Scheduling Cause-Effect Chains without Timing Anomalies in End-to-End Latency

TL;DR

This paper introduces a novel method using Deterministic Data Flow to eliminate timing anomalies in cause-effect chains of real-time systems, enabling precise latency analysis with minimal latency loss.

Contribution

It identifies causes of timing anomalies and proposes the first effective treatment that eliminates them without significant average latency impact, backed by formal proof.

Findings

Reduces maximum end-to-end latency compared to SOTA methods

Effectively eliminates timing anomalies in latency analysis

Maintains negligible average latency loss using DDF

Abstract

In real-time systems, both individual task execution and data propagation must meet strict timing constraints. Cause-effect (CE) chains are widely used to analyze such behaviors by end-to-end latency. However, timing anomalies (TAs) can distort it, where a local reduction in execution times leads to an increase in the overall end-to-end latency. As a result, precisely analyzing the upper bounds of the latency becomes challenging, and such systems typically exhibit larger upper bounds than TA-eliminated systems. Existing studies either eliminate TAs by completely sacrificing average latency to simplify analysis or, despite adopting complex safe analysis methods, do not eliminate TAs effectively, still having high latencies. To address this issue, we identify two basic causes of TAs in end-to-end latency. Based on these causes, we propose the first treatment that eliminates TAs in the latency with negligible average latency loss using Deterministic Data Flow (DDF). We further formally prove its TA-free property. Therefore, we can get a precise upper bound for latency when all jobs execute with their worst-case execution times. Experimental results show that it effectively reduces the maximum end-to-end latency, the average latency, and latency jitter compared with the state-of-the-art (SOTA) method.