ENERGY-EFFICIENT CLOUD DATA CENTRES USING AI-BASED DYNAMIC RESOURCE MANAGEMENT

Abstract

The rapid growth of cloud computing services has led to a significant increase in the energy consumption of data Centres, posing both economic and environmental challenges. To address this issue, there is a growing need for intelligent and adaptive resource management strategies that can optimize energy usage without compromising performance. This research proposes an AI-based dynamic resource management framework for energy-efficient cloud data Centres. The framework leverages machine learning algorithms to predict workload patterns, optimize virtual machine (VM) placements, and dynamically adjust resource allocation in real-time. By integrating predictive analytics with intelligent scheduling techniques, the proposed system effectively minimizes idle server usage, reduces power consumption, and improves overall operational efficiency. Experimental results using simulated cloud environments demonstrate significant energy savings while maintaining desired Quality of Service (QoS) levels. This study highlights the potential of AI-driven solutions in enhancing the sustainability and cost-effectiveness of modern cloud infrastructures.