Synthesizing Job-Level Dependencies for Automotive Multi-Rate Effect Chains

Today's automotive embedded systems comprise a multitude of functionalities, many with complex timing requirements. Besides task specific timing requirements, such applications often have timing requirements for the propagation of data through a chain of tasks. An important metric for control applications is the data age, which is addressed in this paper. The analysis of such systems is non-trivial because tasks involved in the data propagation may execute at different periods, which leads to over and undersampling within one chain. This paper presents a novel method to compute worst-and bestcase end-to-end latencies for such systems. A second contribution synthesizes job-level dependencies for such task sets in a way that data paths which exceed the age constraint are eliminated. An extensive evaluation is performed on synthetic task sets and the applicability to industrial applications is demonstrated in a case study.