Task execution is heavily affected by the different elements composing real-time systems. Modeling and analyzing such effects would allow reducing the pessimism lying behind the worst-cases. A measurement-based probabilistic approach is developed in order to characterize cache behavior with probabilistic average and worst-case profiles. The approach applies also statistics for studying the impact that different system configurations have on the profiles as well as for evaluating the impact of caches on task execution times. The quality of the probabilistic models is verified through test cases with benchmark tasks running on non time-randomized multi-core real-time systems.
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