Scheduling of Synchronous Dataflow Graphs with Partially Periodic Real-Time Constraints

Modern Cyber-Physical Systems (CPSs) are composed of numerous components, some of which require real-time management: for example, management of sensors and actuators requires periodic deadlines while processing parts do not. We refer to these systems as partially periodic. In a partially periodic system, precedence constraints may exist between periodic and aperiodic components. It is notably the case in CPSs where sensors measuring physical variables at a fixed sampling rate are typically feeding data to one or more processing part. A critical challenge for any real-time CPS software is its scheduling on an embedded computing platform. The increasing number of cores in such platforms (as Kalray MPPA Bostan having 288 cores) makes offline non-preemptive scheduling techniques efficient to respect real-time constraints, but requires new analysis and synthesis algorithms. In this paper, we study the schedulability of partially periodic systems modeled as Synchronous Data Flow (SDF) graphs. Our contributions are a few necessary conditions on any live SDF graph, and a linearithmic offline non-preemptive scheduling algorithm on vertices of any directed acyclic task graph. The presented algorithm has been evaluated on a set of randomly generated SDF graphs and on one real use-case. Experiments show that our proposed non-preemptive scheduling algorithm allocates thousands of tasks in less than a second. In the last experiment, the computed schedules achieve a throughput close to that one obtained with global Earliest Deadline First (EDF) scheduling.