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

Ce Chi, Kaixuan Ji, Penglei Song, Avinab Marahatta, Shikui Zhang, Fa Zhang, Dehui Qiu and Zhiyong Liu
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Ce Chi: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
Kaixuan Ji: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, 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
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

Energies, 2021, vol. 14, issue 8, 1-32

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.

Keywords: data center; energy efficiency; deep reinforcement learning; multi-agent; scheduling algorithm; cooling system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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