Utilizing Advanced Group Search Optimization (AGSO) methodology for resource optimization in cloud computing

Abstract

The escalating demand for resources within cloud data centers has accentuated the critical need for robust resource selection strategies implemented by customers. The inefficiencies prevalent in current resource utilization underscore the urgency of addressing this complex challenge. While metaheuristics, in comparison to traditional heuristics, showcase superior capabilities in efficiently scheduling large requests, their potential in selecting customer services can be further augmented by mitigating issues related to slow convergence speed and achieving a more equitable balance between local and global search. The overarching goal of the cloud computing platform is to furnish users with optimal services, prioritizing privacy and confidentiality. Leveraging cloud computing has proven to be instrumental in profit maximization for company executives. In the intricate landscape of cloud computing environments, workflow scheduling algorithms play a pivotal role in optimizing the intricate scheduling processes. This research endeavors to introduce the novel Adaptive Group Search Optimization (AGSO) method, aiming to establish its significance in comparison to widely-used algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Bat Algorithm (BA), Firefly Algorithm (FA), Gravitational Search Algorithm (GSA), and Group Search Optimization (GSO) in the dynamic realm of optimizing resource selection for cloud services. AGSO, an innovative approach, seeks to address and alleviate the inherent limitations observed in existing metaheuristics, offering improvements in convergence speed and striking a refined balance in the exploration-exploitation tradeoff.