A Scalable Approach for Memory Optimization in AUTOSAR Schedule Tables

Modern embedded automotive software uses AUTOSAR for software development. This software is organized into a set of runnables that represent basic functionality. For optimal resource utilization, runnables are grouped into tasks. In AUTOSAR, a schedule table is used for the deterministic time triggering of task activations or events. In the state of the art, the schedule table is generated at design time. The memory demand of the schedule table is highly sensitive to the application parameters, i.e., the periods and offsets of the runnables and task types. Variations in these parameter values can very significantly increase the memory demand of the schedule table, even for small applications consisting of, e.g., only four runnables. In this paper, we propose an alternative approach for the online generation of the schedule table with an upper bound on the memory demand that is insensitive to variations in the values of the periods and offsets of runnables, and task types. Using multiple case studies, we show that our approach, despite a slight runtime overhead, significantly reduces memory demand.