A Study of Meta-heuristic based Techniques of Load Balancing in Cloud Computing Environment

An important issue which had risen with the advent of the cloud computing is the task scheduling methods. Task scheduling plays an important role in the performance and efficiency of the service offered by the clouds. The main focus in task scheduling is to reduce the time needed to complete all the executing tasks i.e. to decrease the Makespan. In this paper we have focused on five Metaheuristic based techniques used for load balancing in clouds, namely: i) Genetic Algorithm (GA); ii)) Ant Colony Optimization (ACO); iii) Particle Swarm Optimization (PSO); iv) Simulated Annealing; and v) Artificial Bee Colony Optimization(ABC). It has been found that the results given by Metaheuristic based techniques are within the reasonable time and are optimal.