Using constraint-based optimization and variability to support continuous self-adaptation

TL;DR: This paper proposes to combine variability with Constraint-Satisfaction Problem techniques to face the challenge of self-adaptation and is illustrated and validated with a context-driven system used to support the control of a home through mobile devices.

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.