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EMSOFT '07 Proceedings of the 7th ACM & IEEE international conference on Embedded software
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Automatic dataflow model extraction from modal real-time stream processing applications
Proceedings of the 14th ACM SIGPLAN/SIGBED conference on Languages, compilers and tools for embedded systems
Exploiting just-enough parallelism when mapping streaming applications in hard real-time systems
Proceedings of the 50th Annual Design Automation Conference
A methodology for automated design of hard-real-time embedded streaming systems
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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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 application is typically modeled as a directed graph where nodes represent 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 applications modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. In such graphs, the actors are data-dependent, however, we analytically 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 compare 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.