Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing

Cloud computing is a collection of heterogeneous\udvirtualized resources that can be accessed on-demand to service\udapplications. Scheduling large and complex workflows becomes\uda challenging issue in cloud computing with a requirement that\udthe execution time as well as cost incurred by using a set of\udheterogeneous cloud resources should be minimizes\udsimultaneously. In this paper, we have extended our previously\udproposed Bi-Criteria Priority based Particle Swarm\udOptimization (BPSO) algorithm to schedule workflow tasks\udover the available cloud resources under given the deadline and\udbudget constraints while considering the confirmed reservation\udof the resources. The extended heuristic is simulated and\udcomparison is done with state-of-art algorithms. The simulation\udresults show that extended BPSO algorithm also decreases the\udexecution cost of schedule as compared to state-of-art\udalgorithms under the same deadline and budget constraint while\udconsidering the exiting load of the resources too