Indexed on: 21 Jan '06Published on: 21 Jan '06Published in: Journal of Grid Computing
Large-scale applications can be expressed as a set of tasks with data dependencies between them, also known as application workflows. Due to the scale and data processing requirements of these applications, they require Grid computing and storage resources. So far, the focus has been on developing easy to use interfaces for composing these workflows and finding an optimal mapping of tasks in the workflow to the Grid resources in order to minimize the completion time of the application. After this mapping is done, a workflow execution engine is required to run the workflow over the mapped resources. In this paper, we show that the performance of the workflow execution engine in executing the workflow can also be a critical factor in determining the workflow completion time. Using Condor as the workflow execution engine, we examine the various factors that affect the completion time of a fine granularity astronomy workflow. We show that changing the system parameters that influence these factors and restructuring the workflow can drastically reduce the completion time of this class of workflows. We also examine the effect on the optimizations developed for the astronomy application on a coarser granularity biology application. We were able to reduce the completion time of the Montage and the Tomography application workflows by 90% and 50%, respectively.