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Multi-objective efficiency enhancement using workload spreading in an operational data center

Author

Listed:
  • Habibi Khalaj, Ali
  • Scherer, Thomas
  • Siriwardana, Jayantha
  • Halgamuge, Saman K.

Abstract

The cooling systems of rapidly growing Data Centers (DCs) consume a considerable amount of energy, which is one of the main concerns in designing and operating DCs. The main source of thermal inefficiency in a typical air-cooled DC is hot air recirculation from outlets of servers into their inlets, causing hot spots and leading to performance reduction of the cooling system. In this study, a thermally aware workload spreading method is proposed for reducing the hot spots while the total allocated server workload is increased. The core of this methodology lies in developing an appropriate thermal DC model for the optimization process. Given the fact that utilizing a high-fidelity thermal model of a DC is highly time consuming in the optimization process, a three dimensional reduced order model of a real DC is developed in this study. This model, whose boundary conditions are determined based on measurement data of an operational DC, is developed based on the potential flow theory updated with the Rankine vortex to account for buoyancy and air recirculation effects inside the DC. Before evaluating the proposed method, this model is verified with a computational fluid dynamic (CFD) model simulated with the same boundary conditions. The efficient load spreading method is achieved by applying a multi-objective particle swarm optimization (MOPSO) algorithm whose objectives are to minimize the hot spot occurrences and to maximize the total workload allocated to servers. In this case study, by applying the proposed method, the Coefficient of Performance (COP) of the cooling system is increased by 17%, and the total allocated workload is increased by 10%. These results demonstrate the effectiveness of the proposed method for energy efficiency enhancement of DCs.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:138:y:2015:i:c:p:432-444
    DOI: 10.1016/j.apenergy.2014.10.083
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    References listed on IDEAS

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    1. Sayyaadi, Hoseyn & Mehrabipour, Reza, 2012. "Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger," Energy, Elsevier, vol. 38(1), pages 362-375.
    2. Siriwardana, Jayantha & Jayasekara, Saliya & Halgamuge, Saman K., 2013. "Potential of air-side economizers for data center cooling: A case study for key Australian cities," Applied Energy, Elsevier, vol. 104(C), pages 207-219.
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    Cited by:

    1. 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).
    2. Habibi Khalaj, Ali & Abdulla, Khalid & Halgamuge, Saman K., 2018. "Towards the stand-alone operation of data centers with free cooling and optimally sized hybrid renewable power generation and energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 451-472.
    3. Habibi Khalaj, Ali & Halgamuge, Saman K., 2017. "A Review on efficient thermal management of air- and liquid-cooled data centers: From chip to the cooling system," Applied Energy, Elsevier, vol. 205(C), pages 1165-1188.
    4. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    5. Borkowski, Mateusz & Piłat, Adam Krzysztof, 2022. "Customized data center cooling system operating at significant outdoor temperature fluctuations," Applied Energy, Elsevier, vol. 306(PB).
    6. 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.
    7. Habibi Khalaj, Ali & Scherer, Thomas & K. Halgamuge, Saman, 2016. "Energy, environmental and economical saving potential of data centers with various economizers across Australia," Applied Energy, Elsevier, vol. 183(C), pages 1528-1549.
    8. Zhang, Shanhong & Yu, Guanghui & Guo, Yu & Wang, Yang, 2023. "Modelling development and optimization on hydrodynamics and energy utilization of fish culture tank based on computational fluid dynamics and machine learning," Energy, Elsevier, vol. 276(C).
    9. 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.
    10. Wang, Fengjuan & Lv, Chengwei, 2024. "A data center expansion scheme considering net-zero carbon operation: Optimization of geographical location, on-site renewable utilization and green certificate purchase," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    11. Silva-Llanca, Luis & Ortega, Alfonso & Fouladi, Kamran & del Valle, Marcelo & Sundaralingam, Vikneshan, 2018. "Determining wasted energy in the airside of a perimeter-cooled data center via direct computation of the Exergy Destruction," Applied Energy, Elsevier, vol. 213(C), pages 235-246.
    12. Wang, Wei & Abdolrashidi, Amirali & Yu, Nanpeng & Wong, Daniel, 2019. "Frequency regulation service provision in data center with computational flexibility," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    13. Uddin, Mueen & Darabidarabkhani, Yasaman & Shah, Asadullah & Memon, Jamshed, 2015. "Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1553-1563.

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