Authors: Sonali Vidhate, Fuldeore Pritee, Jagtap Vaishnavi, Khairnar Vishakha, Aruba Kudai, Safa Madoo
Abstract: The rapid expansion of cloud computing, artificial intelligence (AI), and data-intensive applications has significantly increased the energy consumption of data centers, making sustainability a critical concern. Conventional energy optimization techniques such as virtualization, Dynamic Voltage and Frequency Scaling (DVFS), and static cooling mechanisms provide limited adaptability to modern, dynamic workloads. This research paper presents a comprehensive analysis of AI-driven green computing approaches for improving energy efficiency in data centers. Using insights from existing literature, this work proposes an intelligent framework that integrates machine learning, reinforcement learning, and predictive analytics to optimize workload distribution, cooling systems, and energy demand forecasting in real time. The proposed approach aims to reduce energy consumption, minimize carbon emissions, and improve Power Usage Effectiveness (PUE) while maintaining system performance. Additionally, novel innovations such as carbon-aware scheduling and renewable-energy-aware AI optimization are discussed to enhance sustainability. The findings indicate that AI-based energy management can achieve significant energy savings and support the development of future-ready green data centers.
DOI: http://doi.org/10.5281/zenodo.20849513
