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Chip Temperature-Based Workload Allocation for Holistic Power Minimization in Air-Cooled Data Center

Author

Listed:
  • 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)

Abstract

Minimizing the energy consumption is a dominant problem in data center design and operation. To cope with this issue, the common approach is to optimize the data center layout and the workload distribution among servers. Previous works have mainly adopted the temperature at the server inlet as the optimization constraint. However, the inlet temperature does not properly characterize the server’s thermal state. In this paper, a chip temperature-based workload allocation strategy (CTWA-MTP) is proposed to reduce the holistic power consumption in data centers. Our method adopts an abstract heat-flow model to describe the thermal environment in data centers and uses a thermal resistance model to describe the convective heat transfer of the server. The core optimizes the workload allocation with respect to the chip temperature threshold. In addition, the temperature-dependent leakage power of the server has been considered in our model. The proposed method is described as a constrained nonlinear optimization problem to find the optimal solution by a genetic algorithm (GA). We applied the method to a sample data center constructed with computational fluid dynamics (CFD) software. By comparing the simulation results with other different workload allocation strategies, the proposed method prevents the servers from overcooling and achieves a substantial energy saving by optimizing the workload allocation in an air-cooled data center.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2123-:d:122808
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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.
    2. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    3. 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. Abbas Akbari & Ahmad Khonsari & Seyed Mohammad Ghoreyshi, 2020. "Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers," Energies, MDPI, vol. 13(11), pages 1-15, June.
    2. 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).
    3. 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).

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