Ces dernières années ont vu le développement du cloud computing. Le principe fondateur est de déporter la gestion des services informatique des entreprises dans des centres d'hébergement gérés par des entreprise tiers. Ce déport a pour principal avantage une réduction des coûts pour l'entreprise cliente, les moyens nécessaires à la gestion de ces services étant mutualisés entre clients et gérés par l'entreprise hébergeant ces services. Cette évolution implique la gestion de structures d'hébergement à grande échelle, que la dimension et la complexité rendent difficiles à administrer. Avec le développement des infrastructures de calcul de type cluster ou grille ont émergé des système fournissant un support pour l'administration automatisée de ces environnements. Ces systèmes sont désignés sous le terme Systèmes d'Administration Autonomes (SAA). Ils visent à fournir des services permettant d'automatiser les tâches d'administration comme le déploiement des logiciels, la réparation en cas de panne ou leur dimensionnement dynamique en fonction de la charge. Ainsi, il est naturel d'envisager l'utilisation des SAA pour l'administration d'une infrastructure d'hébergement de type clouds. Cependant, nous remarquons que les SAA disponibles à l'heure actuelle ont été pour la plupart conçus pour répondre aux besoins d'un domaine applicatif particulier. Un SAA doit pouvoir être adapté en fonction du domaine considéré, en particulier celui de l'administration d'un cloud. De plus, dans le domaine du cloud, différents besoins doivent être pris en compte : ceux de l'administrateur du centre d'hébergement et ceux de l'utilisateur du centre d'hébergement qui déploie ses applications dans le cloud. Ceci implique qu'un SAA doit pouvoir être adapté pour répondre à ces besoins divers. Dans cette thèse, nous étudions la conception et l'implantation d'un SAA adaptable. Un tel SAA doit permettre d'adapter les services qu'il offre aux besoins des domaines dans lesquels il est utilisé. Nous montrons ensuite comment ce SAA adaptable peut être utilisé pour l'administration autonome d'un environnement de cloud. / Last years have seen the development of cloud computing. The main underlying principle of to externalize the management of companies' IT services in hosting centers which are managed by third party companies. This externalization allows saving costs for the client company, since the resources required to manage these services are mutualized between clients and managed by the hosting company. This orientation implies the management of large scale hosting centers, whose dimension and complexity make them difficult to manage. With the developement of computing infrastructures such as clusters or grids, researchers investigated the design of systems which provides support of an automatized management of these environments. We refer to these system as Autonomic Management Systems (AMS). They aim at providing services which automate administration tasks such as software deployment, fault repair or dynamic dimensioning according to a load. Therefore, in this context, it is natural to consider the use of AMS for the administration of a cloud infrastructure. However, we observe that currently available AMS have been designed to address the requirements of a particular application domain. It should be possible to adapt an AMS according to the considered domain, in particular that of the cloud. Moreover, in the cloud computing area, different requirements have to be accounted : those of the administrator of the hosting center and those of the user of the hosting center (who deploys his application in the cloud). Therefore, an AMS should be adaptable to fulfill such various needs. In this thesis, we investigate the design and implementation of an adaptable AMS. Such an AMS must allow adaptation of all the services it provides, according to the domains where it is used. We next describe the application of this adaptable AMS for the autonomic management of a cloud environment.

abstract: Emerging trends in cyber system security breaches in critical cloud infrastructures show that attackers have abundant resources (human and computing power), expertise and support of large organizations and possible foreign governments. In order to greatly improve the protection of critical cloud infrastructures, incorporation of human behavior is needed to predict potential security breaches in critical cloud infrastructures. To achieve such prediction, it is envisioned to develop a probabilistic modeling approach with the capability of accurately capturing system-wide causal relationship among the observed operational behaviors in the critical cloud infrastructure and accurately capturing probabilistic human (users’) behaviors on subsystems as the subsystems are directly interacting with humans. In our conceptual approach, the system-wide causal relationship can be captured by the Bayesian network, and the probabilistic human behavior in the subsystems can be captured by the Markov Decision Processes. The interactions between the dynamically changing state graphs of Markov Decision Processes and the dynamic causal relationships in Bayesian network are key components in such probabilistic modelling applications. In this thesis, two techniques are presented for supporting the above vision to prediction of potential security breaches in critical cloud infrastructures. The first technique is for evaluation of the conformance of the Bayesian network with the multiple MDPs. The second technique is to evaluate the dynamically changing Bayesian network structure for conformance with the rules of the Bayesian network using a graph checker algorithm. A case study and its simulation are presented to show how the two techniques support the specific parts in our conceptual approach to predicting system-wide security breaches in critical cloud infrastructures. / Dissertation/Thesis / Masters Thesis Computer Science 2015

