Workflow scheduling in scientific computing systems is one of the most challenging problems that focuses on satisfying user-defined quality of service requirements while minimizing the workflow execution cost. So to reduce the cost, cloud environment, has been deployed in cloud environment, resources will increase but its utilization is another challenge. To maintain & utilize resources in the cloud computing scheduling mechanism is needed. Many algorithms and protocols are used to manage the parallel jobs and resources which are used to enhance the performance of the CPU in the cloud environment. This work Particles swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are used for effective scheduling. This work is based on the optimization of Total execution time and total execution cost. The results of the proposed approach are found to be effective in compare to existing methods. Intelligence optimization Particle Swarm optimization is used which is initialized by Pareto distribution. GWO is used to converge the decision of Virtual Machine (VM) migration by
its convergence to minimize cost and time as illustrated by Total execution time (TET) and Total execution cost (TEC) .It is concluded that GWO performs better in compare to existing BAT algorithm.