Cloud Computing With Resource Allocation Based on Ant Colony Optimization

Abstract

In this study, we explore the intricacies of cloud computing technologies, with an emphasis on the challenges and concerns pertinent to resource allocation. Three optimization techniques-Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA)-have been meticulously analyzed concerning their applications, objectives, and operational methodologies. The study underscores these algorithms' pivotal role in enhancing cloud resource optimization, while also elucidating their respective merits and limitations. As the complexity of cloud computing escalates, devising efficacious strategies for resource management and allocation becomes imperative. Such strategies are paramount in aiding organizations in cost containment and performance amplification. The ensuing comparative analysis has been crafted to offer a holistic insight into the three algorithms, thus empowering cloud providers to judiciously select an optimization technique that aligns with the unique demands and challenges of their cloud computing infrastructure.