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Using Constraint-based Optimization and Variability to Support Continuous Self-Adaptation

Abstract

Self-adaptation is one of the upcoming paradigms that accurately tackles nowadays systems complexity. In this context, Dynamic Software Product Lines model the intrinsic variability of a family of systems, and dynamically support their reconfiguration according to updated context. However, when several configurations are available for the same context, making a decision about the right one is a hard challenge: further dimensions such as QoS are needed to enrich the decision making process. In this paper, we propose to combine variability with Constraint-Satisfaction Problem techniques to face this challenge. The approach is illustrated and validated with a context-driven system used to support the control of a home through mobile devices.

Category

Academic chapter

Language

English

Author(s)

  • Carlos Andres Parra
  • Daniel Romero
  • Sébastien Mosser
  • Romain Rouvoy
  • Laurence Duchien
  • Lionel Seinturier

Affiliation

  • SINTEF Digital / Sustainable Communication Technologies
  • The French National Institute for Research in Computer Science and Control

Year

2012

Publisher

Association for Computing Machinery (ACM)

Book

Proceedings of the 27th Annual ACM Symposium on Applied Computing

ISBN

9781450308571

Page(s)

486 - 491

View this publication at Norwegian Research Information Repository