A Meta Heuristics SMO-SA Hybrid Approach for Resource Provisioning in Cloud Computing Framework

Cloud computing is an up-to-date model for distributing information processing utility and provides a large amount of resources through the internet. The major challenges affecting a cloud computing environment include resource provisioning and security. In this paper, we focused on resource provisioning mechanisms using Meta-heuristics techniques such as spider monkey optimization (SMO) and simulated annealing (SA). A simulated annealing algorithm helps to give a fine solution along with statistical promises for uncovering the best solution, yet it cannot notify whether the best solution is found. So it requires another method to overcome this drawback. This paper presents the Spider Monkey Optimization algorithm with Simulated Annealing (SMO-SA) to generate the best fitness value possible. The aim of the proposed hybrid algorithm is to generate the minimum fitness value by combining spider monkey optimization with simulated annealing to provision the resources dynamically. This paper also presents the step-by-step mathematical working of our proposed hybrid algorithm by applying it to the relevant data set and calculating the speedup factor as well as mean square error (MSE) value along with fitness value, which shows the effective impact of our proposed SMO-SA algorithm.