Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

Danny Weyns, Bradley Schmerl, Masako Kishida, Alberto Leva, Marin Litoiu, Necmiye Ozay, Colin Paterson, Kenji Tei

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

5 Citations (Scopus)

Abstract

Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.

Original languageEnglish
Title of host publicationProceedings - 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages217-223
Number of pages7
ISBN (Electronic)9781665402897
DOIs
Publication statusPublished - 2021 May
Event2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021 - Virtual, Online
Duration: 2021 May 182021 May 24

Publication series

NameProceedings - 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021

Conference

Conference2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021
CityVirtual, Online
Period21/5/1821/5/24

Keywords

  • Cloud enterprise system
  • MAPE
  • Self-adaptive systems
  • control theory
  • machine learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence
  • Control and Optimization
  • Software
  • Information Systems and Management
  • Industrial and Manufacturing Engineering

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