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 (Unpublished)
|
Text
paper_197.pdf - Accepted Version Download (323kB) | Preview |
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.
| 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 |
| Related URLs: | |
| Depositing User: | John Coen |
| Date Deposited: | 03 Aug 2021 10:37 |
| Last Modified: | 27 Jan 2022 15:30 |
| URI: | http://nrl.northumbria.ac.uk/id/eprint/46836 |
Actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
