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The Logical Execution Time (LET) model has been gaining industrial attentionbecause of its timing and data-flow deterministic characteristics, whichsimplify the computation of end-to-end latencies of multi-rate cause-effectchains at the cost of pessimistic latencies. In this paper, we propose a novelmethod to reduce the pessimism in the latencies introduced by LET, whilemaintaining its determinism. We propose a schedule-aware LET model thatshortens the lengths and repositions LET's communication intervals resulting inless pessimistic end-to-end latencies. By adding dependencies between specifictask instances, the method can further reduce the pessimism in the latencycalculations of the LET model. If needed, e.g., for legacy reasons, our methodcan be applied to a subset of tasks only. We evaluate our work based on realworld automotive benchmarks and randomly generated synthetic task sets. Wecompare our results with previous work and the LET model. The experiments showsignificant reductions of worst-case data age and worst-case reaction latencyvalues.

Reducing End-to-End Latencies of Multi-Rate Cause-Effect Chains for the LET Model