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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.
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https://hal.inria.fr/inria-00632269
Contributor : Lionel Seinturier <>
Submitted on : Saturday, May 12, 2012 - 12:48:32 AM
Last modification on : Wednesday, August 26, 2020 - 10:30:08 AM
Long-term archiving on: : Tuesday, December 13, 2016 - 6:54:20 PM

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  • HAL Id : inria-00632269, version 1

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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⟩

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