Test et Validation des Systémes Pair-à-pair

Peer-to-peer (P2P) offers good solutions for many applications such as large data sharing and collaboration in social networks. Thus, it appears as a powerful paradigm to develop scalable distributed applications, as reflected by the increasing number of emerging projects based on this technology. However, building trustworthy P2P applications is difficult because they must be deployed on a large number of autonomous nodes, which may refuse to answer to some requests and even leave the system unexpectedly. This volatility of nodes is a common behavior in P2P systems and can be interpreted as a fault during tests. In this thesis, we propose a framework and a methodology for testing and validating P2P applications. The framework is based on the individual control of nodes, allowing test cases to precisely control the volatility of nodes during their execution. We also propose three different architectures to control the execution of test cases in distributed systems. The first approach extends the classical centralized test coordinator in order to handle the volatility of peers. The other two approaches avoids the central coordinator in order to scale up the test cases. We validated the framework and the methodology through implementation and experimentation on two popular open-source P2P applications (i.e. FreePastry and OpenChord). The experimentation tests the behavior of the system on different conditions of volatility and shows how the tests were able to detect complex implementation problems.Le pair-à-pair (P2P) offre de bonnes solutions pour de nombreuses applications distribuées, comme le partage de grandes quantités de données et/ou le support de collaboration dans les réseaux sociaux. Il apparaît donc comme un puissant paradigme pour développer des applications distribuées évolutives, comme le montre le nombre croissant de nouveaux projets basés sur cette technologie. Construire des applications P2P fiables est difficile, car elles doivent être déployées sur un grand nombre de noeuds, qui peuvent être autonomes, refuser de répondre à certaines demandes, et même quitter le système de manière inattendue. Cette volatilité des noeuds est un comportement commun dans les systèmes P2P et peut être interprétée comme une faute lors des tests. Dans cette thèse, nous proposons un cadre et une méthodologie pour tester et valider des applications P2P. Ce cadre s'appuie sur le contrôle individuel des noeuds, permettant de contrôler précisément la volatilité des noeuds au cours de leur exécution. Nous proposons également trois différentes approches de contrôle d'exécution de scénarios de test dans les systèmes distribués. La première approche étend le coordonnateur centralisé classique pour gérer la volatilité des pairs. Les deux autres approches permettent d'éviter le coordinateur central afin de faire passer à l'échelle l'exécution des cas de tests. Nous avons validé le cadre et la méthodologie à travers la mise en oeuvre et l'expérimentation sur des applications P2P open-source bien connues (FreePastry et OpenChord). Les expérimentations ont permis de tester le comportement des systèmes sur différentes conditions de volatilité, et de détecter des problèmes d'implémentation complexes

QuiiQ automation foundation

Tese de mestrado. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 200

Definitive Consensus for Distributed Data Inference

Inference from data is of key importance in many applications of informatics. The current trend in performing such a task of inference from data is to utilise machine learning algorithms. Moreover, in many applications that it is either required or is preferable to infer from the data in a distributed manner. Many practical difficulties arise from the fact that in many distributed applications we avert from transferring data or parts of it due to costs, privacy and computation considerations. Admittedly, it would be advantageous if the final knowledge, attained through distributed data inference, is common to every participating computing node. The key in achieving the aforementioned task is the distributed average consensus algorithm or simply the consensus algorithm herein. The latter has been used in many applications. Initially the main purpose has been for the estimation of the expectation of scalar valued data distributed over a network of machines without a central node. Notably, the algorithm allows the final outcome to be the same for every participating node. Utilising the consensus algorithm as the centre piece makes the task of distributed data inference feasible. However, there are many difficulties that hinder its direct applicability. Thus, we concentrate on the consensus algorithm with the purpose of addressing these difficulties. There are two main concerns. First, the consensus algorithm has asymptotic convergence. Thus, we may only achieve maximum accuracy if the algorithm is left to run for a large number of iterations. Second, the accuracy attained at any iteration during the consensus algorithm is correlated with the standard deviation of the initial value distribution. The consensus algorithm is inherently imprecise at finite time and this hardens the learning process. We solve this problem by introducing the definitive consensus algorithm. This algorithm attains maximum precision in a finite number of iterations, namely in a number of iterations equal to the diameter of the graph in a distributed and decentralised manner. Additionally, we introduce the nonlinear consensus algorithm and the adaptive consensus algorithm. These are modifications of the original consensus algorithm that allow improved precision with fewer iterations in cases of unknown, partially known and stochastically time-varying network topologies. The definitive consensus algorithm can be incorporated in a distributed data inference framework. We approach the problem of data inference from the perspective of machine learning. Specifically, we tailor this distributed inference framework for machine learning on a communication network with data partitioned on the participating computing nodes. Particularly, the distributed data inference framework is detailed and applied to the case of a multilayer feed forward neural network with error back-propagation. A substantial examination of its performance and its comparison with the non-distributed case, is provided. Theoretical foundation for the definitive consensus algorithm is provided. Moreover, its superior performance is validated by numerical experiments. A brief theoretical examination of the nonlinear and the adaptive consensus algorithms is performed to justify their improved performance with respect to the original consensus algorithm. Moreover, extensive numerical simulations are given to compare the nonlinear and the adaptive algorithm with the original consensus algorithm. The most important contributions of this research are principally the definitive consensus algorithm and the distributed data inference framework. Their combination yields a decentralised distributed process over a communication network capable for inference in agreement over the entire network

