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 opti- mization 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 elucidat- ing their respective merits and limitations. As the complexity of cloud computing escalates, devising efficacious strategies for resource management and alloca- tion 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 de- mands and challenges of their cloud computing infrastructure.
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