An analysis of the use of mutation-based particle swarm optimization (PSO) for load balancing in cloud computing

Cloud computing is a technology that facilitates tasks by allocating virtual machine (VM) dynamically. Users charge resources as they use based on their demands. There are so many challenges faced by cloud provider. One of the most challenges for him is load balancing. There are so many algorithms are available for proper load balancing but here we are focusing on particle swarm based algorithm that can balance the load in cloud computing so that resources are easily available for users. A cloud provider has to face many challenges. One out of the essential problem is load balancing, which suffers from many issues like premature convergence, reduced convergence speed, at first chosen random solutions, and stuck in native optima. The proposed method considered the MakeSpan parameters to handle the problem related to existing met heuristic techniques. The proposed method focuses on the mutation-based Particle Swarm algorithm to balance load among the data centers. Here an efficient load balancing algorithm is developed to minimize performance parameters like MakeSpan time and improve the fitness function in cloud computing. Our aim is to develop an efficient load balancing algorithm using particle swarm based to minimize performance paramaters like make span.

Keywords:

cloud computing , load balancing , particle swarm optimization , virtual machine.