Multi-criteria Grid Resource Management using Performance Prediction Techniques

To date, many of existing Grid resource brokers make their decisions concerning selection of the best resources for computational jobs using basic resource parameters such as, for instance, load. This approach may often be insufficient. Estimations of job start and execution times are needed in order to make more adequate decisions and to provide better quality of service for endusers. Nevertheless, due to heterogeneity of Grids and often incomplete information available the results of performance prediction methods may be very inaccurate. Therefore, estimations of prediction errors should be also taken into consideration during a resource selection phase. We present in this paper the multi-criteria resource selection method based on estimations of job start and execution times, and prediction errors. To this end, we use GRMS [28] and GPRES tools. Tests have been conducted based on workload traces which were recorded from a parallel machine at UPC. These traces cover 3 years of job information as recorded by the LoadLeveler batch management systems. We show that the presented method can considerably improve the efficiency of resource selection decisions.