基于蚁群优化的资源分配云计算

Taras Kniazhyk, Oleksandr Muliarevych
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摘要

在本研究中,我们探讨了云计算技术的复杂性,重点关注与资源分配相关的挑战和问题。我们对粒子群优化(PSO)、蚁群优化(ACO)和遗传算法(GA)这三种优化技术的应用、目标和操作方法进行了细致的分析。研究强调了这些算法在加强云资源优化方面的关键作用,同时也阐明了它们各自的优点和局限性。随着云计算的复杂性不断升级,制定有效的资源管理和分配策略势在必行。这些策略对于帮助企业控制成本和提高性能至关重要。接下来的比较分析旨在提供对这三种算法的整体见解,从而使云提供商能够明智地选择符合其云计算基础设施的独特需求和挑战的优化技术。
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Cloud Computing With Resource Allocation Based on Ant Colony Optimization
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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