Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres
Mackie, Max; Sun, Hongjian; Jiang, Jing
Professor Hongjian Sun email@example.com
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%.
Mackie, M., Sun, H., & Jiang, J. (2021). Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres.
|Conference Name||IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) 2021|
|Conference Location||Espoo, Finland|
|Start Date||Oct 18, 2021|
|End Date||Oct 21, 2021|
|Acceptance Date||Jul 20, 2021|
|Deposit Date||Jul 20, 2021|
|Publicly Available Date||Oct 22, 2021|
|Additional Information||Conference dates: 18-21 October 2021|
Accepted Conference Proceeding
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