An Approach to Mapping Scientific Workflow in Cloud Computing data centers to Minimize Costs of Workflow Execution

Execution of workflow application usually requires powerful-giant computing and storage resources. As consequence of expansion of distributed systems, especially cloud computing systems, an appropriate context has been provided for execution of such application. The interconnection between tasks and data commonality and transfer for the completion of computing cycle has become a major challenge. Therefore, an appropriate schedule which defines data distribution and Tasks order in distributed system is required for effective execution of Workflow application. In this paper, an approach to mapping workflow’s data and tasks between cloud system data centers has been presented. This approach, have been paid enough work to appropriately map tasks and data between data centers in such a way that the total time for task execution and data movement becomes minimal. In other words, the goal of mentioned approach is to present a trade-off between these 2 Goals. Simulations have been demonstrated that the said approach can fulfill stated goals effectively.