Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes

Luis Hernan Garcia Paucar, Nelly Bencomo

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

Abstract

Runtime models support decision-making and reasoning for self-adaptation based on both design-time knowledge and information that may emerge at runtime. In this paper, we demonstrate a novel use of Partially Observable Markov Decision Processes (POMDPs) as runtime models to support the decision-making of a Self Adaptive System (SAS) in the context of the MAPE-K loop. The trade-off between the non-functional requirements (NFRs) has been embodied as a POMDP in the context of the MAPE-K loop. Using Bayesian learning, the levels of satisficement of the NFRs are inferred and updated during execution in the form of runtime models in the Knowledge Base. We evaluate our work with a case study of the networking application domain.
Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
PublisherIEEE
Pages11-16
Number of pages6
Volume2019-June
ISBN (Electronic)978-1-7281-2731-6
ISBN (Print)978-1-7281-2732-3
DOIs
Publication statusPublished - 1 Jun 2019
Event2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) - Umea, Sweden
Duration: 16 Jun 201920 Jun 2019

Publication series

Name2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
PublisherIEEE
ISSN (Print)1949-3673
ISSN (Electronic)1949-3681

Conference

Conference2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
Period16/06/1920/06/19

Bibliographical note

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Keywords

  • Markov Processes
  • Runtime models
  • decision making
  • non-functional requirements
  • self-adaptation
  • uncertainty

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  • Cite this

    Garcia Paucar, L. H., & Bencomo, N. (2019). Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes. In Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 (Vol. 2019-June, pp. 11-16). [8780528] (2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)). IEEE. https://doi.org/10.1109/SASO.2019.00011