A Set Coverage-based Mapping Heuristic for Scheduling Distributed Data-Intensive Applications on Global Grids

Data-intensive grid applications need access to large datasets that may each be replicated on different resources. Minimizing the overhead of transferring these datasets to the resources where the applications are executed requires that appropriate computational and data resources be selected. In this paper, we introduce a heuristic for the selection of resources based on a solution to the set covering problem (SCP). We then pair this mapping heuristic with the well-known MinMin scheduling algorithm and conduct performance evaluation through extensive simulations