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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27th ACM Symposium on Applied Computing (SAC'12), 7th Dependable and Adaptive Distributed Systems (DADS) Track, Mar 2012, Trento, Italy. pp.486-491, 2012
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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, 2012. 〈inria-00632269〉

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