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Practical Task Allocation for Software Fault-Tolerance and Its Implementation in Embedded Automotive Systems
Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA.
Linköping University, Department of Computer and Information Science, Software and Systems. General Motors, USA.
Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA.
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Due to the advent of active safety features and automated driving capabilities, the complexity of embedded computing systems within automobiles continues to increase. Such advanced driver assistance systems (ADAS) are inherently safety-critical and must tolerate failures in any subsystem. However, fault-tolerance in safety-critical systems has been traditionally supported by hardware replication, which is prohibitively expensive in terms of cost, weight, and size for the automotive market. Recent work has studied the use of software-based fault-tolerance techniques that utilize task-level hot and cold standbys to tolerate fail-stop processor and task failures. The benefit of using standbys is maximal when a task and any of its standbys obey the placement constraint of not being co-located on the same processor. We propose a new heuristic based on a "tiered" placement constraint, and show that our heuristic produces a better task assignment that saves at least one processor up to 40% of the time relative to the best known heuristic to date. We then introduce a task allocation algorithm that, for the first time to our knowledge, leverages the run-time attributes of cold standbys. Our empirical study finds that our heuristic uses no more than one additional processor in most cases relative to an optimal allocation that we construct for evaluation purposes using a creative technique. We have designed and implemented our software fault-tolerance framework in AUTOSAR, an automotive industry standard. We use this implementation to provide an experimental evaluation of our task-level fault-tolerance features. Finally, we present an analysis of the worst-case behavior of our task recovery features.

Place, publisher, year, edition, pages
IEEE, 2017.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-141591DOI: 10.1109/RTAS.2017.33ISBN: 978-1-5090-5269-1 (electronic)ISBN: 978-1-5090-5270-7 (print)OAI: oai:DiVA.org:liu-141591DiVA, id: diva2:1146086
Conference
Real-Time and Embedded Technology and Applications Symposium
Available from: 2017-10-02 Created: 2017-10-02 Last updated: 2018-01-13

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Publisher's full texthttp://ieeexplore.ieee.org/abstract/document/7939023/

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CiteExportLink to record
Permanent link

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