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%.
|Number of pages||6|
|Publication status||Accepted/In press - 20 Jul 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
|Conference||ISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies|
|Abbreviated title||IEEE PES ISGT EUROPE 2021|
|Period||18/10/21 → 21/10/21|