Enabling Energy-Efficient Cloud Computing Workloads Through Smart Resource Allocation

Abstract

In the rapidly evolving cloud computing landscape, energy efficiency has emerged as a critical concern due to growing demand for scalable and sustainable IT infrastructure. This study introduces a data-centric approach to cloud computing that leverages real-world datasets to optimize resource allocation, thereby reducing energy consumption without compromising performance. By analyzing patterns in workload demands and resource utilization from publicly available datasets such as the Google Cluster Data and AWS Cost and Usage Reports, our methodology dynamically adjusts computational resources to match actual usage, minimizing idle power and enhancing overall efficiency. We implement machine learning algorithms to predict workload fluctuations and automate resource scaling, demonstrating significant energy savings in diverse cloud environments. The results indicate that our smart resource allocation framework can achieve up to 30% reduction in energy usage while maintaining service quality, highlighting the potential for sustainable cloud operations. This research contributes to the ongoing efforts in making cloud computing more environmentally friendly and cost-effective, paving the way for greener data centers and more responsible resource management.