Clément Quinton

Towards multi-cloud configurations using feature models and ontologies

Configuration 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 (FMs) originating from Software Product Lines (SPL) 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.

Cardinality-based feature models with constraints: a pragmatic approach

Feature 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.

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

Deploying 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

Cloud 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 significantly reduce the size of a virtual appliance.

Feature model differences

Feature models are a widespread means to represent commonality and variability in software product lines. As is the case for other kinds of models, computing and managing feature model differences is useful in various real-world situations. In this paper, we propose a set of novel differencing techniques that combine syntactic and semantic mechanisms, and automatically produce meaningful differences. Practitioners can exploit our results in various ways: to understand, manipulate, visualize and reason about differences. They can also combine them with existing feature model composition and decomposition operators. The proposed automations rely on satisfiability algorithms. They come with a dedicated language and a comprehensive environment. We illustrate and evaluate the practical usage of our techniques through a case study dealing with a configurable component framework.

Using multiple feature models to design applications for mobile phones

The 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 complexity in the development process. These two dimensions can be formalized as Software Product Lines (SPL), independently defined. In this paper, we use a dedicated metamodel 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 implemented in a framework named Applide, and is used to successfully derive customer relationship management software on different devices.

SALOON: a platform for selecting and configuring cloud environments

Migrating legacy systems or deploying a new application to a cloud environment has recently become very trendy, because the number of cloud providers available is still increasing. 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 known as a complex and error‐prone process, usually made in an ad hoc manner. In this paper, we propose SALOON, a software product lines‐based platform to face these issues. We describe the architecture of the SALOON platform, which relies on feature models combined with a domain model used to select among cloud environments a well‐suited one. SALOON supports stakeholders while configuring the selected cloud environment in a consistent way and automates the deployment of such configurations through the generation of executable configuration scripts. This paper also reports on some experiments, showing that using SALOON significantly reduces time to configure a cloud environment compared with a manual approach and provides a reliable way to find a correct and suitable configuration. Moreover, our empirical evaluation shows that our approach is effective and scalable to properly deal with a significant number of cloud environments. Copyright

Consistency checking for the evolution of cardinality-based feature models

Feature-models (fms) are a widely used approach to specify the commonalities and variability in variable systems and software product lines. Various works have addressed edits to fms for fm evolution and tool support to ensure consistency of fms. An important extension to fms are feature cardinalities and related constraints, as extensively used e.g., when modeling variability of cloud computing environments. Since cardinality-based fms pose additional complexity, additional support for evolution and consistency checking with respect to feature cardinalities would be desirable, but has not been addressed yet. In this paper, we discuss common cardinality-based fm edits and resulting inconsistencies based on experiences with fms in cloud domain. We introduce tool-support for automated inconsistency detection and explanation based on an off-the-shelf solver. We demonstrate the feasibility of the approach by an empirical evaluation showing the performance of the tool.

SPLEMMA: a generic framework for controlled-evolution of software product lines

Managing in a generic way the evolution process of feature-oriented Software Product Lines (spls) is complex due to the number of elements that are impacted and the heterogeneity of the spls regarding artifacts used to define them. Existing work presents specific approaches to manage the evolution of spls in terms of such artifacts, i.e., assets, feature models and relation definitions. Moreover stakeholders do not necessarily master all the knowledge of the spl making its evolution difficult and error-prone without a proper tool support. In order to deal with these issues, we introduce SPLEmma, a generic framework that follows a Model Driven Engineering approach to capture the evolution of a spl independently of the kind of assets, technologies or feature models used for the product derivation. Authorized changes are described by the spl maintainer and captured in a model used to generate tools that guide the evolution process and preserve the consistency of the whole spl. We report on the application of our approach on two spls: YourCast for digital signage systems, and SALOON, which enables generation of configurations for cloud providers.

Evolution in dynamic software product lines: challenges and perspectives

In many domains systems need to run continuously and cannot be shut down for reconfiguration or maintenance tasks. Cyber-physical or cloud-based systems, for instance, thus often provide means to support their adaptation at runtime. The required flexibility and adaptability of systems suggests the application of Software Product Line (spl) principles to manage their variability and to support their reconfiguration. Specifically, Dynamic Software Product Lines (dspl) have been proposed to support the management and binding of variability at runtime. While spl evolution has been widely studied, it has so far not been investigated in detail in a dspl context. Variability models that are used in a dspl have to co-evolve and be kept consistent with the systems they represent to support reconfiguration even after changes to the systems at runtime. In this short paper we present a classification of the required operations for jointly evolving problem and solution space in a dspl. We analyze the impact of such operations on the consistency of a dspl and propose an approach to deal with the described issues. We describe a runtime monitoring system used in the domain of industrial automation software as an example of a dspl evolving at runtime to motivate and explain our work.