Dynamic Resource Management and Load Balancing in Cloud Computing using Efficient Hierarchical Clustering Techniques

Abstract

The cloud computing environments demand efficient resource management and load balancing to optimize performance, scalability, and cost-effectiveness. This research proposes a novel approach to dynamic resource management and load balancing using efficient hierarchical clustering techniques. By leveraging the hierarchical clustering, the proposed framework dynamically groups cloud resources based on workload patterns, resource utilization, and system demands. This employs a multi-level clustering strategy to categorize virtual machines and tasks, which enables the adaptive resource allocation and load distribution. Simulations results demonstrate that the proposed technique significantly improves resource utilization, reduces response times, and enhances system scalability compared to traditional load balancing algorithms. The proposed approach also minimizes the energy consumption and operational costs, which makes it suitable for large-scale cloud infrastructures. This research contributes to advancing cloud computing efficiency, which offeres a scalable and robust solution for dynamic resource management in heterogeneous cloud environments.