Schedulable Job-Level Dependencies for Cause-Effect Chains via Graph Neural Networks

• URL: http://arxiv.org/abs/2607.02624v1
• Date: Thu, 02 Jul 2026 11:48:10 GMT
• Title: Schedulable Job-Level Dependencies for Cause-Effect Chains via Graph Neural Networks
• Abstract summary: Job-level dependencies (JLDs) provide a schedule-agnostic mechanism for bounding the data age independently of the underlying scheduler.<n>We propose the first machine-learning-based JLD synthesis method, built around a two-level Graph Neural Network with temperature-controlled sampling.<n>We show that the ML-based generator substantially outperforms the original greedy generator while achieving orders-of-magnitude lower synthesis time.
• Score: 2.0568360952522164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern automotive software architectures comprise large sets of mixed-criticality functions executing on shared multi-core platforms with strict real-time and end-to-end timing requirements. Sensor-to-actuator data propagation in such systems is typically expressed via cause-effect chains with worst-case data-age budgets. Job-level dependencies (JLDs) have been introduced to provide a schedule-agnostic mechanism for bounding the data age independently of the underlying scheduler. The state-of-the-art methods for synthesizing JLDs, however, do not check whether the produced JLDs are enforceable under a concrete scheduling policy or jointly schedulable at the system level. In this paper we propose the first machine-learning-based JLD synthesis method, built around a two-level Graph Neural Network with temperature-controlled sampling that learns the structural patterns connecting cause-effect chain configurations to their JLD solutions. Since learned outputs may not be correct by construction, we embed the GNN in a novel Generate-and-Verify architecture in which a safe DP data-age checker, together with a per-chain EDF feasibility checker and a system-level demand-bound test, accept or reject each candidate. We show that the ML-based generator substantially outperforms the original greedy heuristic while achieving orders-of-magnitude lower synthesis time, demonstrating that learned structural priors can effectively replace exponential propagation-tree enumeration on this class of real-time scheduling problems.

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