• Sanjay Gupta

Cloud computing is an emerging technology in the current scenario of data storage and computation. The umbrella of cloud computing offers various services over the internet, such as infrastructure, software, and application platforms. The process of delivering services required multiple resources in cloud computing. The virtual machine is a utility component of cloud computing systems. The performance of a cloud computing system depends on the management of virtual machines and the allocation of resources. The dynamic load balancing approach deals with swarm intelligence algorithms. In this paper, we propose a meta-heuristic algorithm called MPSO based on particle swarm optimization for virtual machine (VM) scheduling and balancing the load in cloud computing. The particle swarm optimization set the diverse properties of the virtual machine and requested the job. The define fitness constraints function partially allocates jobs for dedicated machines and distributes them according to the process job scheduler. For the evaluation of performance, we used cloud simulator software, which is called cloud analyst. The cloud analysis software is a bag of composition of cloud environment and load balancing policy. In the scenario of policy design, there are two types of policies: one is a genetic algorithm policy and the other is a PSO-based policy. The PSO-based policy reduces the load effect by approximately 10–12% in the compression of the genetic algorithm.

Cloud Computing, Load Balancing, Virtual Machine, CloudSim, PSO

"Efficient Load Balancing Algorithm using Improved Particle Swarm Optimization for Cloud Computing Environment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.4, Issue 2, page no.445-451, February-2017, Available :http://www.jetir.org/papers/JETIR1702071.pdf

"Efficient Load Balancing Algorithm using Improved Particle Swarm Optimization for Cloud Computing Environment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 2, page no. pp445-451, February-2017, Available at : http://www.jetir.org/papers/JETIR1702071.pdf