Configuring and Analysing TSN Networks Considering Low-priority Traffic

Sammanfattning: The IEEE Time-Sensitive Networking (TSN) standards offer a promising solution to deal with the challenge of supporting high-bandwidth, low-latency, and predictable communication in distributed embedded systems. Although TSN provides a gate mechanism to support the low-jitter transmission of high-priority time-triggered traffic, it also brings complexity to the network design as the configuration of such mechanism together with support for low-priority transmission is non-trivial. Moreover, the combination of the gate mechanism and the Credit-based Shaper (CBS) mechanism in TSN deals with many configuration parameters, hence finding the most suitable configuration is complex. To avoid this complexity, the Best-effort (BE) class is sometimes used as an alternative channel to the classes that undergo the CBS mechanism, through which the real-time traffic without strict deadlines is transmitted with a minimum level of Quality of Service (QoS). On the other hand, the end stations that operate based on the legacy communication standards might not support the TSN's traffic shaping mechanisms, hence the designers need to assign the legacy traffic to use the BE class in a TSN network. To the extent of our knowledge, there is no implicit mechanism to support the QoS of BE in a TSN network. Hence, utilizing BE as an alternative to other classes must be guaranteed in terms of meeting the timing requirements, i.e., response times and end-to-end delays. Therefore, the work in this thesis aims at developing techniques and solutions to support the QoS of the lower-priority classes in TSN. In this regard, this work improves the scheduling solutions of high-priority time-triggered traffic to reduce the latency of BE traffic and develops techniques to verify the timing properties of BE traffic considering the impact of all other traffic classes in TSN. Furthermore, the work in this thesis extends the existing end-to-end data-propagation delay analysis for distributed real-time systems based on TSN networks. Finally, the applicability of the proposed techniques is verified and demonstrated by automotive application use cases.