Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system.

Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system.

Les réseaux de télécommunications deviennent de plus en plus complexes, notamment de par la multiplicité des technologies mises en œuvre, leur couverture géographique grandissante, la croissance du trafic en quantité et en variété, mais aussi de par l’évolution des services fournis par les opérateurs. Tout ceci contribue à rendre la gestion de ces réseaux de plus en plus lourde, complexe, génératrice d’erreurs et donc coûteuse pour les opérateurs. On place derrière le terme « réseaux autonome » l’ensemble des solutions visant à rendre la gestion de ce réseau plus autonome. L’objectif de cette thèse est de contribuer à la réalisation de certaines fonctions autonomiques dans les réseaux de télécommunications. Nous proposons une stratégie pour automatiser la gestion des pannes tout en couvrant les différents segments du réseau et les services de bout en bout déployés au-dessus. Il s’agit d’une approche basée modèle qui adresse les deux difficultés du diagnostic basé modèle à savoir : a) la façon d'obtenir un tel modèle, adapté à un réseau donné à un moment donné, en particulier si l'on souhaite capturer plusieurs couches réseau et segments et b) comment raisonner sur un modèle potentiellement énorme, si l'on veut gérer un réseau national par exemple. Pour répondre à la première difficulté, nous proposons un nouveau concept : l’auto-modélisation qui consiste d’abord à construire les différentes familles de modèles génériques, puis à identifier à la volée les instances de ces modèles qui sont déployées dans le réseau géré. La seconde difficulté est adressée grâce à un moteur d’auto-diagnostic actif, basé sur le formalisme des réseaux Bayésiens et qui consiste à raisonner sur un fragment du modèle du réseau qui est augmenté progressivement en utilisant la capacité d’auto-modélisation: des observations sont collectées et des tests réalisés jusqu’à ce que les fautes soient localisées avec une certitude suffisante. Cette approche de diagnostic actif a été expérimentée pour réaliser une gestion multi-couches et multi-segments des alarmes dans un réseau IMS. / While modern networks and services are continuously growing in scale, complexity and heterogeneity, the management of such systems is reaching the limits of human capabilities. Technically and economically, more automation of the classical management tasks is needed. This has triggered a significant research effort, gathered under the terms self-management and autonomic networking. The aim of this thesis is to contribute to the realization of some self-management properties in telecommunication networks. We propose an approach to automatize the management of faults, covering the different segments of a network, and the end-to-end services deployed over them. This is a model-based approach addressing the two weaknesses of model-based diagnosis namely: a) how to derive such a model, suited to a given network at a given time, in particular if one wishes to capture several network layers and segments and b) how to reason a potentially huge model, if one wishes to manage a nation-wide network for example. To address the first point, we propose a new concept called self-modeling that formulates off-line generic patterns of the model, and identifies on-line the instances of these patterns that are deployed in the managed network. The second point is addressed by an active self-diagnosis engine, based on a Bayesian network formalism, that consists in reasoning on a progressively growing fragment of the network model, relying on the self-modeling ability: more observations are collected and new tests are performed until the faults are localized with sufficient confidence. This active diagnosis approach has been experimented to perform cross-layer and cross-segment alarm management on an IMS network.

Les réseaux actuels servent millions de clients mobiles et ils se caractérisent par équipement hétérogène et protocoles de transport et de gestion hétérogènes, et des outils de gestion verticaux, qui sont très difficiles à intégrer dans leur infrastructure. La gestion de pannes est loin d’être automatisée et intelligent, ou un 40 % des alarmes sont redondantes et seulement un 1 ou 2% des alarmes sont corrélées au plus dans un centre opérationnel. Ça indique qu’il y a un débordement significatif des alarmes vers les adminis-trateurs humains, a comme conséquence un haut OPEX vue la nécessité d’embaucher de personnel expert pour accomplir les tâches de gestion de pannes. Comme conclusion, le niveau actuel d’automatisation dans les tâches de gestion de pannes dans réseaux télécoms n’est pas adéquat du tout pour adresser les réseaux programmables, lesquels promettent la programmation des ressources et la flexibilité afin de réduire le time-to-market des nouveaux services. L’automatisation de la gestion des pannes devient de plus en plus nécessaire avec l’arrivée des réseaux programmables, SDN (Software-Defined Networking), NFV (Network Functions Virtualization) et le Cloud. En effet, ces paradigmes accélèrent la convergence entre les domaines des réseaux et la IT, laquelle accélère de plus en plus la transformation des réseaux télécoms actuels en menant à repenser les opérations de gestion de réseau et des services, en particulier les opérations de gestion de fautes. Cette thèse envisage l’application des principes d’autoréparation en infrastructures basées sur SDN et NFV, en focalisant sur l’autodiagnostic comme facilitateur principal des principes d’autoréparation. Le coeur de cette thèse c’est la conception d’une approche de diagnostic qui soit capable de diagnostiquer de manière continuée les services dynamiques virtualisés et leurs dépendances des ressources virtuels (VNFs et liens virtuels) mais aussi les dépendances de ceux ressources virtuels de la infrastructure physique en-dessous, en prenant en compte la mobilité, la dynamicite, le partage de ressources à l’infrastructure en-dessous / Current networks serve billions of mobile customer devices. They encompass heterogeneous equipment, transport and manage-ment protocols, and vertical management tools, which are very difficult and costly to integrate. Fault management operations are far from being automated and intelligent, where around 40% of alarms are redundant only around 1-2% of alarms are correlated at most in a medium-size operational center. This indicates that there is a significant alarm overflow for human administrators, which inherently derives in high OPEX due to the increasingly need to employ high-skilled people to perform fault management tasks. In conclusion, the current level of automation in fault management tasks in Telcos networks is not at all adequate for programmable networks, which promise a high degree of programmability and flexibility to reduce the time-to-market. Automation on fault management is more necessary with the advent of programmable networks, led by with SDN (Software-Defined Networking), NFV (Network Functions Virtualization) and the Cloud. Indeed, the arise of those paradigms accelerates the convergence between networks and IT realms, which as consequence, is accelerating faster and faster the transformation of cur-rent networks leading to rethink network and service management and operations, in particular fault management operations. This thesis envisages the application of self-healing principles in SDN and NFV combined infrastructures, by focusing on self-diagnosis tasks as main enabler of self-healing. The core of thesis is to devise a self-diagnosis approach able to diagnose at run-time the dynamic virtualized networking services and their dependencies from the virtualized resources (VNFs and virtual links) but also the dependencies of those virtualized resources from the underlying network infrastructure, taking into account the mobility, dynamicity, and sharing of resources in the underlying infrastructure