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Partitioned and overhead-aware scheduling of mixed-criticality real-time systems
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. General Motors, USA.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)In: 24th Asia and South Pacific Design Automation Conference, New York: Association for Computing Machinery (ACM), 2019, p. 39-44Conference paper, Published paper (Refereed)
Abstract [en]

Modern real-time embedded and cyber-physical systems comprise a large number of applications, often of different criticalities, executing on the same computing platform. Partitioned scheduling is used to provide temporal isolation among tasks with different criticalities. Isolation is often a requirement, for example, in order to avoid the case when a low criticality task overruns or fails in such a way that causes a failure in a high criticality task. When the number of partitions increases in mixed criticality systems, the size of the schedule table can become extremely large, which becomes a critical bottleneck due to design time and memory constraints of embedded systems. In addition, switching between partitions at runtime causes CPU overhead due to preemption. In this paper, we propose a design framework comprising a hyper-period optimization algorithm, which reduces the size of schedule table and preserves schedulability, and a re-scheduling algorithm to reduce the number of preemptions. Extensive experiments demonstrate the effectiveness of proposed algorithms and design framework.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2019. p. 39-44
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-160489DOI: 10.1145/3287624.3287653Scopus ID: 2-s2.0-85061136107ISBN: 978-1-4503-6007-4 (print)OAI: oai:DiVA.org:liu-160489DiVA, id: diva2:1353803
Conference
Asia and South Pacific Design Automation Conference, Tokyo, Japan, January 21 - 24, 2019
Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-10-02Bibliographically approved

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Samii, SoheilEles, PetruPeng, Zebo

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf