Optimizing End-to-End Latencies by Adaptation of the Activation Events in Distributed Automotive Systems

Schedulability theory provides support for the analysis of the worst case latencies in distributed computations when the architecture of the system is known and the communication and synchronization mechanisms have been defined. In the design of complex automotive systems, however, a great benefit of schedulability analysis may come from its use as an aid in the exploration of the software architecture configurations that can best support the target application. We present an optimization algorithm that leverages the trade-offs between the purely periodic and the data-driven activation models to meet the latency requirements of distributed vehicle functions. We demonstrate its effectiveness on a complex automotive architecture.