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

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

doi:10.1145/2245276.2245370

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

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.

Original language	English
Title of host publication	27th Annual ACM Symposium on Applied Computing, SAC 2012
Pages	486-491
Number of pages	6
DOIs	https://doi.org/10.1145/2245276.2245370
State	Published - 2012
Externally published	Yes
Event	27th Annual ACM Symposium on Applied Computing, SAC 2012 - Trento, Italy Duration: 26 Mar 2012 → 30 Mar 2012

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

Conference

Conference	27th Annual ACM Symposium on Applied Computing, SAC 2012
Country/Territory	Italy
City	Trento
Period	26/03/12 → 30/03/12

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10.1145/2245276.2245370

Cite this

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title = "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.",

author = "Carlos Parra and Daniel Romero and S{\'e}bastien Mosser and Romain Rouvoy and Laurence Duchien and Lionel Seinturier",

year = "2012",

doi = "10.1145/2245276.2245370",

language = "English",

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series = "Proceedings of the ACM Symposium on Applied Computing",

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}

Parra, C, Romero, D, Mosser, S, Rouvoy, R, Duchien, L & Seinturier, L 2012, Using constraint-based optimization and variability to support continuous self-adaptation. in 27th Annual ACM Symposium on Applied Computing, SAC 2012. Proceedings of the ACM Symposium on Applied Computing, pp. 486-491, 27th Annual ACM Symposium on Applied Computing, SAC 2012, Trento, Italy, 26/03/12. https://doi.org/10.1145/2245276.2245370

Using constraint-based optimization and variability to support continuous self-adaptation. / Parra, Carlos; Romero, Daniel; Mosser, Sébastien et al.
27th Annual ACM Symposium on Applied Computing, SAC 2012. 2012. p. 486-491 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Seinturier, Lionel

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AB - 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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ER -

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

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