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A Topology-specific Tight Worst-case Analysis of Strict Priority Traffic in Real-time Systems
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-4157-3537
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5269-3900
2023 (English)In: IEEE Int. Conf. Emerging Technol. Factory Autom., ETFA, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Tight end-to-end worst-case delay bounds for periodic traffic streams are essential for time sensitive networks. In this paper, we provide an algorithm to compute a tight (and accurate) end-to-end worst-case bound by considering distinct topological patterns and the manner in which streams enter and leave switches. This refined analysis uses non-preemptive, strict-priority arbitration mechanism commonly deployed in Ethernet switches. Compared to the state-of-the-art that considers all higher and equal priority interference as contributing to the worst-case bound, we present an analytical approach for computing a tighter worst-case delay bound and prove through discrete event simulations that only a certain number of equal-priority interference streams can actually affect the worst-case case. Our results enable efficient resource allocation and have implications for online re-configuration mechanisms for time-sensitive factory communication systems.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023.
Keywords [en]
Real-time networks, Strict-priority traffic, TSN, Worst-case delay, Discrete event simulation, Interactive computer systems, Online systems, Topology, Bad-case delay, Delay bound, End to end, Periodic traffic, Real - Time system, Real time network, Traffic streams, Worst-case analysis, Real time systems
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-64694DOI: 10.1109/ETFA54631.2023.10275348Scopus ID: 2-s2.0-85175426336ISBN: 9798350339918 (print)OAI: oai:DiVA.org:mdh-64694DiVA, id: diva2:1810942
Conference
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Available from: 2023-11-09 Created: 2023-11-09 Last updated: 2023-11-09Bibliographically approved

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Desai, NitinDobrin, RaduPunnekkat, Sasikumar

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