Leveraging AI for Energy Efficiency in Cloud and Edge Computing Infrastructures

Abstract

As the demand for computing power continues to escalate, the need for energy-efficient solutions in cloud and edge computing infrastructures has become paramount. This paper explores the integration of Artificial Intelligence (AI) techniques to enhance energy efficiency in these environments. We present a comprehensive review of AI-driven methodologies, including predictive analytics, resource optimization, and intelligent workload management, that facilitate reduced energy consumption while maintaining performance. Additionally, we analyze case studies demonstrating the application of AI in real-world scenarios, highlighting significant energy savings and improved sustainability outcomes. Through a detailed examination of various AI frameworks, we identify best practices for deploying energy-efficient cloud and edge computing solutions. Our findings indicate that leveraging AI not only optimizes resource utilization but also contributes to the reduction of carbon footprints, paving the way for greener computing infrastructures. This study underscores the critical role of AI in advancing energy efficiency, offering insights into future research directions and practical applications in the evolving landscape of cloud and edge computing.