Spider Monkey Optimization based Resource Provisioning in Cloud Computing Environment

This paper aims to provide an effective provisioning mechanism of resources with minimum execution time and solve over and under-provisioning issues. Also, to give a performance comparison between two popular meta-heuristics techniques, which may motivate researchers to find an optimization algorithm that is better concerning resource provisioning for a cloud environment. Spider Monkey Optimization (SMO) helps solve the optimization problem and reach the solution minimally. In this research paper, Spider Monkey Optimization has proposed effectively provisioning resources in a cloud environment. Particle Swarm Optimization (PSO) is another meta-heuristic method used to compare with the SMO method. CloudSimPlus simulator is used for simulation. The performance parameters such as execution or provisioning time, fitness value, mean, and standard deviation are calculated to evaluate the performance of the proposed method. This study considered the 100 to 1000 task iteration with a task size of 50. From the simulation results, it is stated that the values of execution time, fitness, the mean and standard deviation of the SMO method are 6 ms, 0.0197, 0.0236, and 0.0011, respectively. In contrast, the values for the PSO method are 57 ms, 0.5675, 0.0567, and 0.5108, respectively. SMO has been found to effectively impact the provisioning of resources by minimizing the execution time, optimizing the fitness value, and lowering the mean and standard deviation values.