Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158611
Title: Joint IT-facility optimization for green data centers via deep reinforcement learning
Authors: Zhou, Xin
Wang, Ruihang
Wen, Yonggang
Tan, Rui
Keywords: Engineering::Computer science and engineering::Computer applications::Computer-aided engineering
Issue Date: 2021
Source: Zhou, X., Wang, R., Wen, Y. & Tan, R. (2021). Joint IT-facility optimization for green data centers via deep reinforcement learning. IEEE Network, 35(6), 255-262. https://dx.doi.org/10.1109/MNET.011.2100101
Project: NRF2017EWT-EP003-023 
NRF2015ENC-GDCR01001-003 
Journal: IEEE Network
Abstract: The data center market grows rapidly with the increase of data and its corresponding applications (e.g., machine learning, cloud storage, Internet of Things, and so on). The growth is boosted recently due to the shift of activities online during the COVID-19 pandemic. Reducing the energy consumption of data centers faces various challenges that are further aggravated by the tropical conditions with high temperature and humidity in the tropics like Singapore. The prevailing siloed approach of operating the information technology (IT) and the facility systems separately has resulted in wasteful over-provisioning. The recently proposed approaches for energy usage minimization under various constraints including thermal safety scale poorly with the data center size and often result in non-optimal solutions. To advance the state of the art, we apply deep reinforcement learning (DRL) to address the scalability problem and achieve optimality over a long time horizon in reducing data center energy usage. In particular, we deploy the data-driven deep model and physical rule based model in lieu of the physical data center during the training and validation phases to manage the thermal safety risks caused by DRL's strategy of learning from errors.
URI: https://hdl.handle.net/10356/158611
ISSN: 0890-8044
DOI: 10.1109/MNET.011.2100101
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/MNET.011.2100101.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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