Vers un Outil de Configuration et de Déploiement pour les Nuages

National audienceCloud Computing is a major trend in distributed computing environments enabling software virtualization on configurable runtime platforms. Development and deployment of Cloud software systems result in new challenges to express and manage their intrinsic variability. Many configuration and customization choices arise due to the heterogeneous and scalable aspect of the Cloud Computing paradigm. Features Model originating from Software Product Line (SPL) approach is one way to handle this variability, manage, create and deploy configuration in the Cloud. In this paper, we introduce SALOON, a framework to configure and describe variability for applications to be deployed in the Cloud. Based on ontologies and extended features models, SALOON takes application's technical and non-functional requirements into consideration to provide the most appropriate cloud solutions.L'informatique dans les nuages est une tendance actuelle majeure pour répartir les traitements et les données de façon virtuelle sur des environnements d'exécution paramétrables. Le développement et le déploiement de logiciels pour les nuages proposent un nouveau chal- lenge scientifique en termes d'expression et de prise en compte de la variabilité. En effet, l'informatique dans les nuages repose sur des principes d'hétérogénéité et d'élasticité, ce qui permet de nombreux choix de configuration et de dimensionnement. Les Modèles de Caractéristiques (MC) issus de l'approche Ligne de Produits Logiciels (LPL) sont une réponse possible pour gérer cette variabilité, préparer et dimensionner des configurations à déployer dans les nuages. Dans cet article, nous introduisons SALOON, un cadre logiciel d'expression de la variabilité et d'aide à la décision pour configurer et dimensionner des applications à déployer dans les nuages. Basé sur des ontologies et des MCS étendus, il prend en compte les aspects techniques et non-fonctionnels de l'application pour trouver un fournisseur de nuages qui correspond au mieux à la configuration de l'application

Cardinality-Based Feature Models With Constraints: A Pragmatic Approach

International audienceFeature models originating from Software Product Line Engineering are a well-known approach to variability modeling. In many situations, the variability does not apply only on features but also on the number of times these features can be cloned. In such a case, cardinality-based feature models are used to specify the number of clones for a given feature. Although previous works already investigated approaches for feature modeling with cardinality, there is still a lack of support for constraints in the presence of clones. To overcome this limitation, we present an abstract model to define constraints in cardinality-based feature models and propose a formal semantics for this kind of constraints. We illustrate the practical usage of our approach with examples from our recent experiences on cloud computing platform configuration

Choisir son Nuage à l'Aide des Modèles de Caractéristiques

National audienceIl existe de nombreuses solutions d'informatique dans les nuages (cloud computing). Nous proposons une approche tirant profit des modèles de caractéristiques pour choisir la solution qui répond au mieux aux besoins techniques et non fonctionnels de l'application à déployer

SALOON, a Platform for Selecting and Configuring Cloud Environments

International audienceCloud computing has recently emerged as a major trend in distributed computing. This layered model enables the configuration of many computing resources that can be provisioned to support the deployment of applications, provided as Software-as-a-Service (SaaS). Many cloud providers, either at Infrastructure (IaaS) or Platform (PaaS) level, propose different services and pricing models. We propose SALOON, a platform for selecting and configuring cloud environments

Handling Constraints in Cardinality-Based Feature Models: The Cloud Environment Case Study

Feature modeling is a well-known approach to describe variability in Software Product Lines. Cardinality-based Feature Models (FMs) is a type of FMs where features can be instantiated several times in the configuration, contrarily to boolean FMs where a feature is present or not. While boolean FMs configuration is easily handled by current approaches, there is still a lack of support regarding cardinality-based FMs. In particular, expressing constraints over the set of feature instances is not supported in current approaches, where cardinality involved in such constraints can not be specified. To face this limitation, we define in this paper cardinality-based expressions and provide the related formal syntax and semantics as well as the way to automate the underlying configuration. We study the need for such a support using cloud computing environment configurations as a motivating example. To evaluate the soundness of the proposed approach, we analyze a corpus of 10 cloud environments. Our empirical evaluation shows that constraints relying on our cardinality-based expressions are common and that our approach is effective and can provide an useful support to developers for modeling and reasoning about FMs with cardinalities.La modélisation à l'aide de caractéristiques est une approche très utilisée dans les lignes de produits logiciels. Les Modèles de Caractéristiques (MCs) étendus avec des cardinalités sont un des MCs dans lesquels une caractéristique peut être instanciée plusieurs fois lors de la configuration, contrairement au MCs booléens dans lesquels une caractéristique est présente ou non. Alors que la configuration de MCs booléens est aujourd'hui maitrisée par différentes approches, il reste cependant un manque en terme de support pour les MCs étendus avec des cardinalités. Notamment, pouvoir exprimé des contraintes sur le nombre d'instances requises n'est pas permis dans les approches existantes, puisque les contraintes ne peuvent être exprimées que sur des caractéristiques booléennes. Pour contrer cette limite, nous fournissons dans cet article une nouvelle notation pour exprimer ces contraintes, une définition formelle de leur syntaxe et de leur sémantique ainsi qu'un moyen d'automatiser la vérification des configurations associées. Pour illustrer notre approche, nous étudions le besoin pour un tel support dans le cadre de la configuration d'environnements d'informatique dans les nuages. Nous évaluons notre approche sur un ensemble de 10 environnements. Notre étude empirique montre que les besoins pour exprimer ce type de contraintes sont communs dans ces environnements et que notre approche est efficace pour les gérer

