Evolutionary Programming for Dynamic Resource Management and Energy Optimization in Cloud Computing

This research paper investigates the use of Evolutionary Programming (EP) for dynamic resource management and energy optimization in cloud computing. The goal is to reduce energy consumption and costs by dynamically allocating resources based on the current workload and energy prices. The proposed EP approach is evaluated on a real-world dataset collected from a cloud data centre. The results show that the EP approach is effective in optimizing resource allocation and reducing energy consumption compared to a baseline static allocation approach. Specifically, the EP approach achieved an average reduction of 28% in energy consumption while maintaining similar levels of performance. The findings of this research have important implications for the industrial and commercial use of cloud computing as it can lead to significant cost savings and a reduced carbon footprint. The proposed EP approach can be easily integrated into existing cloud infrastructures and can be customized to meet specific requirements and constraints.