Using historical accounting information to predict the resource usage of grid jobs

Basing job scheduling decisions on estimated queue wait times may help in efficiently balancing the workload on the grid. Previous work on usage prediction has mainly described methods for the estimation of queue wait times on clusters and supercomputers, based on the prediction of the run times of single jobs in a queue. We evaluate the possibility to use the historical information provided by a grid accounting system to predict not only run times of grid jobs, but also other types of resource usage (or resource consumption), hence increasing the parameter space on which job scheduling decisions may be based. For this purpose we analyze three grid accounting datasets from a large-scale production environment and report interesting findings about their characteristics.