Probabilistic Timing Analysis of Distributed Real-time Automotive Systems

Probabilistic Timing Analysis of Distributed Real-time Automotive Systems by Haibo Zeng Doctor of Philosophy in Engineering-Electrical Engineering and Computer Sciences University of California, Berkeley Professor Alberto L. Sangiovanni-Vincentelli, Chair Distributed architectures supporting the execution of real-time applications are common in automotive systems. Many applications, including most of those developed for active safety and chassis systems, do not impose hard real-time deadlines. Nevertheless, they are sensitive to the latencies of the end-to-end computations from sensors to actuators. We believe a characterization of the timing metrics that, not only provides the worst case bound, but assigns a probability to each possible latency value, is very desirable to estimate the quality of an architecture configuration. In this dissertation, we present stochastic analysis frameworks that calculate the probability distributions of response times for software tasks and messages, and end-to-end latencies in a Controller Area Network based system for the performance evaluation of automotive distributed architectures. Also, the regression technique is used to quickly characterize the message response time probability distribution, which is suitable when only part of the message set is known as in the early design stage. The applicability of the analysis frameworks is validated by either simulation, or trace data extracted