Made available in DSpace on 2014-06-12T15:48:27Z (GMT). No. of bitstreams: 2 arquivo1249_1.pdf: 1963665 bytes, checksum: 4d41d19894034bb41cfe58785e1d816a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Usuários de aplicações avançadas, tais os de grade computacional, encontram algumas vezes problemas de desempenho de rede que frustram as suas expectativas de realização de atividades. Por isso, a assistência para comunidades de usuários que tem dificuldades em usar a rede tem sido identificada como uma das principais questões de suporte relacionadas ao desempenho. Isso explica a crescente demanda por serviços de monitoramento que tentam auxiliar os usuários no entendimento das propriedades de rede, bem como no uso de suas aplicações. Serviços de monitoramento trouxeram grandes benefícios em termos de suporte aos usuários. Mesmo assim, existe uma carência por serviços de aconselhamento que beneficie os usuários com informações que vão além da simples apresentação das propriedades de rede. Atualmente, tanto usuários avançados quanto equipes de suporte contam com ferramentas que apresentam informações de uma perspectiva puramente técnica. A maioria delas se baseiam em informações obtidas como resultado de atividades de monitoramento, independentemente dos níveis de qualidade de serviços percebidos pelos usuários. Tais ferramentas de medição atendem razoavelmente os requisitos dos gerentes, operadores e usuários finais quando se trata de aplicações comuns. Contudo, a comunidade de pesquisa vem destacando a importância da qualidade de experiência (QoE) durante a avaliação dos serviços de rede quando se trata de aplicações avançadas. Ao considerar as opiniões dos usuários, o gerenciamento da qualidade de serviços torna-se mais efetivo, uma vez que a avaliação do serviço não está restrita às considerações da equipe de suporte e gerenciamento. Essa tese apresenta uma abordagem de aconselhamento para o suporte de usuários de rede que foca na colaboração entre usuários através do compartilhamento das suas experiências no uso das aplicações para construir uma base de conhecimento. O conhecimento representado inclui não somente a informação sobre os problemas de desempenho, como também as características as aplicações e as opiniões dos usuários. Através dessas características, a abordagem procura alcançar altos níveis de satisfação dos usuários pela redução gradual do volume de informações de suporte erradas. Para avaliar a viabilidade dessa abordagem, um modelo de sistema de raciocínio baseado em casos (RBC) foi construído e validado através de um estudo experimental conduzido em laboratório por meio de um sistema multiagente. O sistema é apoiado por uma base de conhecimento baseada no uso de ontologias e um esquema de reputação baseado no algoritmo Pagerank. Os resultados do estudo mostraram a efetividade da abordagem proposta, sua resilência à comportamento de usuários incoerentes e em conluio, e a importância do conhecimento do domínio sobre as atividades de suporte a usuários

The success of the internet over last few decades has mainly depended on various infrastructure technologies to run distributed applications. Due to diversification and multi-provider nature of the internet, radical architectural improvements which require mutual agreement between infrastructure providers have become highly impractical. This escalating resistance towards the further growth has created a rising demand for new approaches to address this challenge. Network virtualization is regarded as a prominent solution to surmount these limitations. It decouples the conventional Internet service provider’s role into infrastructure provider (InP) and service provider (SP) and introduce a third player known as virtual network Provider (VNP) which creates virtual networks (VNs). Resource discovery aims to assist the VNP in selecting the best InP that has the best matching resources for a particular VN request. In the current literature, resource discovery focuses mainly on static attributes of network resources highlighting the fact that utilization on dynamic attributes imposes significant overhead on the network itself. In this thesis we propose a resource discovery approach that is capable of utilizing the dynamic resource attributes to enhance the resource discovery and increase the overall efficiency of VN creation. We realize that recourse discovery techniques should be fast and cost efficient, enough to not to impose any significant load. Hence our proposed scheme calculates aggregation values of the dynamic attributes of the substrate resources. By comparing aggregation values to VN requirements, a set of potential InPs is selected. The potential InPs satisfy basic VN embedding requirements. Moreover, we propose further enhancements to the dynamic attribute monitoring process using a vector based aggregation approach.

This master's thesis focuses on optimizing the network tracking tool Zabbix. In the first part this thesis provides an overview of techniques used for monitoring computer networks. The thesis also describes the fundamentals of how Zabbix works, especially in large enviroments. In the second part of the thesis we compare existing optimizations of the database structure which divides large, parent tables into small partitions. The most suitable solution is chosen which is then modified for the BUT network needs. Using this optimization we achieved a decrease of the processor utilization on the Zabbix server and a significant decrease in the growth of data on its hard drive.

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This master thesis deals with the field of possibilities of using social networks to increase competitiveness. In first part there are theoretically explained terms such as social network, social media and then there is described possibility to use social networks in business. The thesis includes theoretical treatment procedure entry of the company on social networks and describes appropriate monitoring and analyzing tools. The practical part is focused on dance club Beton, there are made the critical analysis of current attitudes of the club forward to social network, competitive analysis and comparison between Beton's and competitor's social network activities. The last part is devoted to selection appropriate monitoring tool and designed a social media monitoring system.

