Local Neighbour Spider Monkey Optimization-Based Resource Allocation in Cloud Computing Environment

This paper proposes a novel optimization strategy called Local Neighbour Spider Monkey Optimization (LNSMO) algorithm for efficient resource allocation in Cloud Computing (CC) environs. The core innovation lies in integrating localized neighbourhood learning with adaptive decision-making, which significantly enhances the exploration–exploitation balance during task scheduling. A formal mathematical model is presented, and LNSMO is applied to minimize makespan while maximizing resource utilization and reducing energy consumption. The extensive experiments are conducted using benchmark scenarios and compared against standard algorithms including GA, PSO, ACO and traditional SMO. The proposed LNSMO achieved a 12% reduction in makespan, a 4.2% improvement in resource utilization, and an 8.3% decrease in energy consumption relative to the best-performing baseline. Statistical validation using p-Formula: see text confirmed the significance of the results. These outcomes demonstrate LNSMO’s strong potential for real-world cloud infrastructure optimization.