Reconstruction-Based Adaptive Scheduling Using AI Inferences in Safety-Critical Systems
This provides a practical solution for safety-critical systems (like automotive or aerospace) where reliable real-time scheduling is essential, though it appears incremental as it builds on existing scheduling methods with new validation mechanisms.
The paper tackles the problem of generating safe, executable schedules for time-triggered systems in dynamic environments, where traditional methods risk collisions and invalid schedules. The proposed reconstruction framework transforms AI-generated priorities into validated schedules, significantly enhancing adaptability, integrity, and performance while maintaining computational efficiency.
Adaptive scheduling is crucial for ensuring the reliability and safety of time-triggered systems (TTS) in dynamic operational environments. Scheduling frameworks face significant challenges, including message collisions, locked loops from incorrect precedence handling, and the generation of incomplete or invalid schedules, which can compromise system safety and performance. To address these challenges, this paper presents a novel reconstruction framework designed to dynamically validate and assemble schedules. The proposed reconstruction models operate by systematically transforming AI-generated or heuristically derived scheduling priorities into fully executable schedules, ensuring adherence to critical system constraints such as precedence rules and collision-free communication. It incorporates robust safety checks, efficient allocation algorithms, and recovery mechanisms to handle unexpected context events, including hardware failures and mode transitions. Comprehensive experiments were conducted across multiple performance profiles, including makespan minimisation, workload balancing, and energy efficiency, to validate the operational effectiveness of the reconstruction models. Results demonstrate that the proposed framework significantly enhances system adaptability, operational integrity, and runtime performance while maintaining computational efficiency. Overall, this work contributes a practical and scalable solution to the problem of safe schedule generation in safety-critical TTS, enabling reliable and flexible real-time scheduling even under highly dynamic and uncertain operational conditions.