Using Constraint-based Optimization and Variability to Support Continuous Self-Adaptation - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2012

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.
Fichier principal
Vignette du fichier
sac-cspvar.pdf (360.71 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00632269 , version 1 (12-05-2012)

Identifiers

  • HAL Id : inria-00632269 , version 1

Cite

Carlos Andrés Parra, Daniel Romero, Sébastien Mosser, Romain Rouvoy, Laurence Duchien, et al.. Using Constraint-based Optimization and Variability to Support Continuous Self-Adaptation. 27th ACM Symposium on Applied Computing (SAC'12), 7th Dependable and Adaptive Distributed Systems (DADS) Track, Mar 2012, Trento, Italy. pp.486-491. ⟨inria-00632269⟩
259 View
281 Download

Share

Gmail Facebook X LinkedIn More