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  • 1.
    Houtan, Bahar
    Mälardalen University, School of Innovation, Design and Engineering.
    Configuration and Timing Analysis of TSN-based Distributed Embedded Systems2024Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The set of IEEE Time-Sensitive Networking (TSN) standards is an emerging candidate for backbone communication in modern applications of real-time distributed embedded systems. TSN provides various traffic shaping mechanisms that aim at managing the timing requirements of traffic. Emerging applications of these systems, particularly in the automotive domain, often run complex distributed software that requires low-latency and high-bandwidth communication across multiple onboard electronic control units. Using TSN in these systems introduces multiple challenges. Specifically, the developers of these systems face a lack of development techniques and tools, as TSN standards only offer general recommendations for the use of its features and mechanisms. There is an urgent need for development techniques, tools, and methods to assist the developers in effectively leveraging the features outlined in TSN standards. In this thesis, we identify and address several challenges encountered in the development of TSN-based distributed embedded systems, particularly focusing on the stages of system and software modeling, network configuration, and timing analysis. The overall goal of this thesis is to support the development of these systems in the aforementioned stages while considering the Quality of Service (QoS) requirements of all traffic classes in TSN. We present techniques to facilitate the system and software modeling of TSN-based distributed embedded systems. These techniques enable performing timing analysis in the early stages of system and software development. In the stage of network configuration, we propose techniques for managing the configuration complexity and supporting the automatic configuration of mechanisms in TSN. The proposed configuration techniques consider achieving acceptable QoS in various traffic classes. In the stage of timing analysis, we address the challenges of incorporating various TSN traffic classes and mechanisms by extending the existing timing analyses. The results indicate that the proposed techniques effectively facilitate the system and software modeling, network configuration, and timing analysis of TSN-based distributed embedded systems.

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  • 2.
    Houtan, Bahar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Configuring and Analysing TSN Networks Considering Low-priority Traffic2021Licentiate 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.

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  • 3.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Mälardalen University.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Afshar, S.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Schedulability Analysis of Best-Effort Traffic in TSN Networks2021In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers (IEEE), 2021Conference paper (Other academic)
    Abstract [en]

    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. 

  • 4.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Developing Predictable Vehicular Embedded Systems Utilizing Time-Sensitive Networking–A Research Plan2019In: 15th Swedish National Computer Networking Workshop (SNCNW'19) SNCNW 2019, 2019Conference paper (Refereed)
  • 5.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Supporting end-to-end data propagation delay analysis for TSN-based distributed vehicular embedded systems2023In: Journal of systems architecture, ISSN 1383-7621, E-ISSN 1873-6165, Vol. 141, article id 102911Article in journal (Refereed)
    Abstract [en]

    In this paper, we identify that the existing end-to-end data propagation delay analysis for distributed embedded systems can calculate pessimistic (over-estimated) analysis results when the nodes are synchronized. This is particularly the case of the Scheduled Traffic (ST) class in Time-sensitive Networking (TSN), which is scheduled offline according to the IEEE 802.1Qbv standard and the nodes are synchronized according to the IEEE 802.1AS standard. We present a comprehensive system model for distributed embedded systems that incorporates all of the above mentioned aspect as well as all traffic classes in TSN. We extend the analysis to support both synchronization and non-synchronization among the ECUs as well as offline schedules on the networks. The extended analysis can now be used to analyze all traffic classes in TSN when the nodes are synchronized without introducing any pessimism in the analysis results. We evaluate the proposed model and the extended analysis on a vehicular industrial use case.

  • 6.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Supporting End-to-end Data-propagation Delay Analysis for TSN Networks2021Report (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. 

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  • 7.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Synthesising Schedules to Improve QoS of Best-effort Traffic in TSN Networks2021In: 29th International Conference on Real-Time Networks and Systems (RTNS'21) RTNS 2021, 2021, p. 68-77Conference paper (Refereed)
    Abstract [en]

    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.

  • 8.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bandwidth Reservation Analysis for Schedulability of AVB Traffic in TSNManuscript (preprint) (Other academic)
  • 9.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Aybek, M. O.
    Arcticus Systems, Järfälla, Sweden.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lundbäck, J.
    Arcticus Systems, Järfälla, Sweden.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    End-to-end Timing Modeling and Analysis of TSN in Component-Based Vehicular Software2023In: Proc. - IEEE Int. Symp. Real-Time Distrib. Comput., ISORC, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 126-135Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an end-to-end timing model to capture timing information from software architectures of distributed embedded systems that use network communication based on the Time-Sensitive Networking (TSN) standards. Such a model is required as an input to perform end-to-end timing analysis of these systems. Furthermore, we present a methodology that aims at automated extraction of instances of the end-to-end timing model from component-based software architectures of the systems and the TSN network configurations. As a proof of concept, we implement the proposed end-to-end timing model and the extraction methodology in the Rubus Component Model (RCM) and its tool chain Rubus-ICE that are used in the vehicle industry. We demonstrate the usability of the proposed model and methodology by modeling a vehicular industrial use case and performing its timing analysis.

  • 10.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Aybek, M. O.
    Arcticus Systems, Järfälla, Sweden.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    End-to-end Timing Model Extraction from TSN-Aware Distributed Vehicle Software2022In: Proc. - Euromicro Conf. Softw. Eng. Adv. Appl., SEAA, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 366-369Conference paper (Refereed)
    Abstract [en]

    Extraction of end-to-end timing information from software architectures of vehicular systems to support their timing analysis is a daunting challenge. To address this challenge, this paper presents a systematic method to extract this information from vehicular software architectures that can be distributed over several electronic control units connected by Time-Sensitive Networking (TSN) networks. As a proof of concept, the proposed extraction method is applied to an industrial component model, namely the Rubus Component Model (RCM), and its toolchain. Furthermore, the usability of the proposed method is demonstrated in an industrial use case from the vehicular domain.

  • 11.
    Houtan, Bahar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bergström, Albert
    Mälardalen University.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    An Automated Configuration Framework for TSN Networks2021In: 22nd IEEE International Conference on Industrial Technology (ICIT'21) ICIT 2021, 2021, p. 771-778Conference paper (Refereed)
    Abstract [en]

    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.

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