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Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning

Author

Listed:
  • 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.)

  • 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.)

  • Penglei Song

    (Information Engineering College, Capital Normal University, Beijing 100048, China)

  • Avinab Marahatta

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

  • Shikui Zhang

    (Information Engineering College, Capital Normal University, Beijing 100048, 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.)

  • Dehui Qiu

    (Information Engineering College, Capital Normal University, Beijing 100048, 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 problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.

Suggested Citation

  • Ce Chi & Kaixuan Ji & Penglei Song & Avinab Marahatta & Shikui Zhang & Fa Zhang & Dehui Qiu & Zhiyong Liu, 2021. "Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-32, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2071-:d:532482
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    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.
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    Cited by:

    1. Seppo Sierla & Heikki Ihasalo & Valeriy Vyatkin, 2022. "A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems," Energies, MDPI, vol. 15(10), pages 1-25, May.
    2. Mahbod, Muhammad Haiqal Bin & Chng, Chin Boon & Lee, Poh Seng & Chui, Chee Kong, 2022. "Energy saving evaluation of an energy efficient data center using a model-free reinforcement learning approach," Applied Energy, Elsevier, vol. 322(C).

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