Dynamic decision networks for decision-making in self-adaptive systems: a case study

Nelly Bencomo, Amel Belaggoun, Valerie Issarny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.
Original languageEnglish
Title of host publicationSEAMS '13 proceedings of the 8th international symposium on Software Engineering for Adaptive and self-Managing Systems
Place of PublicationPiscataway, NJ (US)
PublisherIEEE
Pages113-122
Number of pages10
ISBN (Electronic)978-1-4673-4401-2
Publication statusPublished - 2013
Event8th international symposium on Software Engineering for Adaptive and self-Managing Systems - San Francisco, CA, United States
Duration: 20 May 201321 May 2013

Symposium

Symposium8th international symposium on Software Engineering for Adaptive and self-Managing Systems
Abbreviated titleSEAMS 2013
CountryUnited States
CitySan Francisco, CA
Period20/05/1321/05/13

Fingerprint

Adaptive systems
Decision making
Decision theory
Bayesian networks
Software engineering
Uncertainty

Keywords

  • self-adaptive systems, dynamic decision networks, bayesian networks, uncertainty modeling.

Cite this

Bencomo, N., Belaggoun, A., & Issarny, V. (2013). Dynamic decision networks for decision-making in self-adaptive systems: a case study. In SEAMS '13 proceedings of the 8th international symposium on Software Engineering for Adaptive and self-Managing Systems (pp. 113-122 ). Piscataway, NJ (US) : IEEE.
Bencomo, Nelly ; Belaggoun, Amel ; Issarny, Valerie. / Dynamic decision networks for decision-making in self-adaptive systems : a case study. SEAMS '13 proceedings of the 8th international symposium on Software Engineering for Adaptive and self-Managing Systems. Piscataway, NJ (US) : IEEE, 2013. pp. 113-122
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Bencomo, N, Belaggoun, A & Issarny, V 2013, Dynamic decision networks for decision-making in self-adaptive systems: a case study. in SEAMS '13 proceedings of the 8th international symposium on Software Engineering for Adaptive and self-Managing Systems. IEEE, Piscataway, NJ (US) , pp. 113-122 , 8th international symposium on Software Engineering for Adaptive and self-Managing Systems, San Francisco, CA, United States, 20/05/13.

Dynamic decision networks for decision-making in self-adaptive systems : a case study. / Bencomo, Nelly; Belaggoun, Amel; Issarny, Valerie.

SEAMS '13 proceedings of the 8th international symposium on Software Engineering for Adaptive and self-Managing Systems. Piscataway, NJ (US) : IEEE, 2013. p. 113-122 .

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Issarny, Valerie

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N2 - Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

AB - Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

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Bencomo N, Belaggoun A, Issarny V. Dynamic decision networks for decision-making in self-adaptive systems: a case study. In SEAMS '13 proceedings of the 8th international symposium on Software Engineering for Adaptive and self-Managing Systems. Piscataway, NJ (US) : IEEE. 2013. p. 113-122