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Supporting End-to-end Data-propagation Delay Analysis for TSN Networks
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (Heterogeneous systems - hardware software co-design)
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (Heterogeneous systems - hardware software co-design)ORCID iD: 0000-0003-3469-1834
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (Heterogeneous systems - hardware software co-design)
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (Heterogeneous systems - hardware software co-design)ORCID iD: 0000-0001-7586-0409
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2021 (English)Report (Other academic)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Västerås: Mälardalen Real-Time Research Centre, Mälardalen University , 2021.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-56547ISRN: MDH-MRTC-339/2021-1-SEOAI: oai:DiVA.org:mdh-56547DiVA, id: diva2:1612628
Available from: 2021-11-18 Created: 2021-11-18 Last updated: 2021-12-07Bibliographically approved
In thesis
1. Configuring and Analysing TSN Networks Considering Low-priority Traffic
Open this publication in new window or tab >>Configuring and Analysing TSN Networks Considering Low-priority Traffic
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Mälardalen university, 2021. p. 140
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 316
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-56349 (URN)978-91-7485-536-4 (ISBN)
Presentation
2021-12-16, Delta and Zoom, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Available from: 2021-11-09 Created: 2021-11-09 Last updated: 2021-11-25Bibliographically approved

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paper(1888 kB)385 downloads
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Houtan, BaharAshjaei, Seyed Mohammad HosseinDaneshtalab, MasoudSjödin, MikaelMubeen, Saad

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