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

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Towards building information modelling for existing structures

The transformation of cities from the industrial age (unsustainable) to the knowledge age (sustainable) is essentially a ‘whole life cycle’ process consisting of; planning, development, operation, reuse and renewal. During this transformation, a multi-disciplinary knowledge base, created from studies and research about the built environment aspects is fundamental: historical, architectural, archeologically, environmental, social, economic, etc is critical. Although there are a growing number of applications of 3D VR modelling applications, some built environment applications such as disaster management, environmental simulations, computer aided architectural design and planning require more sophisticated models beyond 3D graphical visualization such as multifunctional, interoperable, intelligent, and multi-representational. Advanced digital mapping technologies such as 3D laser scanner technologies can be are enablers for effective e-planning, consultation and communication of users’ views during the planning, design, construction and lifecycle process of the built environment. For example, the 3D laser scanner enables digital documentation of buildings, sites and physical objects for reconstruction and restoration. It also facilitates the creation of educational resources within the built environment, as well as the reconstruction of the built environment. These technologies can be used to drive the productivity gains by promoting a free-flow of information between departments, divisions, offices, and sites; and between themselves, their contractors and partners when the data captured via those technologies are processed and modelled into BIM (Building Information Modelling). The use of these technologies is key enablers to the creation of new approaches to the ‘Whole Life Cycle’ process within the built and human environment for the 21st century. The paper describes the research towards Building Information Modelling for existing structures via the point cloud data captured by the 3D laser scanner technology. A case study building is elaborated to demonstrate how to produce 3D CAD models and BIM models of existing structures based on designated technique

Challenges for Automatic Multi-Cloud Configuration

National audienceMulti-cloud computing enables customers to exploit benefits of different cloud provi-ders to optimize reliability, performance and costs. Meanwhile, using multiple cloud providers reduces the risk of vendor lock-in as customers reduce their reliance on provider specific fea-tures. However, the large number of available cloud provider offerings and the differences among them makes it very complex to choose the best combination of cloud providers to deploy an application. Feature models from Software Product Line Engineering have been used to describe variability in cloud provider offerings and automatically generate valid cloud config-urations. In this paper we explore the challenges that must be faced to extend the use of feature models to automatically configure multi-cloud environments.La multitude des offres de nuages permet aux clients d'exploiter les avantages de chaque fournisseur pour optimiser la fiabilité, la performance et les coûts des logiciels dé-ployés. En même temps, l'usage de fournisseurs multiples de nuages réduit le risque d'être dé-pendant des caractéristiques spécifiques d'un fournisseur. Néanmoins, le grand nombre d'offres de fournisseurs de nuages, et leurs différences, rendent très difficile le choix d'une combinaison optimale de fournisseurs pour deployer une application. Les modèles de caractéristiques issus de l'ingénierie des lignes de produits logiciels ont déjà été utilisés pour décrire la variabilité parmi les offres des fournisseurs de nuage et pour générer automatiquement des configurations valides. Dans cet article, nous explorons les défis qui doivent être abordés pour étendre cette approche en vue de configurer automatiquement des environements de type multi-nuages

Using Constraint Reasoning on Feature Models to Populate Ecosystem-driven Cloud Services e- Marketplace

Service providers leverage cloud ecosystems and cloud e-marketplaces to increase the business value of their services and reach a wider range of service users. A cloud ecosystem enable participating services to combine with other services, along their QoS properties; while the e-marketplace provides an environment where atomic services interconnect in unprecedented ways to be traded on the marketplace platform. Noting the unprofitability, impracticality and error-prone nature of performing ad hoc service combination of atomic services, the concern addressed in this technical report is how to guide the combination of atomic services participating in an ecosystem in a seamless manner. In this technical report, we proposed the use of feature models to model the inter-relationships and constraints among the atomic services, which is transformed into a constraint satisfaction problem and off-the-shelve constraint solvers are used to determining valid combinations. The collection of valid combinations become the blueprint that guides service composition and populates the e-marketplace service directory; users can then make service selection decisions based on the list. The applicability of the approach proposed in this report is demonstrated via an example of Customer relationship management as a service ecosystem

Orthogonal variability modeling to support multi-cloud application configuration

Cloud service providers benefit from a vast majority of customers due to variability and making profit from commonalities between the cloud services that they provide. Recently, application configuration dimensions has been increased dramatically due to multi-tenant, multi-device and multi-cloud paradigm. This challenges the configuration and customization of cloud-based software that are typically offered as a service due to the intrinsic variability. In this paper, we present a model-driven approach based on variability models originating from the software product line community to handle such multi-dimensional variability in the cloud. We exploit orthogonal variability models to systematically manage and create tenant-specific configuration and customizations. We also demonstrate how such variability models can be utilized to take into account the already deployed application parts to enable harmonized deployments for new tenants in a multi-cloud setting. The approach considers application functional and non-functional requirements to provide a set of valid multi-cloud configurations. We illustrate our approach through a case study