Improved hard real-time scheduling of CSDF-modeled streaming applications

Recently, it has been shown that hard real-time scheduling theory can be applied to streaming applications modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. However, that approach is not efficient in terms of throughput and processor utilization. Therefore, in this paper, we propose an improved hard real-time scheduling approach to schedule streaming applications modeled as acyclic CSDF graphs on a Multi-Processor System-on-Chip (MPSoC) platform. The proposed approach converts each actor in a CSDF graph to a set of real-time periodic tasks. The conversion enables application of many hard real-time scheduling algorithms which offer fast calculation of the required number of processors for scheduling the tasks. We evaluate the performance and time complexity of our approach in comparison to several existing scheduling approaches. Experiments on a set of real-life streaming applications demonstrate that our approach: 1) results in systems with higher throughput and better processor utilization in comparison to the existing hard real-time scheduling approach for CSDF graphs while requiring comparable time for the system derivation; 2) gives the same throughput as the existing periodic scheduling approach for CSDF graphs but requires much shorter time to derive the task schedule and tasks' parameters (periods, start times, etc.); and 3) gives the throughput that is equal or very close to the maximum achievable throughput of an application obtained via self-timed scheduling, but requires much shorter time to derive the schedule. The total time needed for the proposed conversion approach and the calculation of the minimum number of processors needed to schedule the tasks and the calculation of the size of communication buffers between tasks is in the range of seconds.