Analysis of Green Computing Models on AWS Using Machine Learning Algorithms

The increasing demand for computational resources has led to a surge in energy consumption, posing significant environmental challenges. This study analyzes green computing models on Amazon Web Services (AWS) by leveraging machine learning algorithms to optimize resource utilization and minimize energy consumption. Using a dataset of virtual machine (VM) metrics-such as CPU and memory usage, network traffic, and power consumption-various machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, are employed to predict energy efficiency and identify key factors influencing sustainable computing. The analysis focuses on understanding the relationship between resource utilization and power consumption and how task characteristics like type and priority impact energy efficiency. By exploring AWS services, such as EC2 and Auto Scaling, and applying machine learning techniques for real-time resource allocation, this research provides insights into reducing the carbon footprint of cloud operations. The findings contribute to the development of intelligent resource management strategies, promoting eco-friendly computing practices in cloud environments while maintaining operational efficiency.