A Data-Intensive Workflow Scheduling Algorithm for Grid Computing

The data-intensive workflow in scientific and enterprise grids has gained popularity in recent times. Data-intensive workflow needs to access, process and transfer large datasets that may each be replicated on different data hosts. Because of the large data sets, the execution time is bounded by the cost of data transfer. Minimizing the time of transferring these datasets to the computational resources where the tasks of workflow are executed requires that appropriate computational and data resources be selected. In this paper, we introduce an algorithm MDTT to select the resource set which the task should be mapped. Our experiments show that our algorithm is able to minimize the total makespan of data-intensive workflow and the time of data transferring.