Rapid Prototyping of Domain-Specific Architecture Languages

International audienceSoftware architecture has become a sensitive discipline, which consists in concretizing the user requirements into a set of artifacts that can be used to model and reason about the software to be developed. However, the architect often relies on its own knowledge to map domain-specific requirements onto generic software abstractions. Most of the time, this leads to the definition of repetitive tasks and architecture fragments, which can be particularly error prone. We therefore believe that architects need a more flexible approach to cope with the definition of domain-specific architectures by leveraging general purpose architecture description languages. This paper introduces the FraSCAla framework as an architectural framework that can be used to rapidly prototype and experiment domain-specific ADLs in order to catalyze the definition and to improve the reliability of software architectures. We demonstrate the merits of this approach on two case studies that illustrate component-based architectures exhibiting various categories of architectural patterns

A Generative Middleware for Heterogeneous and Distributed Services

International audienceModern software-based services increasingly rely on a highly heterogeneous and dynamic interconnection of platforms and devices offering a wide diversity of capabilities ranging from cloud server with virtually unlimited resources down to micro-controllers with only a few KB of RAM. This paper motivates the fact that no single software framework or software engineering approach is suited to span across this range, and proposes an approach which leverages the latest advances in model-driven engineering, generative techniques and models@runtime in order to tame this tremendous heterogeneity. This paper presents a set of languages dedicated to the integration, deployment and continuous operation of existing libraries and components already available and implemented in various languages. The proposed approach is validated on an industrial case study in the eHealth domain, implemented by an industrial partner that provide an qualitative evaluation of the approach. This case study involves a large number of sensors, devices and gateways based on Rasperry Pi, Intel Edison and Arduino

The Standard Problem

Crafting, adhering to, and maintaining standards is an ongoing challenge. This paper uses a framework based on common models to explore the standard problem: the impossibility of creating, implementing or maintain definitive common models in an open system. The problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. Fitting work by conformance services repairs these gaps between a standard and what is required for interoperation, using several strategies: (a) Universal conformance (all agents access the same standard); (b) Mediated conformance (an interoperability layer supports heterogeneous agents) and (c) Localized conformance, (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents. Machine learning should have a major role in adaptive fitting. Choosing a conformance service depends on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or are adjusted at task time. This analysis thus decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.Comment: Keywords: information standard, interoperability, machine learning, technology evaluation 25 Pages Main text word Count: 5108 Abstract word count: 206 Tables: 1 Figures: 7 Boxes: 2 Submitted to JAMI

Project Final Report Use and Dissemination of Foreground

This document is the final report on use and dissemination of foreground, part of the CONNECT final report. The document provides the lists of: publications, dissemination activities, and exploitable foregroun

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings