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Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers

Author

Listed:
  • Kaixuan Ji

    (High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

  • Ce Chi

    (High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

  • Fa Zhang

    (High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

  • Antonio Fernández Anta

    (IMDEA Networks Institute, Avda. del Mar Mediterraneo, 22, 28918 Leganes, Spain)

  • Penglei Song

    (Information Engineering College, Capital Normal University, Beijing 100048, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

  • Avinab Marahatta

    (Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China)

  • Youshi Wang

    (Meituan-Dianping Group, Beijing 100102, China)

  • Zhiyong Liu

    (High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

Abstract

The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy reduction results are compared. Results show that EATS achieves more energy-savings of servers, cooling systems, state transition in comparison to the other two techniques under a various number of servers, cooling modules and task arrival intensities. It is validated that EATS is effective at reducing total energy consumption and improving the resource utilization of data centers.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2382-:d:541478
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    References listed on IDEAS

    as
    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. Nicola Jones, 2018. "How to stop data centres from gobbling up the world’s electricity," Nature, Nature, vol. 561(7722), pages 163-166, September.
    3. 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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