Scientific workflow scheduling in computational grids — Planning, reservation, and data/network-awareness

A very important issue in executing a scientific workflow in computational grids is how to map and schedule workflow tasks onto multiple distributed resources and handle task dependencies in a timely manner to deliver users' expected performance. In this paper, we present our work to develop and evaluate an advanced workflow scheduler in computational grid environments, the GRACCE scheduler. The GRACCE scheduler applies advanced scheduling techniques, such as resource negotiation and reservation, data/network-aware scheduling and performance prediction in the resource allocation and execution planning process. To evaluate the scheduler, we have set up an experimental environment that models a computational grid in those aspects relevant to workflow scheduling. Our results show the average performance improvement, using the GRACCE scheduler, is about 20% under high resource loads.