Quantitative characterization of event streams in analysis of hard real-time applications

Many real-time embedded systems process event streams which are composed of a finite number of different event types. Each different event type on the stream would typically impose a different workload to the system, and thus the knowledge of possible correlations and dependencies between the different event types could be exploited to get tighter analytic performance estimations of the complete system. We propose an abstract stream model to characterize such an event stream. The model captures the needed information of all possible traces of a class of event streams and can hence be used to obtain hard bounded worst-case and best-case estimations of a system. We show how the proposed abstract stream model can be obtained from a concrete stream specification, and how it can be used for performance analysis. The applicability of our approach and its advantages over traditional worst-case performance analysis are shown in a case study of a multimedia application.