Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Most of the hard-real-time scheduling theory for multiprocessor systems assumes independent periodic or sporadic tasks. Such a simple task model is not directly applicable to modern embedded streaming applications. This is because a modern streaming appli-cation is typically modeled as a directed graph where nodes rep-resent actors (i.e. tasks) and edges represent data-dependencies. The actors in such graphs have data-dependency constraints and do not necessarily conform to the periodic or sporadic task models. Therefore, in this paper we investigate the applicability of hard-real-time scheduling theory for periodic tasks to streaming appli-cations modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. In such graphs, the actors are data-dependent, however, we analyt-ically prove that they (i.e. the actors) can be scheduled as implicit-deadline periodic tasks. As a result, a variety of hard-real-time scheduling algorithms for periodic tasks can be applied to schedule such applications with a certain guaranteed throughput. We com-pare the throughput resulting from such scheduling approach to the maximum achievable throughput of an application for a set of 19 real streaming applications. We find that in more than 80 % of the cases, the throughput resulting from our approach is equal to the maximum achievable throughput