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Towards reinforceme...
Towards reinforcement learning approach to energy-efficient control of server fans in data centres
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- Berezovskaya, Yulia (författare)
- Luleå tekniska universitet,Datavetenskap
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- Yang, Chen-Wei (författare)
- Luleå tekniska universitet,Datavetenskap
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- Vyatkin, Valeriy (författare)
- Luleå tekniska universitet,Datavetenskap,Aalto University, Helsinki, Finland
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). - : IEEE. ; , s. 1-4
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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visa färre...
Abstract
Ämnesord
Stäng
- Modern data centres require control, which aims to improve their energy efficiency and maintain their high availability. This work considers the implementation of a server fan agent, which is intended to minimise the power consumption of the corresponding server fan or group of fans. In the paper, the reinforcement learning approach to energy-efficient control of server fans is suggested. The reinforcement learning workflow is considered. The Simulink blocks simplifying the building of the environment for the reinforcement learning agent are developed. This work provides the framework for creating and training reinforcement learning agents of different types. As the paper is only a work-in-progress, possible type of agents and their training process is described, but training and deploying the agent is a work for the future.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Training
- Fans
- Data centers
- Power demand
- Software packages
- Conferences
- Reinforcement learning
- data centre
- energy-efficient control
- multi-agent control
- reinforcement learning
- Dependable Communication and Computation Systems
- Kommunikations- och beräkningssystem
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)