An Experimental Evaluation of Energy-Efficient Green Cloud Computing Strategies

Saurabh Prashant Maske

Assistant Professor (CHB) Department of Computer Science, G.S. Gawande Mahavidyalaya, Umarkhed, Dist. Yavatmal

spmaske01@gmail.com

Abstract
The rapid expansion of cloud computing has led to the large-scale deployment of data centers, resulting in high energy consumption and increased environmental concerns. Traditional cloud management approaches mainly emphasize performance optimization, often overlooking energy efficiency and sustainability. Green cloud computing addresses these challenges by introducing energy-aware strategies that aim to minimize power consumption while maintaining acceptable system performance.

This paper presents an experimental performance study of green cloud computing techniques for energy optimization. The study evaluates multiple energy-efficient strategies, including dynamic virtual machine consolidation, energy-aware resource allocation, idle resource management, and dynamic voltage and frequency scaling. Experiments are conducted using the CloudSim toolkit to simulate a realistic cloud environment with heterogeneous virtual machines and varying workloads.

The performance of green cloud computing techniques is assessed using key metrics such as total energy consumption, average response time, and resource utilization. Experimental results show that the adoption of green strategies significantly reduces energy consumption compared to traditional cloud resource management approaches, while maintaining satisfactory Quality of Service levels. The findings highlight the importance of integrating energy-aware techniques to achieve sustainable and cost-effective cloud computing environments. This study contributes valuable insights for researchers and practitioners aiming to design energy-efficient cloud infrastructures.

Keywords
Green Cloud Computing; Energy Optimization; Energy-Efficient Resource Management; Cloud Computing; Virtual Machine Consolidation; Sustainable Computing.