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Worst-Case Cause-Effect Reaction Latency in Systems with Non-Blocking Communication
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
2019 (English)In: Design, Automation & Test in Europe Conference & Exhibition: DATE 2019, IEEE, 2019, p. 1625-1630Conference paper, Published paper (Refereed)
Abstract [en]

In real-time embedded systems, a system functionality is often implemented using a data-flow chain over a set of communicating tasks. A critical non-functional requirement in such systems is to restrict the amount of time, i.e. cause-effect latency, for an input to impact its corresponding output. The problem of estimating the worst-case cause-effect latency is well-studied in the context of blocking inter-task communication. Recent research results show that non-blocking communication preserving functional semantics is critical for the model-based design of dynamically updatable systems. In this paper, we study the worst-case cause-effect reaction latency estimation problem in the context of non-blocking inter-task communication. We present a computationally efficient algorithm that tightly over-approximates the exact worst-case reaction latency in cause-effect data-flow chains.

Place, publisher, year, edition, pages
IEEE, 2019. p. 1625-1630
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-389931DOI: 10.23919/DATE.2019.8715264ISI: 000470666100301ISBN: 978-3-9819263-2-3 (electronic)OAI: oai:DiVA.org:uu-389931DiVA, id: diva2:1340006
Conference
DATE 2019, March 25–29, Florence, Italy
Available from: 2019-08-01 Created: 2019-08-01 Last updated: 2019-10-07Bibliographically approved

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Abdullah, JakariaDai, GaoyangWang, Yi

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