Enhancing cloud service efficiency through ant colony optimization with multi-objective task scheduling

Abstract

The emergence of cloud computing technology as a prevailing paradigm for providing scalable and readily available computing resources has presented a complex challenge in terms of efficiently allocating activities to resources, taking into consideration many innovative applications. The integration of Ant Colony Optimization, with particular focus on addressing multiple objectives, presents a potentially adaptable approach to tackling the intricacies inherent in the management of cloud services. This problem is often categorized as an NP-hard problem and necessitates methods that not only cater to user requirements but also enhance overall system efficiency. In this paper, the application of Ant Colony Optimization (ACO) is utilized for improving cloud service efficiency. As cloud infrastructure handle diverse and dynamic workloads, the study addresses the challenges of optimizing resource utilization and minimizing cost. The proposed methodology introduces multi-objective optimization, aiming to balance conflicting goals and create Pareto-optimal solutions. The integration of ACO in cloud-based job scheduling contributes to the discourse on advancing efficiency in modern computing environments. Results show the effectiveness of the proposed approach.