On Evaluating JARS: Job history-based Adaptive grid resource selection

Grids have ensured to solve a large-scale complex scientific problem with utilizing many heterogeneous resources that are geographically separated. However, as grid resources and scientific applications become diverse, the difficulty of their execution management and control has been increased. In this paper, we propose JARS (Job history-based Adaptive grid Resource Selection) system and evaluate with different scenarios. Specifically, statistical technique, Plackett-Burman design with fold-over, is applied on analyzing grid environments and job history. Through JARS system, accumulated knowledge of applications' execution can be utilized in practical resource selection. As a proof-of-concept, we analyzed job history from an aerospace research grid system and obtained characteristics of grid resources and applications. The simulations present that resources with acceptable performance and reliability are selected under heavy resource failures.