Grid workflow scheduling based on task and data locations

Grid workflow systems provide mechanisms to execute complex tasks which consist of related sub tasks. Due to the intensive computing and data transferring in Grid workflows, the locations of tasks and data have great impact to the execution performance of Grid workflows. In this paper, we present a novel approach to search for optimal Grid workflow scheduling effectively. We model workflow execution with fetching input data and running tasks, and present a optimized scheduling searching algorithm based on simulated annealing, which can find neighborhood scheduling fast. The experimental results show that our approach is effective and scalable.