[pt] A implantação de aplicações baseadas em componentes distribuídos é composta por um conjunto de atividades geridas por uma Infraestrutura de Implantação. Aplicações atuais estão se tornando cada vez mais complexas, necessitando de um ambiente alvo dinâmico e multi-plataforma. Assim, a atividade de planejamento de uma implantação é o passo mais crítico, pois define a configuração da infraestrutura de execução de forma a atender os requisitos do ambiente alvo de uma aplicação. Por outro lado, o modelo de serviço na nuvem chamado Infraestrutura como Serviço(IaaS) oferece recursos computacionais sob demanda, com características dinâmicas, escaláveis e elásticas. Nesta dissertação nós estendemos a Infraestrutura de Implantação para componentes SCS de forma a permitir o uso de nuvens privadas ou públicas como o ambiente alvo de uma implantação, através do uso de uma cloud API e políticas flexíveis para especificar um ambiente alvo personalizado. Além disso, hospedamos a infraestrutura de implantação na nuvem. Isto permitiu-nos usar recursos computacionais sob demanda para instanciar os serviços da Infraestrutura de Implantação, produzindo uma Plataforma como Serviço(PaaS) experimental. / [en] Deployment of distributed component-based applications is composed of a set of activities managed by a Deployment Infrastructure. Current applications are becoming increasingly more complex, requiring a multi-platform and a dynamic target environment. Thus, the planning activity is the most critical step because it defines the configuration of the execution infrastructure in order to satisfy the requirements of the application’s target environment. On the other hand, the cloud service model called Infrastructure as a Service (IaaS) offers on-demand computational resources with dynamic, scalable, and elastic features. In this work we have extended the Deployment Infrastructure for SCS componentes to support private or public clouds as its target environment, through the use of a cloud API and flexible policies to specify a customized target environment. Additionally, we host the Deployment Infrastructure on the cloud, which allow us to use on-demand computational resources to instantiate Deployment Infrastructure services, creating an experimental Platform as a Service (PaaS).

Cloud datacenters comprise hundreds or thousands of disparate application services, each having stringent performance and availability requirements, sharing a finite set of heterogeneous hardware and software resources. The implication of such complex environment is that the occurrence of performance problems, such as slow application response and unplanned downtimes, has become a norm rather than exception resulting in decreased revenue, damaged reputation, and huge human-effort in diagnosis. Though causes can be as varied as application issues (e.g. bugs), machine-level failures (e.g. faulty server), and operator errors (e.g. mis-configurations), recent studies have attributed capacity-related issues, such as resource shortage and contention, as the cause of most performance problems on the Internet today. As cloud datacenters become increasingly autonomous there is need for automated performance diagnosis systems that can adapt their operation to reflect the changing workload and topology in the infrastructure. In particular, such systems should be able to detect anomalous performance events, uncover manifestations of capacity bottlenecks, localize actual root-cause(s), and possibly suggest or actuate corrections. This thesis investigates approaches for diagnosing performance problems in cloud infrastructures. We present the outcome of an extensive survey of existing research contributions addressing performance diagnosis in diverse systems domains. We also present models and algorithms for detecting anomalies in real-time application performance and identification of anomalous datacenter resources based on operational metrics and spatial dependency across datacenter components. Empirical evaluations of our approaches shows how they can be used to improve end-user experience, service assurance and support root-cause analysis. / Cloud Control (C0590801)