Reducing End-to-End Latencies of Multi-Rate Cause-Effect Chains for the LET Model
The Logical Execution Time (LET) model has been gaining industrial attention because of its timing and data-flow deterministic characteristics, which simplify the computation of end-to-end latencies of multi-rate cause-effect chains at the cost of pessimistic latencies. In this paper, we propose a novel method to reduce the pessimism in the latencies introduced by LET, while maintaining its determinism. We propose a schedule-aware LET model that shortens the lengths and repositions LET's communication intervals resulting in less pessimistic end-to-end latencies. By adding dependencies between specific task instances, the method can further reduce the pessimism in the latency calculations of the LET model. If needed, e.g., for legacy reasons, our method can be applied to a subset of tasks only. We evaluate our work based on real world automotive benchmarks and randomly generated synthetic task sets. We compare our results with previous work and the LET model. The experiments show significant reductions of worst-case data age and worst-case reaction latency values.
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