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Reasoning About Sensing Uncertainty in Decision-Making for Self-adaptation

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Software Engineering and Formal Methods (SEFM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10729))

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Abstract

Self-Adaptive systems are expected to adapt to unanticipated run-time events using imperfect information about their environment. This entails handling the effects of uncertainties in decision-making, which are not always considered as a first-class concern. This paper contributes a formal analysis technique that explicitly considers uncertainty in sensing when reasoning about the best way to adapt, possibly executing uncertainty reduction operations to improve system utility. We illustrate our approach on a Denial of Service (DoS) attack scenario and present some preliminary results that show the benefits of uncertainty-aware decision-making with respect to using an uncertainty-ignorant approach.

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Notes

  1. 1.

    See Appendix A.2 in [10] for details.

  2. 2.

    A full listing of the PRISM-Games model can be found in [5].

  3. 3.

    CAPTCHA is a type of challenge-response test used in computing to determine whether or not the user is human (https://en.wikipedia.org/wiki/CAPTCHA).

  4. 4.

    In this model, we assume that the effect of tactics is deterministic.

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Acknowledgments

This material is based on research sponsored by AFRL and DARPA under agreement number FA8750-16-2-0042 and by the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the AFRL, DARPA, ONR or the U.S. Government. References herein to any specific commercial product, process, or service by trade name, trade mark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. This material has been approved for public release and unlimited distribution.

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Authors and Affiliations

  1. School of Computer Science, Institute for Software Research, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Javier Cámara, Wenxin Peng, David Garlan & Bradley Schmerl

Authors
  1. Javier Cámara
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  2. Wenxin Peng
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  3. David Garlan
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  4. Bradley Schmerl
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Corresponding author

Correspondence to Javier Cámara .

Editor information

Editors and Affiliations

  1. Nazarbayev University, Astana, Kazakhstan

    Antonio Cerone

  2. Fondazione Bruno Kessler, Povo, Italy

    Marco Roveri

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Cámara, J., Peng, W., Garlan, D., Schmerl, B. (2018). Reasoning About Sensing Uncertainty in Decision-Making for Self-adaptation. In: Cerone, A., Roveri, M. (eds) Software Engineering and Formal Methods. SEFM 2017. Lecture Notes in Computer Science(), vol 10729. Springer, Cham. https://doi.org/10.1007/978-3-319-74781-1_35

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  • DOI: https://doi.org/10.1007/978-3-319-74781-1_35

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