Abstract:

Real-Time Systems often use multiprocessor architectures to meet timing constraints. On the other hand, machine learning has been being increasingly applied across various domains. This paper focuses on Schedulability analysis for real-time tasks on multiprocessors, using a integrated model melding Decision Tree and Tabu Search algorithms. This approach confirms schedulability, reducing computation time and optimizing Software and Hardware space exploration processes. We validated our method using real system dataset.