On the Improved Hard Real-Time Scheduling of Cyclo-Static Dataflow

Abstract

Recently, it has been shown that the hard real-time scheduling theory can be applied to streaming applications modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. However, this recent approach is not always efficient in terms of throughput and processor utilization. Therefore, in this article, we propose an improved hard real-time scheduling approach to schedule streaming applications modeled as acyclic CSDF graphs on a Multiprocessor 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 that offer fast calculation of the required number of processors for scheduling the tasks. In addition, we propose a method to reduce the graph latency when the converted tasks are scheduled as real-time periodic 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) delivers shorter application latency by applying the proposed method for graph latency reduction while providing better throughput and processor utilization when compared to the existing hard real-time scheduling approach; (3) 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, and so on); and (4) gives the throughput that is equal to 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.