The Grid Backfilling: a Multi-site Scheduling Architecture with Data Mining Prediction Techniques

In large Grids, like the National Grid Service (NGS), or large distributed architecture different scheduling entities are involved. Despite a global scheduling approach would archive higher performance and could increment the utilization of global system in these scenarios usually independent schedulers carry out its own scheduling decisions. In this paper we present how a coordinated scheduling among all the different centers using data mining prediction techniques can substantially improve the performance of the global distributed infrastructure, and can provide a uniform access to the user to all the heterogeneous Grid resources. We present the Grid Backfilling meta-scheduling policy that optimizes the global utilization of the system resources and increases substantially the response time for the jobs. We also present how data mining techniques applied to historical information can provide very suitable inputs for carrying out the Grid Backfilling meta-scheduling decisions.