Genetic Algorithm for Scheduling Time-Triggered Communication Networks with Data Compression

End-to-end timing requirements and resource constraints are the main challenges in scheduling communication networks for distributed real-time systems. In this paper, we propose a genetic algorithm to solve the scheduling problem of a time-triggered communication network with built-in compression. The proposed scheduling model considers the possibility of combining correlated streams and reducing their size with the proposed compression method. Under this model, the highly correlated streams are selected to be compressed and their corresponding computation jobs are executed on the same end-systems. This method leads to a significant reduction of transmission times and end-to-end delays. We perform experiments with different scenarios to evaluate the efficiency of the algorithm and the applied compression method in minimizing the critical path delay. The experimental results show considerable improvements in total makespan and prove that the proposed model outperforms the model without data compression.