Philippe Merle

A component-based middleware platform for reconfigurable service-oriented architectures

ThetextitService Component Architecture (SCA) is a technology‐independent standard for developing distributed Service‐oriented Architectures (SOA). The SCA standard promotes the use of components and architecture descriptors, and mostly covers the lifecycle steps of implementation and deployment. Unfortunately, SCA does not address the governance of SCA applications and provides no support for the maintenance of deployed components. This article covers this issue and introduces the FRASCATI platform, a run‐time support for SCA with dynamic reconfiguration capabilities and run‐time management features. This article presents the internal component‐based architecture of the FRASCATI platform, and highlights its key features. The component‐based design of the FRASCATI platform introduces many degrees of flexibility and configurability in the platform itself and it can host the SOA applications. This article reports on micro‐benchmarks highlighting that run‐time manageability in the FRASCATI platform does not decrease its performance when compared with the de facto reference SCA implementation: Apache TUSCANY. Finally, a smart home scenario illustrates the extension capabilities and the various reconfigurations of the FRASCATI platform. Copyright

Reconfigurable SCA Applications with the FraSCAti Platform

The Service Component Architecture (SCA) is a technology agnostic standard for developing and deploying distributed service-oriented applications. However, SCA does not define standard means for runtime manageability (including introspection and reconfiguration) of SOA applications and of their supporting environment. This paper presents the FraSCAti platform, which brings runtime management features to SCA, and discusses key principles in its design: the adoption of an extended SCA component model for the implementation of SOA applications and of the FraSCAti platform itself; the use of component-based interception techniques for dynamically weaving non-functional services such as transaction management with components. The paper presents micro-benchmarks that show that runtime manageability in the FraSCAti platform is achieved without hindering its performance relative to the de facto reference SCA implementation, Apaches Tuscany.

A Federated Multi-cloud PaaS Infrastructure

Cloud platforms are increasingly being used for hosting a broad diversity of services from traditional e-commerce applications to interactive web-based Ides. How-ever, we observe that the proliferation of offers by cloud providers raises several challenges. Developers will not only have to deploy applications for a specific cloud, but will also have to consider migrating services from one cloud to another, and to manage distributed applications spanning multiple clouds. In this paper, we present our federated multi-cloud PaaS infrastructure for addressing these challenges. This infrastructure is based on three foundations: i) an open service model used to design and implement both our multi-cloud PaaSand the SaaS applications running on top of it, ii) a configurable architecture of the federated PaaS, and iii) some infrastructure services for managing both our multi-cloud PaaS and the SaaS applications. We then show how this multi-cloud PaaS can be deployed on top of thirteen existing IaaS/PaaS. We finally report on three distributed SaaS applications developed with and deployed on our federated multi-cloud PaaS infrastructure.

Reverse engineering architectural feature models

Reverse engineering the variability of an existing system is a challenging activity. The architect knowledge is essential to identify variation points and explicit constraints between features, for instance in feature models (FMs), but the manual creation of FMs is both time-consuming and error-prone. On a large scale, it is very difficult for an architect to guarantee that the resulting FM is consistent with the architecture it is associated with. In this paper, we present a comprehensive, tool supported process for reverse engineering architectural FMs. We develop automated techniques to extract and combine different variability descriptions of an architecture. Then, alignment and reasoning techniques are applied to integrate the architect knowledge and reinforce the extracted FM. We illustrate the process when applied to a representative software system and we report on our experience in this context.

Extraction and evolution of architectural variability models in plugin-based systems

Variability management is a key issue when building and evolving software-intensive systems, making it possible to extend, configure, customize and adapt such systems to customers’ needs and specific deployment contexts. A wide form of variability can be found in extensible software systems, typically built on top of plugin-based architectures that offer a (large) number of configuration options through plugins. In an ideal world, a software architect should be able to generate a system variant on-demand, corresponding to a particular assembly of plugins. To this end, the variation points and constraints between architectural elements should be properly modeled and maintained over time (i.e., for each version of an architecture). A crucial, yet error-prone and time-consuming, task for a software architect is to build an accurate representation of the variability of an architecture, in order to prevent unsafe architectural variants and reach the highest possible level of flexibility. In this article, we propose a reverse engineering process for producing a variability model (i.e., a feature model) of a plugin-based architecture. We develop automated techniques to extract and combine different variability descriptions, including a hierarchical software architecture model, a plugin dependency model and the software architect knowledge. By computing and reasoning about differences between versions of architectural feature models, software architect can control both the variability extraction and evolution processes. The proposed approach has been applied to a representative, large-scale plugin-based system (FraSCAti), considering different versions of its architecture. We report on our experience in this context.

