Intelligent Green Computing
This research project tackles sustainability challenges in cloud–edge computing by addressing the lack of accurate, architecture-agnostic methods for measuring energy use and carbon emissions in distributed systems. It highlights structural limitations in current energy models and monitoring tools, especially their reliance on hardware-specific assumptions and incomplete sustainability metrics.
The project introduces PRECISE, a machine-learning-based framework for hardware-independent energy estimation. Through a systematic review of sustainability metrics and deployment in Kubernetes-based edge–cloud environments, it shows how reliable energy estimation can support carbon-aware optimisation, guide developer and operational decisions, and enable the design of more sustainable cloud-native applications.
Dataset:
Edge Cluster Data – IEEE Dataport
(590 views, 56 votes, 13 downloads)
Related paper:
GreenBytes: Intelligent Energy Estimation for Edge–Cloud Intelligence
Thesis:
To be available soon. The thesis is currently embargoed due to intellectual property restrictions.
Academic profile:
Website: https://researchportal.lsbu.ac.uk/en/persons/kasra-kassai/