Abstract
Dataflow models can be used to model and program concurrent systems and applications. Static timed dataflow models commonly abstract the temporal behavior of systems in terms of their worst-case behaviors. This may lead to models that are very pessimistic. The scenario methodology can be applied to the dataflow modeling approach to group similar dynamic behaviors into static dataflow behaviors that abstract the system scenarios in a tight fashion. Constraints on the possible scenario transitions in the system can be modeled, among other options, by a finite state automaton. This approach leads to a model called scenario-aware dataflow (SADF) that is presented in this chapter. We introduce the model and its semantics and discuss its fundamental analysis techniques. We discuss a parameterized extension and its analysis. We discuss a dataflow programming model and its implementation challenges. We give an overview of refined analysis techniques and run-time exploitation possibilities of SADF.
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