Towards a dynamic CORBA component platform

Distributed object computing (DOC) middleware, even if commonly used, has several drawbacks in supporting large and complex distributed applications: no visibility of distributed object interconnections, no implementation separation between business logic and system services, and no application deployment process. In response to this, DOC middleware is evolving to distributed component computing (DCC) middleware such as the Enterprise Java Beans and the CORBA Component Model (CCM). Such middleware provides new solutions to exhibit component interconnections, to separate functional and non-functional aspects, and to deploy components. However, this new middleware generation does not allow applications to have fine-grain control of their deployment process, i.e. the deployment process is hard-coded into DCC middleware and applications cannot adapt it to their requirements. In the context of the CCM, this paper promotes a flexible deployment process supported by a dynamic CORBA Component platform. This platform is composed of an OMG IDL compiler, generic container servers, and our CorbaScript engine (basis and first implementation of the OMG IDLscript specification). It allows software architects to dynamically drive the deployment of their distributed components, as well as to be reactive to their evolutions.

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.

soCloud: a service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds

Multi-cloud computing is a promising paradigm to support very large scale world wide distributed applications. Multi-cloud computing is the usage of multiple, independent cloud environments, which assumed no priori agreement between cloud providers or third party. However, multi-cloud computing has to face several key challenges such as portability, provisioning, elasticity, and high availability. Developers will not only have to deploy applications to a specific cloud, but will also have to consider application portability from one cloud to another, and to deploy distributed applications spanning multiple clouds. This article presents soCloud a service-oriented component-based Platform as a Service for managing portability, elasticity, provisioning, and high availability across multiple clouds. soCloud is based on the OASIS Service Component Architecture standard in order to address portability. soCloud provides services for managing provisioning, elasticity, and high availability across multiple clouds. soCloud has been deployed and evaluated on top of ten existing cloud providers: Windows Azure, DELL KACE, Amazon EC2, CloudBees, OpenShift, dotCloud, Jelastic, Heroku, Appfog, and an Eucalyptus private cloud.

Applying OMG D&C specification and ECA rules for autonomous distributed component-based systems

Manual administration of complex distributed applications is almost impossible to achieve. On the one side, work in autonomic computing focuses on systems that maintain themselves, driven by high-level policies. Such a self-administration relies on the concept of a control loop. The autonomic computing control loop involves an abstract representation of the system to analyze the situation and to adapt it properly. On the other side, models are currently used to ease design of complex distributed systems. Nevertheless, at runtime, models remain useless, because they are decoupled from the running system, which dynamically evolves. Our proposal, named Dacar, introduces models in the control loop. Using adequate models, it is possible to design and execute both the distributed systems and their autonomic policies. The metamodel suggested in this paper mixes both OMG Deployment and Configuration (OMG D&C) specification and the Event-Condition-Action (ECA) metamodels. This paper addresses the different concerns involved in the control loop and focuses on the metamodel concepts that are required to express entities of the control loop. This paper also gives an overview of our Dacar prototype and illustrates it on a ubiquitous application case study.

Managing elasticity across multiple cloud providers

In the context of cloud computing, elasticity is the capacity to scale computing resources up and down easily. Currently, most Platforms as a Service (PaaS) manage application elasticity within a single cloud provider. However, the not so infrequent issue of cloud outages has become a concern that hinders the availability of cloud-based applications. The most promising solutions to this issue are those based on the federation of multiple clouds. In this paper, we present a Multi-Cloud-PaaS architecture. We show how this architecture can be used for managing elasticity across multiple cloud providers.