Learning environment model at runtime for self-adaptive systems

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル32nd Annual ACM Symposium on Applied Computing, SAC 2017
出版社Association for Computing Machinery
ページ1198-1204
ページ数7
ISBN(電子版)9781450344869
DOI
出版ステータスPublished - 2017 4 3
イベント32nd Annual ACM Symposium on Applied Computing, SAC 2017 - Marrakesh, Morocco
継続期間: 2017 4 42017 4 6

出版物シリーズ

名前Proceedings of the ACM Symposium on Applied Computing
Part F128005

Other

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

ASJC Scopus subject areas

  • Software

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