AI-Based Scheduling for Adaptive Time-Triggered Networks

Abstract

Time-triggered systems are ideal for safety-critical systems due to the inherent determinism and better fault tolerance. However, the current trend of adaptation in time-triggered systems is typically limited to switching between a small number of precomputed schedules. Artificial neural networks (ANNs) have the potential to overcome this limitation. Adaptation in time-triggered systems requires switching to a new schedule, which satisfies correctness constraints and meets the timing requirements. Computing such a schedule is time consuming, thus in general, not feasible at runtime. ANNs provide the opportunity for learning schedules and thus inferring new schedules at runtime with short delays. However, ANNs have not been sufficiently exploited for optimising time-triggered scheduling. In this paper, an ANN is implemented to learn schedules to provide adaptation for time-triggered systems while ensuring that collision and precedence constraints are met. In our evaluation, the AI-based scheduler is compared with conventional scheduling algorithms such as list scheduling and genetic algorithm in terms of makespan and computation time. The results show the AI-based scheduler's potential when increasing the scheduling problem's complexity.