Abstract-Cloud computing is a collection of heterogeneous virtualized resources that can be accessed on-demand to service applications. Scheduling large and complex workflows becomes a challenging issue in cloud computing with a requirement that the execution time as well as cost incurred by using a set of heterogeneous cloud resources should be minimizes simultaneously. In this paper, we have extended our previously proposed Bi-Criteria Priority based Particle Swarm Optimization (BPSO) algorithm to schedule workflow tasks over the available cloud resources under given the deadline and budget constraints while considering the confirmed reservation of the resources. The extended heuristic is simulated and comparison is done with state-of-art algorithms. The simulation results show that extended BPSO algorithm also decreases the execution cost of schedule as compared to state-of-art algorithms under the same deadline and budget constraint while considering the exiting load of the resources too.
Another recent novel algorithm in 2015, Verma and Kaushal REF proposed Bi-Criteria of Priority according to Particle Swarm Optimization (BPSO) policy to schedule workflow tasks.
Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing
International Journal of Information Technology and Computer Science
Computer Science