Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres

Mackie, Max, Sun, Hongjian and Jiang, Jing (2021) Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres. In: ISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies: Smart Grids: Toward a Carbon-free Future, 18-21 Oct 2021, Virtual, Espoo, Finland. (Unpublished)

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This paper proposes a method of migrating workload among geo-distributed data centres that are equipped with on-site renewable energy sources (RES), such as solar and wind energy, to decarbonise data centres. It aims to optimise the performance of such a system by introducing a tunable Reinforcement Learning (RL) based load-balancing algorithm that implements a Neural Network to intelligently migrate workload. By migrating workload within the network of geo-distributed data centres, spatial variations in electricity price and intermittent RES can be capitalised upon to enhance data centres' operations. The proposed algorithm is evaluated by running simulations using real-world data traces. It is found that the proposed algorithm is able to reduce costs by 6.1 whilst also increasing the utilisation of RES by 10.7.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Data centre, Machine Learning, Load Balancing, Reinforcement Learning
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
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Depositing User: John Coen
Date Deposited: 03 Aug 2021 10:37
Last Modified: 27 Jan 2022 15:30

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