Authors: Logeshwaran V, Bhuvaneswari B
Abstract: In recent years, the exponential growth of dataintensive applications and cloud-based services has drastically increased energy consumption in data centers worldwide. This surge poses significant challenges, including high operational costs, carbon emissions, and sustainability concerns. To address these issues, this paper proposes a comprehensive AI-based framework for energyefficient data center optimization. The approach integrates intelligent control algorithms, predictive analytics, and adaptive resource allocation mechanisms that dynamically manage workloads, cooling systems, and server utilization. Using techniques such as machine learning (ML), reinforcement learning (RL), and neural network-based control, the proposed model achieves significant energy savings while maintaining service-level agreements (SLAs). The research demonstrates how AI- driven systems can autonomously predict server loads, optimize cooling parameters, and minimize idle energy consumption. The findings contribute to the design of sustainable, selfoptimizing data centers for the next generation of green computing infrastructure.
