Modèles de caractéristiques augmentés de cardinalités relatives

Feature modeling is widely used to capture and manage commonalities and variabilities in software product lines.Cardinality-based feature models are used when variability applies not only to the selection or exclusion of features but also to the number of times a feature can be included in a product.Feature cardinalities are usually considered to apply in local or global scope. However, through our work in managing variability in cloud computing providers, we have identified cases where these interpretations are insufficient to capture the variability of the cloud environment.In this paper, we redefine cardinality-based feature models to allow multiple relative cardinalities between features and discuss the effects of relative cardinalities on cross-tree constraints.To evaluate our approach we conducted an analysis of relative cardinalities in four cloud computing providers.In addition, we developed tools for reasoning on feature models with relative cardinalities and performed experiments to verify the performance and scalability of the approach.The results from our study indicate that extending feature models with relative cardinalities is feasible and improves variability modeling, especially in the case of cloud environments

Une approche basée sur les lignes de produits logiciels pour la configuration et adaptation des environments multi-nuages

Cloud computing is characterized by a model in which computing resources are delivered as services in a pay-as-you-go manner, which eliminates the need for upfront investments, reducing the time to market and opportunity costs. Despite its benefits, cloud computing brought new concerns about provider dependence and data confidentiality, which further led to a growing trend on consuming resources from multiple clouds. However, building multi-cloud systems is still very challenging and time consuming due to the heterogeneity across cloud providers' offerings and the high-variability in the configuration of cloud providers. This variability is expressed by the large number of available services and the many different ways in which they can be combined and configured. In order to ensure correct setup of a multi-cloud environment, developers must be aware of service offerings and configuration options from multiple cloud providers.To tackle this problem, this thesis proposes a software product line-based approach for managing the variability in cloud environments in order to automate the setup and adaptation of multi-cloud environments. The contributions of this thesis enable to automatically generate a configuration or reconfiguration plan for a multi-cloud environment from a description of its requirements. The conducted experiments aim to assess the impact of the approach on the automated analysis of feature models and the feasibility of the approach to automate the setup and adaptation of multi-cloud environments.Le cloud computing est caractérisé par un modèle dans lequel les ressources informatiques sont fournies en tant qu'un service d'utilité, ce qui élimine le besoin de grands investissements initiaux. Malgré ses avantages, le cloud computing a suscité de nouvelles inquiétudes concernant la dépendance des fournisseurs et la confidentialité des données, ce qui a conduit à l'émergence des approches multi-cloud. Cependant, la construction de systèmes multi-cloud est toujours difficile en raison de l'hétérogénéité entre les offres des fournisseurs de cloud et de la grande variabilité dans la configuration des fournisseurs de cloud. Cette variabilité est caractérisé par le grand nombre de services disponibles et les nombreuses façons différentes de les combiner et de les configurer. Afin de garantir la configuration correcte d'un environnement multi-cloud, les développeurs doivent connaître les offres de services et les options de configuration de plusieurs fournisseurs de cloud.Pour traiter ce problème, cette thèse propose une approche basée sur les lignes de produits logiciels pour gérer la variabilité dans les cloud afin d'automatiser la configuration et l'adaptation des environnements multi-cloud. Les contributions de cette thèse permettent de générer automatiquement un plan de configuration ou de reconfiguration pour un environnement multi-cloud à partir d'une description de ses exigences. Les expérimentations menées visent à évaluer l'impact de l'approche sur l'analyse automatisée des modèles de caractéristiques et la faisabilité de l'approche pour automatiser la configuration et l'adaptation des environnements multi-nuages

Extending Feature Models with Relative Cardinalities

International audienceFeature modeling is widely used to capture and manage common-alities and variabilities in software product lines. Cardinality-based feature models are used when variability applies not only to the selection or exclusion of features but also to the number of times a feature can be included in a product. Feature cardinalities are usually considered to apply in either a local or global scope. However , we have identified that these interpretations are insufficient to capture the variability of cloud environments. In this paper, we redefine cardinality-based feature models to allow multiple relative cardinalities between features and we discuss the effects of relative cardinalities on feature modeling semantics, consistency and cross-tree constraints. To evaluate our approach we conducted an analysis of relative cardinalities in four cloud computing providers. In addition, we developed tools for reasoning on feature models with relative cardinalities and performed experiments to verify the performance and scalability of the approach. The results from our study indicate that extending feature models with relative cardinal-ities is feasible and improves variability modeling, particularly in the case of cloud environments

Modèles de caractéristiques augmentés de cardinalités relatives

Feature modeling is widely used to capture and manage commonalities and variabilities in software product lines.Cardinality-based feature models are used when variability applies not only to the selection or exclusion of features but also to the number of times a feature can be included in a product.Feature cardinalities are usually considered to apply in local or global scope. However, through our work in managing variability in cloud computing providers, we have identified cases where these interpretations are insufficient to capture the variability of the cloud environment.In this paper, we redefine cardinality-based feature models to allow multiple relative cardinalities between features and discuss the effects of relative cardinalities on cross-tree constraints.To evaluate our approach we conducted an analysis of relative cardinalities in four cloud computing providers.In addition, we developed tools for reasoning on feature models with relative cardinalities and performed experiments to verify the performance and scalability of the approach.The results from our study indicate that extending feature models with relative cardinalities is feasible and improves variability modeling, especially in the case of cloud environments