ABSTRACT
Cloud computing has revolutionized computing paradigms by providing scalable resources accessible over the internet, To maximise efficiency and cut expenses, efficient resource allocation and job scheduling are necessary. This study explores the application of the Cuckoo Search Algorithm (CSA) to enhance virtual machine (VM) placement in cloud data centers. The project involves designing a simulation framework to model cloud data centers and workloads, integrating CSA into task scheduling processes, and evaluating performance depending on important variables such as energy efficiency, resource use, and job completion time. Through rigorous experimentation, the study demonstrates that CSA-based allocation significantly improves resource utilization and responsiveness to workload fluctuations compared to traditional methods. The algorithm's efficiency in exploring solution spaces and adapting to dynamic environments contributes to enhanced performance and cost-effectiveness in cloud environments. These findings suggest that CSA holds promise for optimizing resource allocation in dynamic cloud environments, advancing task scheduling techniques, improving service quality, and reducing operational costs. Future research could focus on refining CSA parameters across diverse cloud scenarios, integrating with machine learning techniques for enhanced predictive capabilities and real-time adaptation to evolving workload demands.
