Efficient services composition for grid-enabled data-intensive applications

Grids technologies enable the deployment of complex data-intensive scientific applications. Nonspecific scientific codes may benefit from grid computing capabilities by (i) assembling codes in workflows (code parallelism) and (ii) processing large amounts of data (data parallelism). We designed MOTEUR, a service-based workflow manager, to describe data-intensive scientific applications in a compact framework and to efficiently process the resulting computations by transparently exploiting different parallelism levels. Theoretical performances are analyzed and results are shown based on a real application to medical image databases processing