Cloud computing, thanks to the pervasiveness of information technologies, provides a foundational environment for developing IT applications, offering organizations virtually unlimited and flexible computing resources on a pay-per-use basis. However, the large data centres where cloud computing services are hosted consume significant amounts of electricity annually due to Information and Communication Technology (ICT) components. This issue is exacerbated by the increasing deployment of large artificial intelligence (AI) models, which often rely on distributed data centres, thereby significantly impacting the global environment. This study proposes the COUNTER model, designed for sustainable cloud resource management. COUNTER is integrated with cluster graph neural networks and evaluated in a simulated cloud environment, aiming to reduce energy consumption while maintaining quality of service parameters. Experimental results demonstrate improvements in resource utilisation, energy consumption, and cost effectiveness compared to the baseline model, HUNTER, which employs a gated graph neural network aimed at achieving carbon neutrality in cloud computing for modern ICT systems.
@article{arxiv.2504.09995,
title = {COUNTER: Cluster GCN based Energy Efficient Resource Management for Sustainable Cloud Computing Environments},
author = {Han Wang and Sukhpal Singh Gill and Steve Uhlig},
journal= {arXiv preprint arXiv:2504.09995},
year = {2025}
}