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.
@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/entities/document/10.4230/LIPIcs.ECRTS.2023.3}, 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} }
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