This thesis presents a framework for collecting network traffic data usable in performance evaluations of Bluetooth Mesh networks. The framework is designed to be adaptive, effective, and efficient. These design goals are intended to minimize resource usage and thereby take constraints in Bluetooth Mesh into account. An implementation of the framework, based on the Bluetooth Mesh model concept, is also presented. The implementation is then validated and evaluated to analyse to what degree it fulfills the requirements of adaptive, effective, and efficient data collection. The evaluation demonstrates the importance of minimizing the size of the reports sent in the framework since larger messages sent with short intervals have a noticeable effect on both the packet delivery ratio of user traffic and the reporting latency. It is also shown that the adaptive reporting feature, that aims to reduce the effect of the framework on user traffic by postponing reporting during high traffic loads, has a positive effect on neighboring nodes overall packet delivery ratio.

Grâce à l'évolution des outils d'automatisation et d'intelligence artificielle, les réseauxmobiles sont devenus de plus en plus dépendants de la machine. De nos jours, une grandepartie des tâches de gestion de réseaux est exécutée d'une façon autonome, sans interventionhumaine. Dans cette thèse, nous avons focalisé sur l'utilisation des techniques d'analyse dedonnées dans le but d'automatiser et de consolider le processus de résolution de défaillancesdans les réseaux. Pour ce faire, nous avons défini deux objectifs principaux : la détectiond'anomalies et le diagnostic des causes racines de ces anomalies. Le premier objectif consiste àdétecter automatiquement les anomalies dans les réseaux sans faire appel aux connaissancesdes experts. Pour atteindre cet objectif, nous avons proposé un algorithme, Watchmen AnomalyDetection (WAD), basé sur le concept de la reconnaissance de formes (pattern recognition). Cetalgorithme apprend le modèle du trafic réseau à partir de séries temporelles périodiques etdétecte des distorsions par rapport à ce modèle dans le flux de nouvelles données. Le secondobjectif a pour objet la détermination des causes racines des problèmes réseau sans aucuneconnaissance préalable sur l'architecture du réseau et des différents services. Pour ceci, nousavons conçu un algorithme, Automatic Root Cause Diagnosis (ARCD), qui permet de localiser lessources d'inefficacité dans le réseau. ARCD est composé de deux processus indépendants :l'identification des contributeurs majeurs à l'inefficacité globale du réseau et la détection desincompatibilités. WAD et ARCD ont fait preuve d'efficacité. Cependant, il est possible d'améliorerces algorithmes sur plusieurs aspects. / With the evolution of automation and artificial intelligence tools, mobile networks havebecome more and more machine reliant. Today, a large part of their management tasks runs inan autonomous way, without human intervention. In this thesis, we have focused on takingadvantage of the data analysis tools to automate the troubleshooting task and carry it to a deeperlevel. To do so, we have defined two main objectives: anomaly detection and root causediagnosis. The first objective is about detecting issues in the network automatically withoutincluding expert knowledge. To meet this objective, we have proposed an algorithm, WatchmenAnomaly Detection (WAD), based on pattern recognition. It learns patterns from periodic timeseries and detect distortions in the flow of new data. The second objective aims at identifying theroot cause of issues without any prior knowledge about the network topology and services. Toaddress this question, we have designed an algorithm, Automatic Root Cause Diagnosis (ARCD)that identifies the roots of network issues. ARCD is composed of two independent threads: MajorContributor identification and Incompatibility detection. WAD and ARCD have been proven to beeffective. However, many improvements of these algorithms are possible.

A proof of concept study into the working of network media capture and visualization through the use of Packet Capture in real-time. An application was developed that is able to capture tcp network packets; identify and display images in raw HTTP network traffic through the use of search, sort, error detection, timeout failsafe algorithms in real time. The application was designed for network administrators to visualize raw network media content together with its relevant network source \& address identifiers. Different approaches were tried and tested such as using Perl with GTK+ and Visual Studio C\# .Net. Furthermore two different types of image identification methods were used: raw magic string identification in pure tcp network traffic and HTTP Mime type identification. The latter being more accurate and faster. C# was seen as vastly superior in both speed of prototyping and final performance evaluation. The study presents a novel new way of monitoring networks on the basis of their media content through deep packet inspection

With the emergence of computer networks as one of the primary modes of communication, and with their adoption for an increasingly wide range of applications, there is a growing need to understand and characterize the traffic they carry. The rise of large scale network attacks adds urgency to this need. However, the large size, high speed and increasing complexity of these networks imply that tracking and characterizing the traffic they carry is an increasingly difficult problem. Dealing with higher level aggregates, such as flows instead of packets, does not solve the problem because these aggregates tend to be quite numerous and exhibit dynamics of their own. In this thesis, we investigate a novel approach to deal with the immense amounts of data associated with problems in network measurement and monitoring. Building upon the paradigm of Data Streaming, which processes a large stream of data using a small working memory to answer a class of queries, we develop an architecture for Network Data Streaming that can accommodate additional constraints imposed in the context of network monitoring. Using this architecture, we design algorithms for monitoring properties of network traffic that have traditionally been considered too difficult to monitor at high speed network links and routers. Our first algorithm provides the ability to accurately estimate the size of individual flows. A second algorithm to estimate the distribution of flow sizes enables network operators to monitor anomalies in the traffic. Incorporating the use of packet sampling, we can extend the latter algorithm to estimate the flow size distribution of arbitrary subpopulations. Finally, we apply the tools of Network Data Streaming to the operation of packet sampling itself. Using the ability to efficiently estimate flow-statistics such as approximate per-flow size, we design a family of mechanisms where the sampling decision is guided by this knowledge. The individual solutions developed in this thesis share a common architectural theme, supporting the monitoring of highly dynamic populations. Integrating this with the traditional sampling based framework for network monitoring will enable a broad range of applications for accurate and comprehensive monitoring of network traffic.