Reconstruction-Based Adaptive Scheduling Using AI Inferences in Safety-Critical Systems

--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. T o 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. Safety-critical time-triggered systems (TTS) are commonly used in areas like automotive, aviation, industrial automation, and medical devices, where operations must be predictable and reliable. These systems rely on carefully designed schedules that specify exact times for tasks to run and messages to be sent, ensuring deterministic behavior. However, real-world situations can introduce unexpected events such as hardware failures, variations in task execution times (slack), or changes in operational modes. As a result, these systems must adapt quickly and effectively to maintain safety and performance [1] [2]. Metascheduling is a widely adopted solution to provide adaptability in time-triggered systems. Unlike traditional static scheduling, metascheduling involves creating multiple pre-computed schedules designed to handle different anticipated scenarios such as hardware failures, task execution slacks, or mode transitions.