Applications of Virtual Machine Using Multi-Objective Optimization Scheduling Algorithm for Improving CPU Utilization and Energy Efficiency in Cloud Computing

Financial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, thus, efficient consumption entail a high financial cost. Through the effective utilization of scheduled gaps, total time in workflow can decreased by placing uncompleted tasks gaps through approximate computations. In current research, novel approach based multi-objective optimization is utilized with CloudSim underlying simulator order evaluate VM (virtual machine) allocation performance. this study, we determine consumption, CPU utilization, number executed instructions each scheduling interval complex solutions improve efficiency reduce time. Finally, simulation results analyses, all tested parameters simulated evaluated proper validation CloudSim. Based results, PSO (particle swarm optimization) achieve better effects different than GA (genetic algorithm) can.