Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres

Max Mackie, Hongjian Sun, Jing Jiang

Research output: Contribution to conferencePaperpeer-review

Abstract

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%.
Original languageEnglish
Number of pages6
Publication statusAccepted/In press - 20 Jul 2021
EventISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies: Smart Grids: Toward a Carbon-free Future - Virtual, Aalto University, Espoo, Finland
Duration: 18 Oct 202121 Oct 2021
https://ieee-isgt-europe.org/

Conference

ConferenceISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies
Abbreviated titleIEEE PES ISGT EUROPE 2021
CountryFinland
CityEspoo
Period18/10/2121/10/21
Internet address

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