Energy-efficient Cloud Computing

Our research in this area aims to develop innovative techniques and methodologies for optimising the energy efficiency of cloud computing services, workloads, and infrastructure. We explore various strategies, such as virtualization, resource allocation, workload scheduling, and dynamic power management, to minimize the energy consumption of data centers while maintaining optimal performance and reliability. We also explore alternative approaches to cloud service architectures, including cloud continuum and edge computing, which distribute computing resources more efficiently and reduce the overall energy footprint.

Additionally, we investigate autonomous network management, which seeks to harness the power of decentralised, federated, or distributed AI to intelligently manage underlying compute infrastructure and services. This approach aims to reduce downtime, decrease energy usage, and maximize resource utilization, leading to more efficient and eco-friendly cloud services. By incorporating decentralised AI-driven insights and automation, we strive to create self-optimizing cloud systems that can adapt and respond to changing workloads and environmental conditions in real-time, further reducing energy consumption and environmental impact.

Through our research in energy-efficient cloud computing, we aim to provide both practical solutions and thought leadership in the development of greener, more sustainable cloud services, contributing to a future where digital technologies have a reduced impact on the environment.