Learning environment model at runtime for self-adaptive systems

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

4 Citations (Scopus)

Abstract

Self-adaptive systems alter their behavior in response to environmental changes to continually satisfy their requirements. Self-adaptive systems employ an environment model, which should be updated during runtime to maintain consistency with the real environment. Although some techniques have been proposed to learn environment model based on execution traces at the design time, these techniques are time consuming and consequently inappropriate for runtime learning. Herein, a technique using a stochastic gradient descent and the difference in the data acquired during the run-time is proposed as an efficient learning environment model. The computational time and accuracy of our technique are verified through a case study.

Original languageEnglish
Title of host publication32nd Annual ACM Symposium on Applied Computing, SAC 2017
PublisherAssociation for Computing Machinery
Pages1198-1204
Number of pages7
ISBN (Electronic)9781450344869
DOIs
Publication statusPublished - 2017 Apr 3
Event32nd Annual ACM Symposium on Applied Computing, SAC 2017 - Marrakesh, Morocco
Duration: 2017 Apr 42017 Apr 6

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F128005

Other

Other32nd Annual ACM Symposium on Applied Computing, SAC 2017
CountryMorocco
CityMarrakesh
Period17/4/417/4/6

Keywords

  • Gradient descent
  • Learning
  • Self-adaptive

ASJC Scopus subject areas

  • Software

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  • Cite this

    Tanabe, M., Tei, K., Fukazawa, Y., & Honiden, S. (2017). Learning environment model at runtime for self-adaptive systems. In 32nd Annual ACM Symposium on Applied Computing, SAC 2017 (pp. 1198-1204). (Proceedings of the ACM Symposium on Applied Computing; Vol. Part F128005). Association for Computing Machinery. https://doi.org/10.1145/3019612.3019776