kanbur, Baris Burak & Wu, Chenlong & Fan, Simiao & Duan, Fei, 2021. "System-level experimental investigations of the direct immersion cooling data center units with thermodynamic and thermoeconomic assessments," Energy, Elsevier, vol. 217(C).
    2. Cho, Jinkyun, 2024. "Optimal supply air temperature with respect to data center operational stability and energy efficiency in a row-based cooling system under fault conditions," Energy, Elsevier, vol. 288(C).
    3. Manaserh, Yaman M. & Tradat, Mohammad I. & Bani-Hani, Dana & Alfallah, Aseel & Sammakia, Bahgat G. & Nemati, Kourosh & Seymour, Mark J., 2022. "Machine learning assisted development of IT equipment compact models for data centers energy planning," Applied Energy, Elsevier, vol. 305(C).
    4. Cho, Jinkyun & Park, Beungyong & Jang, Seungmin, 2022. "Development of an independent modular air containment system for high-density data centers: Experimental investigation of row-based cooling performance and PUE," Energy, Elsevier, vol. 258(C).
    5. 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).
    6. Kaixuan Ji & Ce Chi & Fa Zhang & Antonio Fernández Anta & Penglei Song & Avinab Marahatta & Youshi Wang & Zhiyong Liu, 2021. "Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers," Energies, MDPI, vol. 14(9), pages 1-26, April.
    7. Han, Zongwei & Ji, Qiang & Wei, Haotian & Xue, Da & Sun, Xiaoqing & Zhang, Xueping & Li, Xiuming, 2020. "Simulation study on performance of data center air-conditioning system with novel evaporative condenser," Energy, Elsevier, vol. 210(C).
    8. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Pal, Souvik & Puri, Ishwar K., 2020. "Cooling architecture selection for air-cooled Data Centers by minimizing exergy destruction," Energy, Elsevier, vol. 201(C).
    9. Moazamigoodarzi, Hosein & Gupta, Rohit & Pal, Souvik & Tsai, Peiying Jennifer & Ghosh, Suvojit & Puri, Ishwar K., 2020. "Modeling temperature distribution and power consumption in IT server enclosures with row-based cooling architectures," Applied Energy, Elsevier, vol. 261(C).
    10. Hu, Zhi-Hua & Zheng, Yu-Xin & Wang, You-Gan, 2022. "Packing computing servers into the vessel of an underwater data center considering cooling efficiency," Applied Energy, Elsevier, vol. 314(C).
    11. 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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