AI-Driven Resource Allocation for Energy-Efficient Cloud Data Centers Abstract
Abstract
Cloud data centers consume massive amounts of energy due to dynamic workloads and increasing user demands. This paper proposes an Artificial Intelligence (AI)-based resource allocation framework that optimizes virtual machine placement, workload balancing, and energy consumption in cloud environments. The model uses machine learning algorithms to predict workload patterns and allocate computing resources dynamically. Experimental analysis demonstrates reduced power usage, improved server utilization, and lower operational costs compared to traditional scheduling methods. The proposed system enhances scalability, sustainability, and Quality of Service (QoS) for modern cloud infrastructures.
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