SWARM INTELLIGENCE OPTIMIZATION FOR RESOURCE ALLOCATION IN CLOUD COMPUTING ENVIRONMENTS

Abstract

Cloud computing has emerged as a powerful paradigm for resource allocation due to its scalability and flexibility. Efficient resource allocation is critical for optimizing the performance and utilization of cloud resources. In this context, swarm intelligence optimization algorithms, such as Salp Swarm Optimization (SSO), have shown promising results in solving complex optimization problems. This paper presents a novel approach that utilizes SSO for resource allocation in cloud computing environments. The proposed approach aims to maximize resource utilization, minimize response time, and improve overall system performance. The SSO algorithm is used to dynamically allocate virtual machines (VMs) to physical hosts based on their resource demands and availability. Experimental results demonstrate that the proposed approach outperforms existing methods in terms of resource utilization and response time, thereby enhancing the efficiency of cloud computing environments.