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; Zhiyong Liu

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2023.31546

Published Article

LAPSE:2023.31546

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, Zhiyong Liu

April 19, 2023

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.

Record ID

LAPSE:2023.31546

Keywords

cooling system, data center, deep reinforcement learning, Energy Efficiency, multi-agent, scheduling algorithm

Subject

Planning & Scheduling

Suggested Citation

Chi C, Ji K, Song P, Marahatta A, Zhang S, Zhang F, Qiu D, Liu Z. Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning. (2023). LAPSE:2023.31546

Author Affiliations

Chi C: 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 [ORCID]
Ji K: 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
Song P: Information Engineering College, Capital Normal University, Beijing 100048, China
Marahatta A: Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
Zhang S: Information Engineering College, Capital Normal University, Beijing 100048, China
Zhang F: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
Qiu D: Information Engineering College, Capital Normal University, Beijing 100048, China
Liu Z: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China

Journal Name

Energies

Volume

14

Issue

8

First Page

2071

Year

2021

Publication Date

2021-04-08