SIPBIO : biometrics SIP extension

During the last few decades biometric technologies have become an important research field in computer security. Their deployment, however, in heterogeneous enterprise systems, is complex due to the lack of standardisation. Session Initiation Protocol (SIP) is a popular communication protocol widely used in voice over Internet protocol networks; due to its flexibility, SIP has been broadly adopted in telecommunications for carrier level and telephony systems. This thesis proposes the use of SIPBIO, an extension to SIP, to establish and control multimedia sessions for biometric interactions. For biometric usage in telecommunications networks, a synthesis of techniques to use human characteristics as challenge tokens for access to network resources is first presented. An overview of the SIP protocol is then exposed, by focusing on understanding SIP messages and their component elements. Posteriorly, advanced concepts, such as extensions to the default protocol are introduced. After the technology background review, the core of the proposal is presented with extensive use-case scenarios of biometric operations and the introduction of necessary SIPBIO requirements. Formal processes are defined along with the method to extend SIP to the proposed SIPBIO protocol. It follows a detailed outline of all headers and body components that give form to SIPBIO and define its nature. These stages provide the fundamentals for the protocol implementation. Finally, simulations of some common cases are presented to show the feasibility of SIPBIO. This can be used as a sample flow for full implementations and applications. This thesis corroborates the viability of using a SIP-based protocol for establishing, maintaining and tearing down biometric multimedia sessions

13th International Conference on Modeling, Optimization and Simulation - MOSIM 2020

Comité d’organisation: Université Internationale d’Agadir – Agadir (Maroc) Laboratoire Conception Fabrication Commande – Metz (France)Session RS-1 “Simulation et Optimisation” / “Simulation and Optimization” Session RS-2 “Planification des Besoins Matières Pilotée par la Demande” / ”Demand-Driven Material Requirements Planning” Session RS-3 “Ingénierie de Systèmes Basées sur les Modèles” / “Model-Based System Engineering” Session RS-4 “Recherche Opérationnelle en Gestion de Production” / "Operations Research in Production Management" Session RS-5 "Planification des Matières et des Ressources / Planification de la Production” / “Material and Resource Planning / Production Planning" Session RS-6 “Maintenance Industrielle” / “Industrial Maintenance” Session RS-7 "Etudes de Cas Industriels” / “Industrial Case Studies" Session RS-8 "Données de Masse / Analyse de Données” / “Big Data / Data Analytics" Session RS-9 "Gestion des Systèmes de Transport” / “Transportation System Management" Session RS-10 "Economie Circulaire / Développement Durable" / "Circular Economie / Sustainable Development" Session RS-11 "Conception et Gestion des Chaînes Logistiques” / “Supply Chain Design and Management" Session SP-1 “Intelligence Artificielle & Analyse de Données pour la Production 4.0” / “Artificial Intelligence & Data Analytics in Manufacturing 4.0” Session SP-2 “Gestion des Risques en Logistique” / “Risk Management in Logistics” Session SP-3 “Gestion des Risques et Evaluation de Performance” / “Risk Management and Performance Assessment” Session SP-4 "Indicateurs Clés de Performance 4.0 et Dynamique de Prise de Décision” / ”4.0 Key Performance Indicators and Decision-Making Dynamics" Session SP-5 "Logistique Maritime” / “Marine Logistics" Session SP-6 “Territoire et Logistique : Un Système Complexe” / “Territory and Logistics: A Complex System” Session SP-7 "Nouvelles Avancées et Applications de la Logique Floue en Production Durable et en Logistique” / “Recent Advances and Fuzzy-Logic Applications in Sustainable Manufacturing and Logistics" Session SP-8 “Gestion des Soins de Santé” / ”Health Care Management” Session SP-9 “Ingénierie Organisationnelle et Gestion de la Continuité de Service des Systèmes de Santé dans l’Ere de la Transformation Numérique de la Société” / “Organizational Engineering and Management of Business Continuity of Healthcare Systems in the Era of Numerical Society Transformation” Session SP-10 “Planification et Commande de la Production pour l’Industrie 4.0” / “Production Planning and Control for Industry 4.0” Session SP-11 “Optimisation des Systèmes de Production dans le Contexte 4.0 Utilisant l’Amélioration Continue” / “Production System Optimization in 4.0 Context Using Continuous Improvement” Session SP-12 “Défis pour la Conception des Systèmes de Production Cyber-Physiques” / “Challenges for the Design of Cyber Physical Production Systems” Session SP-13 “Production Avisée et Développement Durable” / “Smart Manufacturing and Sustainable Development” Session SP-14 “L’Humain dans l’Usine du Futur” / “Human in the Factory of the Future” Session SP-15 “Ordonnancement et Prévision de Chaînes Logistiques Résilientes” / “Scheduling and Forecasting for Resilient Supply Chains

Risk Management for the Future

A large part of academic literature, business literature as well as practices in real life are resting on the assumption that uncertainty and risk does not exist. We all know that this is not true, yet, a whole variety of methods, tools and practices are not attuned to the fact that the future is uncertain and that risks are all around us. However, despite risk management entering the agenda some decades ago, it has introduced risks on its own as illustrated by the financial crisis. Here is a book that goes beyond risk management as it is today and tries to discuss what needs to be improved further. The book also offers some cases