A Particle Swarm Optimization based Technique for Scheduling Workflow in Cloud DataCenter

Live virtual migration is a way for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This way has been developed to decrease the downtime for migrating overloaded VMs, but it still consumes timeand cost, and a huge amount of memory is involved in this migration process. To overcome these drawbacks, we propose a Load Balancing method using Particle Swarm Optimization (LBPSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migration of the entire overloaded VM. We have designed an optimization model to migrate these extra tasks to the new host LB-PSO by applying Particle Swarm Optimization (PSO), where PSO will randomly find the suitable VM so as to transfer the load. To evaluate the proposed method, we have extended the cloud simulator (Cloudsim) package with the use of PSO in its task scheduling model. The simulation results show that the proposed LB-PSO method has significantly reduced the time taken by the load balancing with live migration on heterogeneous environment as compared to traditional load balancing approaches with live migration.