Real-time co-scheduling of multiple dataflow graphs on multi-processor systems

It is challenging to schedule multiple dataflow applications concurrently on multi-processor embedded systems with processor sharing. As a viable solution, an approach has been proposed recently, in which the dataflow graphs are transformed into a set of independent realtime tasks. However, it may produce poor resource utilization and excessive buffer usage. Alternatively, we propose a novel two-phase scheduling technique. In the first phase, a set of static schedules is produced for each dataflow considering the resource sharing possibility; Then, we use a meta-heuristic to find the combination of per-graph schedules to minimize the resource requirement by processor sharing. We show that the proposed technique exhibits better resource and buffer efficiency.