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

TL;DR

This paper introduces a schedule-aware LET model that reduces pessimistic end-to-end latencies in multi-rate cause-effect chains while preserving determinism, validated through automotive benchmarks and synthetic tests.

Contribution

It presents a novel method to decrease latency pessimism in the LET model by adjusting task scheduling and dependencies, applicable to subsets of tasks if necessary.

Findings

Significant reduction in worst-case data age.

Notable decrease in worst-case reaction latency.

Effective on real automotive benchmarks and synthetic data.

Abstract

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.