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 language | English |
|---|---|
| Number of pages | 6 |
| Publication status | Unpublished - 18 Oct 2021 |
| Event | ISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies: Smart Grids: Toward a Carbon-free Future - Virtual, Aalto University, Espoo, Finland Duration: 18 Oct 2021 → 21 Oct 2021 https://ieee-isgt-europe.org/ |
Conference
| Conference | ISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies |
|---|---|
| Abbreviated title | IEEE PES ISGT EUROPE 2021 |
| Country/Territory | Finland |
| City | Espoo |
| Period | 18/10/21 → 21/10/21 |
| Internet address |
Keywords
- Data centre
- Machine Learning
- Load Balancing
- Reinforcement Learning