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/
Agrawal, Kunal ;
Baruah, Sanjoy ;
Bender, Michael A. ;
Marchetti-Spaccamela, Alberto
The Safe and Effective Use of Low-Assurance Predictions in Safety-Critical Systems
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: |
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Algorithms using predictions, robust scheduling, energy minimization, classification, on-line scheduling |
Collection: |
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35th Euromicro Conference on Real-Time Systems (ECRTS 2023) |
Issue Date: |
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2023 |
Date of publication: |
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03.07.2023 |