Comparison of Deep Reinforcement Learning Algorithms in Data Center Cooling Management: A Case Study

Tianyang Hua, Jianxiong Wan, Shan Jaffry, Zeeshan Rasheed, Leixiao Li, Zhiqiang Ma

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

The growth in scale and power density of Data Centers (DC) poses serious challenges to the cooling management. Recently, there are many studies using machine learning to solve the cooling management problems. However, a comprehensive comparative study is still missing. In this work, we compare the performance of various Deep Reinforcement Learning (DRL) algorithms, including Deep-Q Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Branching Dueling Q-Network (BDQ), using the Active Ventilation Tiles (AVTs) control problem in raised-floor DC as an example. In particular, we design two multiagent algorithms based on DQN and three critic architectures for DDPG. Simulations based on real world workload show that DDPG provides the best performance over the considered algorithms.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages392-397
Number of pages6
ISBN (Electronic)9781665442077
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne, Australia
Duration: 17 Oct 202120 Oct 2021

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Country/TerritoryAustralia
CityMelbourne
Period17/10/2120/10/21

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