Abstract
The processing graph method (PGM) is a widely used framework for modeling applications with producer/consumer precedence constraints. PGM was originally developed by the U.S. Navy to model signal-processing applications where data communications exist among connected tasks. Prior work has shown how to schedule PGM-specified systems on uniprocessors and globally-scheduled multiprocessors. In this paper, this work is extended to enable such systems to be supported in a distributed collection of multicore machines. In such a context, pure global and partitioned scheduling approaches are problematic. Moreover, data communication costs must be considered. In this paper, a clustered scheduling algorithm is proposed for soft real-time PGM-specified distributed task systems for which bounded deadline tardiness is acceptable. This algorithm is effective in reducing data communication costs with little utilization loss. This is shown both analytically and via experiments conducted to compare it with an optimal integer linear programming solution.