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Research Advances on AI-Powered Thermal Management for Data Centers
Hui Liu(
),
),
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Liu H, Aljbri A, Song J, et al.
Research Advances on AI-Powered Thermal Management for Data Centers.
Tsinghua Science and Technology,
2022, 27(2): 303-314.
https://doi.org/10.26599/TST.2021.9010019
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Given the complex nature of data centers’ thermal management, which costs too many resources, processing time, and energy consumption, thermal awareness and thermal management powered by artificial intelligence (AI ) are the targeted study. In addition to a few research on AI techniques and models, other strategies have also been introduced in recent years. Data center models, including cooling, thermal, power, and workload models, and their relationship are factors that need to be understood in the optimal thermal management system. Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines (VMs), hotspot identification, thermal state estimation, and power usage change. AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening. At present, research on AI-powered thermal control is still in its infancy. This paper concludes with four issues in thermal management, which will be the scope of further research.
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Research Advances on AI-Powered Thermal Management for Data Centers
Hui Liu(
),
),
Abstract
Given the complex nature of data centers’ thermal management, which costs too many resources, processing time, and energy consumption, thermal awareness and thermal management powered by artificial intelligence (AI ) are the targeted study. In addition to a few research on AI techniques and models, other strategies have also been introduced in recent years. Data center models, including cooling, thermal, power, and workload models, and their relationship are factors that need to be understood in the optimal thermal management system. Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines (VMs), hotspot identification, thermal state estimation, and power usage change. AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening. At present, research on AI-powered thermal control is still in its infancy. This paper concludes with four issues in thermal management, which will be the scope of further research.
Keywords:
artificial intelligence, data center, thermal management, heuristic, metaheuristic
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Publication history
Received: 31 December 2020
Revised: 08 February 2021
Accepted: 28 February 2021
Published:
29 September 2021
Issue date: April 2022
Copyright
© The author(s) 2022
Acknowledgements
This paper was supported by the National Natural Science Foundation of China (Nos. 61662057 and 61672143).
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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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