Compositional Timing Analysis of Asynchronized Distributed Cause-effect Chains

Abstract

Real-time systems require the formal guarantee of timing constraints, not only for the individual tasks but also for the end-to-end latency of data flows. The data flow among multiple tasks, e.g., from sensors to actuators, is described by a cause-effect chain, independent from the priority order of the tasks. In this article, we provide an end-to-end timing-analysis for cause-effect chains on asynchronized distributed systems with periodic task activations, considering the maximum reaction time (MRT) (i.e., the duration of data processing) and the maximum data age (MDA) (i.e., the worst-case data freshness). We first provide an analysis of the end-to-end latency on one local electronic control unit (ECU) that has to consider only the jobs in a bounded time interval. We extend our analysis to globally asynchronized systems by exploiting a compositional property to combine the local results. Throughout synthesized data based on an automotive benchmark as well as on randomized parameters, we show that our analytical results improve the state-of-the-art.