A Multi-Objective Clustered Input Oriented Salp Swarm Algorithm in Cloud Computing

Infrastructure as a Service (IaaS) in cloud computing enables flexible resource distribution over the Internet, but achieving optimal scheduling remains a challenge. Effective resource allocation in cloud-based environments, particularly within the IaaS model, poses persistent challenges. Existing methods often struggle with slow optimization, imbalanced workload distribution, and inefficient use of available assets. These limitations result in longer processing times, increased operational expenses, and inadequate resource deployment, particularly under fluctuating demands. To overcome these issues, a novel Clustered Input-Oriented Salp Swarm Algorithm (CIOSSA) is introduced. This approach combines two distinct strategies: Task Splitting Agglomerative Clustering (TSAC) with an Input Oriented Salp Swarm Algorithm (IOSSA), which prioritizes tasks based on urgency, and a refined multi-leader model that accelerates optimization processes, enhancing both speed and accuracy. By continuously assessing system capacity before task distribution, the model ensures that assets are deployed effectively and costs are controlled. The dual-leader technique expands the potential solution space, leading to substantial gains in processing speed, cost-effectiveness, asset efficiency, and system throughput, as demonstrated by comprehensive tests. As a result, the suggested model performs better than existing approaches in terms of makespan, resource utilisation, throughput, and convergence speed, demonstrating that CIOSSA is scalable, reliable, and appropriate for the dynamic settings found in cloud computing.