Platforms and software systems for an autonomic internet

The current Internet does not enable easy introduction and deployment of new network technologies and services. This paper aims to progress the Future Internet (FI) by introduction of a service composition and execution environment that re-use existing components of access and core networks. This paper presents essential service-centric platforms and software systems that have been developed with the aim to create a flexible environment for an Autonomic Internet.Peer ReviewedPostprint (published version

Manageability of Future Internet Virtual Networks from a Practical Viewpoint

International audienceThe Autonomic Internet project approach relies on abstractions and distributed systems of a five plane solution for the provision of Future Internet Services (OSKMV): Orchestration, Service Enablers, Knowledge, Management and Virtualisation Planes. This paper presents a practical viewpoint of the manageability of virtual networks, exercising the components and systems that integrate this approach and that are being validated. This paper positions the distributed systems and networking services that integrate this solution, focusing on the provision of Future Internet services for self-configuration and self- performance management scenes

Scalability approaches for causal multicast: a survey

The final publication is available at Springer via http://dx.doi.org/10.1007/s00607-015-0479-0Many distributed services need to be scalable: internet search, electronic commerce, e-government... In order to achieve scalability, high availability and fault tolerance, such applications rely on replicated components. Because of the dynamics of growth and volatility of customer markets, applications need to be hosted by adaptive, highly scalable systems. In particular, the scalability of the reliable multicast mechanisms used for supporting the consistency of replicas is of crucial importance. Reliable multicast might propagate updates in a pre-determined order (e.g., FIFO, total or causal). Since total order needs more communication rounds than causal order, the latter appears to be the preferable candidate for achieving multicast scalability, although the consistency guarantees based on causal order are weaker than those of total order. 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Semantic Service Description Framework for Efficient Service Discovery and Composition

Web services have been widely adopted as a new distributed system technology by industries in the areas of, enterprise application integration, business process management, and virtual organisation. However, lack of semantics in current Web services standards has been a major barrier in the further improvement of service discovery and composition. For the last decade, Semantic Web Services have become an important research topic to enrich the semantics of Web services. The key objective of Semantic Web Services is to achieve automatic/semi-automatic Web service discovery, invocation, and composition. There are several existing semantic Web service description frameworks, such as, OWL-S, WSDL-S, and WSMF. However, existing frameworks have several issues, such as insufficient service usage context information, precisely specified requirements needed to locate services, lacking information about inter-service relationships, and insufficient/incomplete information handling, make the process of service discovery and composition not as efficient as it should be. To address these problems, a context-based semantic service description framework is proposed in this thesis. This framework focuses on not only capabilities of Web services, but also the usage context information of Web services, which we consider as an important factor in efficient service discovery and composition. Based on this framework, an enhanced service discovery mechanism is proposed. It gives service users more flexibility to search for services in more natural ways rather than only by technical specifications of required services. The service discovery mechanism also demonstrates how the features provided by the framework can facilitate the service discovery and composition processes. Together with the framework, a transformation method is provided to transform exiting service descriptions into the new framework based descriptions. The framework is evaluated through a scenario based analysis in comparison with OWL-S and a prototype based performance evaluation in terms of query response time, the precision and recall ratio, and system scalability

Advanced methods for offshore windfarm planning

There have been increasing interests and projects of Offshore wind farm (OWF) development across the world given the rich wind resources in order to achieve carbon neutral objectives. Appropriate electrical system design of OWF is of key importance in terms of cost saving and improving system efficiency. Two novel electric system layout optimization models for OWF planning are proposed to optimize the topology of collector system and connected transmission system simultaneously in OWFs with single and multiple substations. For OWF with single-substation, a novel mathematical model to represent the system topology is proposed to reduce the number of variables so as to effectively decrease the search space of the optimisation problem, where the continuous substation sitting problem is discretized by a 2-step rasterization method. For large-scale OWFs, the overall electric system optimization problem has been classified into 3 levels: substation optimization, feeder selection, and cable determination. Fuzzy clustering technique and wind turbine allocation method has been proposed to effectively divide the large offshore windfarms into partitions. Both HVDC and HVAC cables are considered as alternatives used in the associated transmission system, which can be optimized at the substation level. The concept of clustering is further applied in feeder level to cluster wind turbines into appropriate feeders. The proposed model and the optimization algorithms are tested and validated in two large-scale offshore winds. A comprehensive decision support model is proposed which covers three key factors that characterize OWF integration: investment cost, system stability and the interactions between MTDC and local AC system, all of which are concerned to characterize the optimal integration location of wind turbines into AC bus location and appropriate converter size installed at the corresponding MTDC terminals. To better fit into the real-world situation, various wind speed and load scenarios have been considered. Validity and effectiveness of the proposed model has been tested to integrate two wind farms to a benchmark AC system via a MTDC grid. The research methodologies presented in the thesis form a rather comprehensive approach for OWF design and planning. With the rapid development in OWF technologies, future research needs are also identified and presented in the thesis

