Profiling Driven Scenario Detection and Prediction for Multimedia Applications

Modern multimedia applications usually have real-time constraints and they are implemented using heterogeneous multiprocessor systems-on-chip. Dimensioning a system requires accurate estimations of resources needed by the applications. Overestimation leads to over-dimensioning. For a good resource estimation, all the cases in which an application can run must be considered. To avoid an explosion in the number of different cases, those that are similar with respect to required resources are combined into, so called, scenarios. This paper presents a method and a tool that can automatically detect the most important variables from an application and use them to define and dynamically predict scenarios, with respect to the necessary time budget, for soft real-time multimedia applications. The tool was tested for two multimedia applications. Using a proactive scenario-based scheduler based on the scenarios and the runtime predictor generated by our tool, the cycle budget over-estimation decreases with up to 83.50%, paying an acceptable cost of up to 1.74% in the number of missed deadlines