Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres

Mackie, Max; Sun, Hongjian; Jiang, Jing

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%.

Citation

Mackie, M., Sun, H., & Jiang, J. (2021, October). Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres. Presented at IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) 2021, Espoo, Finland

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) 2021
• Start Date: Oct 18, 2021
• End Date: Oct 21, 2021
• Acceptance Date: Jul 20, 2021
• Publication Date: 2021-10
• Deposit Date: Jul 20, 2021
• Publicly Available Date: Oct 22, 2021
• Public URL: https://durham-repository.worktribe.com/output/1138687
• Publisher URL: https://easychair.org/smart-program/ISGT-Europe2021/index.html
• Additional Information: Conference dates: 18-21 October 2021