With an increasing number of applications, real-time embedded systems are gaining in size and complexity. Many of these systems are complex as a whole, but consist of smaller modules interacting with each other. This structure makes them amenable to compositional design. For real-time systems, compositional design is done using models consisting of components arranged in a scheduling hierarchy. Analysis of such systems depends on the choice of the task model and the interfaces used to abstract component timing requirements.
Real-time applications have been traditionally modeled either as periodic and sporadic tasks which are easy to analyze but simplistic, or as task graphs and automata models that are very expressive, but complex to analyze, especially with respect to compositional analysis. We develop conditional task models with a view to claim the middle ground. We show that these models, while being expressive enough to capture conditional release of jobs, or dependencies between tasks, also allow for efficient analysis. We establish results for checking schedulability of task sets comprising of conditional tasks, and techniques to compositionally analyze a hierarchical resource sharing system where each component comprises of conditional tasks.
Schedulability analysis of tasks is based on the assumption that the task parameters such as execution requirements and inter-arrival times between jobs are known exactly. In most cases however, only an estimate of these quantities is available at the time of analysis. If the task parameters turn out to be better than those considered, then the analysis should be sustainable with the new parameter values. If the task parameters turn out to be worse off, then the analysis should be robust enough to tolerate some deterioration. With this view, we introduce and address sustainability and robustness questions for analysis with conditional tasks.
Finally, the introduced models and compositional analysis techniques are illustrated with an automotive case study. The study clearly demonstrates the utility of introduced techniques over previous approaches for compositional analysis.
