Graph Neural Networks Based Meta-scheduling in Adaptive Time-Triggered Systems
Samer Alshaer, Carlos Lua, Pascal Muoka, Daniel Chidiebere Onwuchekwa, Roman Obermaisser · 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) · 2022
Meta-scheduling algorithms are used for adaptation in time-triggered systems as they adapt to different scenarios such as failures or different environmental conditions. Most meta-scheduling algorithms demand a considerable amount of storage space from the host cyber-physical system due to the state-space explosion problem in covering a reasonable number of scenarios. This work deploys the Graph Neural Network (GNN) to learn the multi-schedules from the meta-scheduling algorithm required for adaptation. The GNN is used to learn the scheduling mechanism of a Genetic Algorithm (GA) so that at runtime, adaptation is achieved using the GNN model. We further investigate the impact of modifiable tasks during a meta-scheduling operation on the overall makespan. Finally, a comparison of the makespans is made between a List Scheduler (LS), GA and the proposed GNN-based technique to evaluate the impact of our approach. Our proposed GNN-based approach outperforms the LS scheduler as the number of modifiable tasks increases. The results show that the proposed GNN-based meta-scheduling can be suitable for real-time scenario adaptation in cyber-physical systems.