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Designing and simulating large networks, based on the Time-Sensitive Networking (TSN) standards, require complex and demanding configuration at the design and pre-simulation phases. The existing configuration and simulation frameworks support only the manual configuration of TSN networks. This hampers the applicability of these frameworks to large-sized TSN networks, especially in complex industrial embedded system applications. This paper proposes a modular framework to automate offline scheduling in TSN networks to facilitate the design time and pre-simulation automated network configurations as well as interpretation of the simulations. To demonstrate and evaluate the applicability of the proposed framework, a large TSN network is automatically configured and its performance is evaluated by measuring end-to-end delays of time-critical flows in a state-of-the-art simulation framework, namely NeSTiNg.

The IEEE Time-Sensitive Networking (TSN) standards' amendment 802.1Qbv provides real-time guarantees for Scheduled Traffic (ST) streams by the Time Aware Shaper (TAS) mechanism. In this paper, we develop offline schedule optimization objective functions to configure the TAS for ST streams, which can be effective to achieve a high Quality of Service (QoS) of lower priority Best-Effort (BE) traffic. This becomes useful if real-time streams from legacy protocols are configured to be carried by the BE class or if the BE class is used for value-added (but non-critical) services. We present three alternative objective functions, namely Maximization, Sparse and Evenly Sparse, followed by a set of constraints on ST streams. Based on simulated stream traces in OMNeT++/INET TSN NeSTiNg simulator, we compare our proposed schemes with a most commonly applied objective function in terms of overall maximum end-to-end delay and deadline misses of BE streams. The results confirm that changing the schedule synthesis objective to our proposed schemes ensures timely delivery and lower end-to-end delays in BE streams.

This paper presents a schedulability analysis for the Best-Effort (BE) traffic class within Time-Sensitive Networking (TSN) networks. The presented analysis considers several features in the TSN standards, including the Credit-Based Shaper (CBS), the Time-Aware Shaper (TAS), and the frame preemption. Although the BE class in TSN is primarily used for the traffic with no strict timing requirements, some industrial applications prefer to utilize this class for the non-hard real-time traffic instead of classes that use the CBS. The reason mainly lies in the fact that the complexity of TSN configuration becomes significantly high when the time-triggered traffic via the TAS and other classes via the CBS are used altogether. We demonstrate the applicability of the presented analysis on a vehicular application use case. We show that a network designer can get information on the schedulability of the BE traffic, based on which the network configuration can be further refined with respect to the application requirements. 

End-to-end data-propagation delay analysis allows verification of important timing constraints, such as age and reaction, that areoften specified on chains of tasks and messages in real-time systems.We identify that the existing analysis does not support distributed taskchains that include the Time-Sensitive Networking (TSN) messages. Tothis end, this paper extends the existing analysis to allow the end-to-endtiming analysis of distributed task chains that include TSN messages.The extended analysis supports all types of traffic in TSN, includingthe Scheduled Traffic (ST), Audio Video Bridging (AVB), and BestEffort (BE) traffic. Furthermore, the extended analysis accounts for thesynchronization among the end stations that are connected via TSN.The applicability of the analysis is demonstrated using an automotiveapplication case study.