License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ECRTS.2023.3
URN: urn:nbn:de:0030-drops-180323
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18032/
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Agrawal, Kunal ; Baruah, Sanjoy ; Bender, Michael A. ; Marchetti-Spaccamela, Alberto

The Safe and Effective Use of Low-Assurance Predictions in Safety-Critical Systems

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LIPIcs-ECRTS-2023-3.pdf (1.0 MB)

Abstract

The algorithm-design paradigm of algorithms using predictions is explored as a means of incorporating the computations of lower-assurance components (such as machine-learning based ones) into safety-critical systems that must have their correctness validated to very high levels of assurance. The paradigm is applied to two simple example applications that are relevant to the real-time systems community: energy-aware scheduling, and classification using ML-based classifiers in conjunction with more reliable but slower deterministic classifiers. It is shown how algorithms using predictions achieve much-improved performance when the low-assurance computations are correct, at a cost of no more than a slight performance degradation even when they turn out to be completely wrong.

BibTeX - Entry

@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2023.3,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Bender, Michael A. and Marchetti-Spaccamela, Alberto},
  title =	{{The Safe and Effective Use of Low-Assurance Predictions in Safety-Critical Systems}},
  booktitle =	{35th Euromicro Conference on Real-Time Systems (ECRTS 2023)},
  pages =	{3:1--3:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-280-8},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{262},
  editor =	{Papadopoulos, Alessandro V.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18032},
  URN =		{urn:nbn:de:0030-drops-180323},
  doi =		{10.4230/LIPIcs.ECRTS.2023.3},
  annote =	{Keywords: Algorithms using predictions, robust scheduling, energy minimization, classification, on-line scheduling}
}

Keywords: Algorithms using predictions, robust scheduling, energy minimization, classification, on-line scheduling
Collection: 35th Euromicro Conference on Real-Time Systems (ECRTS 2023)
Issue Date: 2023
Date of publication: 03.07.2023

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