Luminescence-based characterization of crystalline silicon solar cells

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Apprentissage permanent par feedback endogène, application à un système robotique

Les applications robotiques sont liées à l'environnement sociotechnique dynamique dans lequel elles sont intégrées. Dans ce contexte, l'auto-adaptation est une préoccupation centrale et la conception d'applications intelligentes dans de tels environnements nécessite de les considérer comme des systèmes complexes. Le domaine de la robotique est très vaste. L'accent est mis sur les systèmes qui s'adaptent aux contraintes de leur environnement et non sur la mécanique ou le traitement du signal. À la lumière de ce contexte, l'objectif de cette thèse est la conception d'un mécanisme d'apprentissage capable d'apprendre de manière continue en utilisant des feedbacks endogènes (i.e. des interactions internes) dans des environnements sociotechniques dynamiques. Ce mécanisme d'apprentissage doit aussi vérifier plusieurs propriétés qui sont essentielles dans ce contexte comme : l'agnosticité, l'apprentissage tout au long de la vie, l'apprentissage en ligne, l'auto-observation, la généralisation des connaissances, le passage à l'échelle, la tolérance au volume de données et l'explicabilité. Les principales contributions consistent en la construction de l'apprentissage endogène par contextes et la conception du mécanisme d'apprentissage ELLSA pour Endogenous Lifelong Learner by Self-Adaptation. Le mécanisme d'apprentissage proposé est basé sur les systèmes multi-agents adaptatifs combinés à l'apprentissage endogène par contextes. La création de l'apprentissage endogène par contextes est motivée par la caractérisation d'imprécisions d'apprentissage qui sont détectées par des négociations locales entre agents. L'apprentissage endogène par contextes comprends aussi un mécanisme de génération de données artificielles pour améliorer les modèles d'apprentissage tout en réduisant la quantité nécessaire de données d'apprentissage. Dans un contexte d'apprentissage tout au long de la vie, ELLSA permet une mise à jour dynamique des modèles d'apprentissage. Il introduit des stratégies d'apprentissage actif et d'auto-apprentissage pour résoudre les imprécisions d'apprentissage. L'utilisation de ces stratégies dépend de la disponibilité des données d'apprentissage. Afin d'évaluer ses contributions, ce mécanisme est appliqué à l'apprentissage de fonctions mathématiques et à un problème réel dans le domaine de la robotique : le problème de la cinématique inverse. Le scénario d'application est l'apprentissage du contrôle de bras robotiques multi-articulés. Les expériences menées montrent que l'apprentissage endogène par contextes permet d'améliorer les performances d'apprentissage grâce à des mécanismes internes. Elles mettent aussi en évidence des propriétés du système selon les objectifs de la thèse : feedback endogènes, agnosticité, apprentissage tout au long de la vie, apprentissage en ligne, auto-observation, généralisation, passage à l'échelle, tolérance au volume de données et explicabilité.Robotic applications are linked to the dynamic sociotechnical environment in which they are embedded. In this scope, self-adaptation is a central concern and the design of intelligent applications in such environments requires to consider them as complex systems. The field of robotics is very broad. The focus is made on systems that adapt to the constraints of their environment and not on mechanics or signal processing. In light of this context, the objective of this thesis is the design of a learning mechanism capable of continuous learning using endogenous feedback (i.e. internal interactions) in dynamic sociotechnical environments. This learning mechanism must also verify several properties that are essential in this context such as: agnosticity, lifelong learning, online learning, self-observation, knowledge generalization, scalability, data volume tolerance and explainability. The main contributions consist of the construction of Endogenous Context Learning and the design of the learning mechanism ELLSA for Endogenous Lifelong Learner by Self-Adaptation. The proposed learning mechanism is based on Adaptive Multi-Agent Systems combined with Context Learning. The creation of Endogenous Context Learning is motivated by the characterization of learning inaccuracies that are detected by local negotiations between agents. Endogenous Context Learning also includes an artificial data generation mechanism to improve learning models while reducing the amount of the required learning data. In a Lifelong Learning setting, ELLSA enables dynamic updating of learning models. It introduces Active Learning and Self-Learning strategies to resolve learning inaccuracies. The use of these strategies depends on the availability of learning data. In order to evaluate its contributions, this mechanism is applied to the learning of mathematical functions and to a real problem in the field of robotics: the Inverse Kinematics problem. The application scenario is the learning of the control of multi-jointed robotic arms. The conducted experiments show that Endogenous Context Learning enables to improve the learning performances thanks to internal mechanisms. They also highlight the properties of the system according to the objectives of the thesis: endogenous feedback, agnosticity, lifelong learning, online learning, self-observation, knowledge generalization, scalability, data volume tolerance and explainability