Methods for Efficient and Adaptive Scheduling of Next-Generation Time-Triggered Networks

Real-time networks play a fundamental role in embedded systems. To meet timing requirements, provide low jitter and bounded latency in such networks the time-triggered communication paradigm is frequently applied in such networks. In this paradigm, a schedule specifying the transmission times of all the traffic is synthesized a priori. Given the steady increase in size and complexity of embedded systems, coupled with the addition of wireless communication, a new time-triggered network model of larger and mixed wired-wireless network isdeveloping. Developing such next-generation networks entails significant research challenges, especially concerning scalability, i.e., allowing generation of schedules of the very large next-generation networks in a reasonable time. A second challenge concerns a well-known limitation of the time-triggered paradigm: its lack of flexibility. Large networks exacerbate this problem, as the number of changes during network operation increases with the number of components, which renders static scheduling approaches unsuitable. In this thesis, we first propose a remedy to the scalability challenge that the synthesis of next-generation network schedules introduces. We propose a family of divide-and-conquer approaches that segment the entire scheduling problem into small enough subproblems that can be effectively and efficiently solved by state-of-the-art schedulers. Second, we investigate how adaptive behaviours can be introduced into the time-triggered paradigm with the implementation of a Self-Healing Protocol. This protocol addresses the flexibility challenge by only updating a small segment of the schedule in response to changes during runtime. This provides a significant advantage compared to current approaches that fully reschedule the network. In the course of our research, we found that our protocol become more effective when the slack in the original schedule is evenly distributed during the schedule synthesis. As a consequence, we also propose a new scheduling approach that maximizes the distances between frames, increasing the success rate of our protocol. The divide-and-conquer approaches developed in this thesis were able to synthesize schedules of two orders of magnitude more traffic and one order of magnitude more nodes in less than four hours. Moreover, when applied to current industrial size networks, they reduced the synthesis time from half an hour to less than one minute compared with state-of-the-art schedulers. The Self-Healing Protocol opened a path towards adaptive time-triggered being able to heal schedules online after link and switch failures in less than ten milliseconds