Learning-based adaptation determination method for problem recognition of self-adaptive software

Kwangsoo Seol, Doo Kwon Baik

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

    Abstract

    In this paper, we propose a method for identifying the adaptation period when a problem occurs in a system in order to reduce the unnecessary adaptation of self-adaptive software. Consequently, the dangerous situation information is defined, the behavior information at the time of problem occurrence is learned, and the adaptive performance is determined by comparing it with the existing similar situations by using the k-nearest neighbors algorithm. By the use of the proposed method, a situation where an unnecessary adaptation process is performed while running the self-adaptive system could be avoided, system load may be reduced, and service quality may be enhanced.

    Original languageEnglish
    Title of host publicationProceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015
    EditorsDavid de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Hamid R. Arabnia
    PublisherCSREA Press
    Pages399-400
    Number of pages2
    ISBN (Electronic)1601324073, 9781601324078
    Publication statusPublished - 2019
    Event2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015 - Las Vegas, United States
    Duration: 2015 Jul 272015 Jul 30

    Publication series

    NameProceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015

    Conference

    Conference2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015
    Country/TerritoryUnited States
    CityLas Vegas
    Period15/7/2715/7/30

    Bibliographical note

    Funding Information:
    This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2012M3C4A7033346). Doo-Kwon Baik is corresponding author

    Publisher Copyright:
    © 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.

    Keywords

    • Machine learning
    • Problem recognition
    • Self-adaptive software

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software

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