Summary 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 anad hocmanner. In this paper, we proposeSALOON, asoftware product lines‐based platform to face these issues. We describe the architecture of theSALOONplatform, which relies on feature models combined with a domain model used to select among cloud environments a well‐suited one.SALOONsupports 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 usingSALOONsignificantly 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 © 2015 John Wiley & Sons, Ltd.

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.

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.

SUMMARY Nowadays, the computational devices are everywhere. In malls, offices, streets, cars, and even homes, we can find devices providing and consuming functionality to improve the user satisfaction. These devices include sensors that provide information about the environment state (e.g., temperature, occupancy, light levels), service providers (e.g., Internet TVs, GPS), smartphones (that contain user preferences), and actuators that act on the environment (e.g., closing the blinds, activating the alarm, changing the temperature). Although these devices exhibit communication capabilities, their integration into a larger monitoring system remains a challenging task, partly because of the strong heterogeneity of technologies and protocols. Therefore, in this article, we focus on home environments and propose a middleware solution, called DigiHome, that applies the Service Component Architecture (SCA) component model to integrate data and events generated by heterogeneous devices in this kind of environments. DigiHome exploits the SCA extensibility to incorporate the REpresentational State Transfer (REST) architectural style and, in this way, leverages on the integration of multiscale systems‐of‐systems (from wireless sensor networks to the Internet). Additionally, the platform applies Complex Event Processing technology that detects application‐specific situations. We claim that the modularization of concerns fostered by DigiHome and materialized in a service‐oriented architecture, makes it easier to incorporate new services and devices in smart home environments. The benefits of the DigiHome platform are demonstrated on smart home scenarios covering home automation, emergency detection, and energy saving situations. Copyright © 2011 John Wiley & Sons, Ltd.

SUMMARY 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 F RA SCA TI 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 F RA SCA TI platform, and highlights its key features. The component‐based design of the F RA SCA TI 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 F RA SCA TI platform does not decrease its performance when compared with the de facto reference SCA implementation: Apache T USCANY . Finally, a smart home scenario illustrates the extension capabilities and the various reconfigurations of the F RA SCA TI platform. Copyright © 2011 John Wiley & Sons, Ltd.

SCA (Service Component Architecture) is an OASIS standard for describing service-oriented middleware architectures. In particular, SCA promotes a disciplined way for designing distributed architectures based on a component model and an Architecture Description Language (ADL). However, SCA does not cover the deployment and the run-time management of SCA applications. In this paper, we therefore describe the FraSCAti platform, which provides run-time support, deployment capabilities, and run-time management for SCA. Compared to state-of-the-art platforms, FraSCAti brings a dynamic reflective support to SCA and enables both introspecting and reconfiguring service-oriented architectures at run-time. To achieve this capability, the components are completed by a dedicated container, which is automatically generated by the platform. Furthermore, FraSCAti is a highly configurable platform that can be easily customized by finely selecting the features and functionalities which need to be included. In this way, the platform can be adapted to different application needs and middleware environments.