ABSTRACT
Workflow scheduling is a challenging field in computing in which tasks are scheduled according to the user
requirement and it becomes costly due to the quality of service demand by the user. Cloud environment has been
deployed for this work so as to reduce the overall cost. 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. 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. The particle swarm optimization is initialized by using Pareto
distribution. TET and TEC illustrated the minimized cost and time by using the GWO to converge the decision of
virtual machine. Thus the work concludes that GWO performs better in compare to existing BAT algorithm.
Keywords - Particles swarm Optimization (PSO), Grey Wolf Optimization (GWO), Virtual Machine, BAT
algorithm.