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AdaptMC: A Control-Theoretic Approach for Achieving Resilience in Mixed-Criticality Systems

Authors Alessandro Vittorio Papadopoulos, Enrico Bini, Sanjoy Baruah, Alan Burns

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Control methods
  • Computer systems organization → Real-time systems
Keywords
  • mixed criticality
  • control theory
  • run-time resilience
  • bounded overloads

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Abstract

A system is said to be resilient if slight deviations from expected behavior during run-time does not lead to catastrophic degradation of performance: minor deviations should result in no more than minor performance degradation. In mixed-criticality systems, such degradation should additionally be criticality-cognizant. The applicability of control theory is explored for the design of resilient run-time scheduling algorithms for mixed-criticality systems. Recent results in control theory have shown how appropriately designed controllers can provide guaranteed service to hard-real-time servers; this prior work is extended to allow for such guarantees to be made concurrently to multiple criticality-cognizant servers. The applicability of this approach is explored via several experimental simulations in a dual-criticality setting. These experiments demonstrate that our control-based run-time schedulers can be synthesized in such a manner that bounded deviations from expected behavior result in the high-criticality server suffering no performance degradation and the lower-criticality one, bounded performance degradation.

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Alessandro Vittorio Papadopoulos, Enrico Bini, Sanjoy Baruah, and Alan Burns. AdaptMC: A Control-Theoretic Approach for Achieving Resilience in Mixed-Criticality Systems. In 30th Euromicro Conference on Real-Time Systems (ECRTS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 106, pp. 14:1-14:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.ECRTS.2018.14

Author Details

Alessandro Vittorio Papadopoulos
  • Mälardalen University, Västerås, Sweden
Enrico Bini
  • University of Turin, Turin, Italy
Sanjoy Baruah
  • Washington University, St. Louis (MO), USA
Alan Burns
  • University of York, York, UK

Funding

This work was partially supported by the Swedish Foundation for Strategic Research under the project "Future factories in the cloud (FiC)" with grant number GMT14-0032. This work is also supported by NSF grants CNS 1409175, CPS 1446631, and CNS 1563845.

Supplementary Materials

References

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