As large-scale computational applications in various scientific domains have been utilized over many integrated sets of grid computing resources, the difficulty of their execution management and control has increased. It is beneficial to refer job history from many application executions, in order to identify application's characteristics and to decide grid resource selection policies meaningfully. In this paper, we apply a statistical technique, Plackett-Burman design with fold-over, for analyzing grid environments and execution history of applications. It identifies main factors in grid environments and applications, ranks based on how much they affect. Especially, the effective factors could be used for future resource selection. Through this process, application is performed on the selected resource and the result is added to job history. We analyzed job history from an aerospace research grid system. The effective key factors were identified and applied to resource selection policy.
['Cinyoung Hur', 'Yoonhee Kim']
Adaptive grid resource selection based on job history analysis using Plackett-Burman designs