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

Authors Kunal Agrawal , Sanjoy Baruah , Michael A. Bender , Alberto Marchetti-Spaccamela

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Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
  • Software and its engineering → Scheduling
Keywords
  • Algorithms using predictions
  • robust scheduling
  • energy minimization
  • classification
  • on-line scheduling

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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.

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Kunal Agrawal, Sanjoy Baruah, Michael A. Bender, and Alberto Marchetti-Spaccamela. The Safe and Effective Use of Low-Assurance Predictions in Safety-Critical Systems. In 35th Euromicro Conference on Real-Time Systems (ECRTS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 262, pp. 3:1-3:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ECRTS.2023.3

Author Details

Kunal Agrawal
  • Washington University in Saint Louis, MO, USA
Sanjoy Baruah
  • Washington University in Saint Louis, MO, USA
Michael A. Bender
  • Stony Brook University, NY, USA
Alberto Marchetti-Spaccamela
  • Sapienza Università di Roma, Italy

Funding

  • Baruah, Sanjoy: US National Science Foundation (Grants CPS- 1932530, CNS-2141256, and CNS-2229290)
  • Bender, Michael A.: {US National Science Foundation (Grants CCF-2247577 and CCF-2106827)}
  • Marchetti-Spaccamela, Alberto: ERC 788893 AMDROMA, Sapienza Res. Proj. RM11816436B2F886

References

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