Automated Selection and Configuration of Cloud Environments Using Software Product Lines Principles

International audienceDeploying an application to a cloud environment has recently become very trendy, since it offers many advantages such as improving reliability or scalability. These cloud environments provide a wide range of resources at different levels of functionality, which must be appropriately configured by stakeholders for the application to run properly. Handling this variability during the configuration and deployment stages is a complex and error-prone process, usually made in an ad hoc manner in existing solutions. In this paper, we propose a software product lines based approach to face these issues. Combined with a domain model used to select among cloud environments a suitable one, our approach supports stakeholders while configuring the selected cloud environment in a consistent way, and automates the deployment of such configurations through the generation of executable deployment scripts. To evaluate the soundness of the proposed approach, we conduct an experiment involving 10 participants with different levels of experience in cloud configuration and deployment. The experiment shows that using our approach significantly reduces time and most importantly, provides a reliable way to find a correct and suitable cloud configuration. Moreover, our empirical evaluation shows that our approach is effective and scalable to properly deal with a significant number of cloud environments

Leveraging Feature Models to Configure Virtual Appliances

International audienceCloud computing is a major trend in distributed computing environments. Software virtualization technologies allow cloud Infrastructure-as-a-Service (IaaS) providers to instantiate and run a large number of virtual appliances. However, one of the major challenges is to reduce the disk space footprint of such virtual appliances to improve their storage and transfer across cloud servers. In this paper, we propose to use a Software Product Line (SPL) approach and describe the virtual appliance as a set of common and variable elements modeled by means of Feature Model (FM). We describe a solution to reverse engineer a FM from a virtual appliance and we show how we take advantage of the SPL configuration mechanisms to signifi cantly reduce the size of a virtual appliance

Towards Multi-Cloud Configurations Using Feature Models and Ontologies

International audienceConfiguration and customization choices arise due to the heterogeneous and scalable aspect of the cloud computing paradigm. To avoid being restricted to a given cloud and ensure application requirements, using several clouds to deploy a multi-cloud configuration is recommended but introduces several challenges due to the amount of providers and their intrinsic variability. In this paper, we present a model-driven approach based on Feature Models originating from Software Product Lines to handle cloud variability and then manage and create cloud configurations. We combine it with ontologies, used to model the various semantics of cloud systems. The approach takes into consideration application technical requirements as well as non-functional ones to provide a set of valid cloud or multi-cloud configurations and is implemented in a framework named Saloon

Using Multiple Feature Models to Design Applications for Mobile Phones

International audienceThe design of a mobile phone application is a tedious task according to its intrinsic variability. Software designers must take into account in their development process the versatility of available platforms (e.g., Android, iPhone). In addition to this, the variety of existing devices and their divergences (e.g., frontal camera, GPS) introduce another layer of com- plexity in the development process. These two dimensions can be formalized as Software Product Lines (SPL), inde- pendently defined. In this paper, we use a dedicated meta- model to bridge the gap between an application SPL and a mobile device one. This meta-model is also the support for the product derivation process. The approach is imple- mented in a framework named ApplIDE, and is used to successfully derive customer relationship management soft- ware on different devices

Rapport de prospective sur l'interopérabilité dans le monde du Cloud et du SaaS

Ce document présente une solution pour la configuration et le déploiement d'applications dans un environnement de cloud computing. La solution permet de : (1) découpler l'applicatif à déployer de l'environnement dans lequel il sera déployer, (2) spécifier les besoins de l'applicatif nécessaires à son bon déploiement, (3) spécifier les caractéristiques des offres d'hébergement, (4) permettre le calcul de la correspondance entre les besoins et les offres d'hébergement, (5) générer le script qui permet de déployer un applicatif sur une offre d'hébergement. Cette solution est mise en oeuvre dans l'outil Saloon dont les fonctionnalités sont présentées dans ce livrable. Saloon utilise des techniques de lignes de produits logiciels, d'ontologies et de modèles de caractéristiques pour atteindre les cinq objectifs énoncés ci-dessus