https://www.mdu.se/

Probabilistic timing analysis techniques have been proposed for real-time systems to remedy the problems that deterministic estimates of the task's Worst-Case Execution Time and Worst-Case Response-Time can be both intractable and overly pessimistic. Often, assumptions are made that a task's response time and execution time probability distributions are independent of the other tasks. This assumption may not hold in real systems. In this paper, we analyze the timing behavior of a simple periodic task on a Raspberry Pi model 3 running Arch Linux ARM. In particular, we observe and analyze the distributions of wake-up latencies and execution times for the sequential jobs released by a simple periodic task. We observe that the timing behavior of jobs is affected by release events during the job's execution time, and of other processes running in between subsequent jobs of the periodic task. Using a data consistency approach we investigate whether it is reasonable to model the timing distribution of jobs affected by release events and intermediate processes as translations of the empirical timing distribution of non-affected jobs. According to the analysis, this paper shows that a translated distribution model of non-affected jobs is invalid for the execution time distribution of jobs affected by intermediate processes. Regarding the wake-up latency distribution with intermediate processes, a translated distribution model is improbable, but cannot be completely ruled out.