Using Performance Prediction to Allocate Grid Resources

Large-scale applications often require computational grids to obtain the needed compute power for execution. Generally, users are given access to a collection of resources that can be used for execution. The collection of resources can be dynamic, and the users must decide which collection of heterogeneous and distributed resources to use. However, many users often do not have knowledge of all of the resource performance characteristics on which to make informed decisions and therefore need automated tools to perform the mapping of jobs to the available resources. In this paper, we present a resource planner system that uses performance prediction, based upon historical data, to identify the appropriate resources. This system is used with a gravitational-wave physics experiment, LIGO, for which the initial results indicate an average of 24% reduction in execution time using the performance prediction versus a random selection of resources.