Network overload avoidance by traffic engineering and content caching

The Internet traffic volume continues to grow at a great rate, now driven by video and TV distribution. For network operators it is important to avoid congestion in the network, and to meet service level agreements with their customers. This thesis presents work on two methods operators can use to reduce links loads in their networks: traffic engineering and content caching. This thesis studies access patterns for TV and video and the potential for caching. The investigation is done both using simulation and by analysis of logs from a large TV-on-Demand system over four months. The results show that there is a small set of programs that account for a large fraction of the requests and that a comparatively small local cache can be used to significantly reduce the peak link loads during prime time. The investigation also demonstrates how the popularity of programs changes over time and shows that the access pattern in a TV-on-Demand system very much depends on the content type. For traffic engineering the objective is to avoid congestion in the network and to make better use of available resources by adapting the routing to the current traffic situation. The main challenge for traffic engineering in IP networks is to cope with the dynamics of Internet traffic demands. This thesis proposes L-balanced routings that route the traffic on the shortest paths possible but make sure that no link is utilised to more than a given level L. L-balanced routing gives efficient routing of traffic and controlled spare capacity to handle unpredictable changes in traffic. We present an L-balanced routing algorithm and a heuristic search method for finding L-balanced weight settings for the legacy routing protocols OSPF and IS-IS. We show that the search and the resulting weight settings work well in real network scenarios

Understanding a large-scale IPTV network via system logs

Recently, there has been a global trend among the telecommunication industry on the rapid deployment of IPTV (Internet Protocol Television) infrastructure and services. While the industry rushes into the IPTV era, the comprehensive understanding of the status and dynamics of IPTV network lags behind. Filling this gap requires in-depth analysis of large amounts of measurement data across the IPTV network. One type of the data of particular interest is device or system log, which has not been systematically studied before. In this dissertation, we will explore the possibility of utilizing system logs to serve a wide range of IPTV network management purposes including health monitoring, troubleshooting and performance evaluation, etc. In particular, we develop a tool to convert raw router syslogs to meaningful network events. In addition, by analyzing set-top box (STB) logs, we propose a series of models to capture both channel popularity and dynamics, and users' activity on the IPTV network.Ph.D.Committee Chair: Jun Xu; Committee Member: Jia Wang; Committee Member: Mostafa H. Ammar; Committee Member: Nick Feamster; Committee Member: Xiaoli M

Open educational resources for all? Comparing user motivations and characteristics across The Open University’s iTunes U channel and OpenLearn platform.

With the rise in access to mobile multimedia devices, educational institutions have exploited the iTunes U platform as an additional channel to provide free educational resources with the aim of profile-raising and breaking down barriers to education. For those prepared to invest in content preparation, it is possible to produce interactive, portable material that can be made available globally. Commentators have questioned both the financial implications for platform-specific content production, and the availability of devices for learners to access it (Osborne, 2012). The Open University (OU) makes its free educational resources available on iTunes U and via its web-based open educational resources (OER) platform, OpenLearn. The OU’s OER on iTunes U reached the 60 million download mark in 2013; its OpenLearn platform boasts 27 million unique visitors since 2006. This paper reports the results of a large-scale study of users of the OU’s iTunes U channel and OpenLearn platform. A survey of several thousand users revealed key differences in demographics between those accessing OER via the web and via iTunes U. In addition, the data allowed comparison between three groups: formal learners, informal learners and educators. The study raises questions about whether university-provided OER meet the needs of users and makes recommendations for how content can be modified to suit their needs. As the publishing of OER becomes core to business, we reflect on reasons why understanding users’ motivations and demographics is vital, allowing for needs-led resource provision and content that is adapted to best achieve learner satisfaction, and to deliver institutions’ social mission

Developing sustainable business models for institutions’ provision of open educational resources: Learning from OpenLearn users’ motivations and experiences

Universities across the globe have, for some time, been exploring the possibilities for achieving public benefit and generating business and visibility through releasing and sharing open educational resources (OER). Many have written about the need to develop sustainable and profitable business models around the production and release of OER. Downes (2006), for example, has questioned the financial sustainability of OER production at scale. Many of the proposed business models focus on OER’s value in generating revenue and detractors of OER have questioned whether they are in competition with formal education. This paper reports on a study intended to broaden the conversation about OER business models to consider the motivations and experiences of OER users as the basis for making a better informed decision about whether OER and formal learning are competitive or complementary with each other. The study focused on OpenLearn - the Open University’s (OU) web-based platform for OER, which hosts hundreds of online courses and videos and is accessed by over 3,000,000 users a year. A large scale survey and follow-up interviews with OpenLearn users worldwide revealed that university provided OER can offer learners a bridge to formal education, allowing them to try out a subject before registering on a formal course and to build confidence in their abilities as learners. In addition, it was found that using OER during formal paid-for study can improve learners’ performance and self-reliance, leading to increased retention and satisfaction with the learning experience

Contribution to quality of user experience provision over wireless networks

The widespread expansion of wireless networks has brought new attractive possibilities to end users. In addition to the mobility capabilities provided by unwired devices, it is worth remarking the easy configuration process that a user has to follow to gain connectivity through a wireless network. Furthermore, the increasing bandwidth provided by the IEEE 802.11 family has made possible accessing to high-demanding services such as multimedia communications. Multimedia traffic has unique characteristics that make it greatly vulnerable against network impairments, such as packet losses, delay, or jitter. Voice over IP (VoIP) communications, video-conference, video-streaming, etc., are examples of these high-demanding services that need to meet very strict requirements in order to be served with acceptable levels of quality. Accomplishing these tough requirements will become extremely important during the next years, taking into account that consumer video traffic will be the predominant traffic in the Internet during the next years. In wired systems, these requirements are achieved by using Quality of Service (QoS) techniques, such as Differentiated Services (DiffServ), traffic engineering, etc. However, employing these methodologies in wireless networks is not that simple as many other factors impact on the quality of the provided service, e.g., fading, interferences, etc. Focusing on the IEEE 802.11g standard, which is the most extended technology for Wireless Local Area Networks (WLANs), it defines two different architecture schemes. On one hand, the infrastructure mode consists of a central point, which manages the network, assuming network controlling tasks such as IP assignment, routing, accessing security, etc. The rest of the nodes composing the network act as hosts, i.e., they send and receive traffic through the central point. On the other hand, the IEEE 802.11 ad-hoc configuration mode is less extended than the infrastructure one. Under this scheme, there is not a central point in the network, but all the nodes composing the network assume both host and router roles, which permits the quick deployment of a network without a pre-existent infrastructure. This type of networks, so called Mobile Ad-hoc NETworks (MANETs), presents interesting characteristics for situations when the fast deployment of a communication system is needed, e.g., tactics networks, disaster events, or temporary networks. The benefits provided by MANETs are varied, including high mobility possibilities provided to the nodes, network coverage extension, or network reliability avoiding single points of failure. The dynamic nature of these networks makes the nodes to react to topology changes as fast as possible. Moreover, as aforementioned, the transmission of multimedia traffic entails real-time constraints, necessary to provide these services with acceptable levels of quality. For those reasons, efficient routing protocols are needed, capable of providing enough reliability to the network and with the minimum impact to the quality of the service flowing through the nodes. Regarding quality measurements, the current trend is estimating what the end user actually perceives when consuming the service. This paradigm is called Quality of user Experience (QoE) and differs from the traditional Quality of Service (QoS) approach in the human perspective given to quality estimations. In order to measure the subjective opinion that a user has about a given service, different approaches can be taken. The most accurate methodology is performing subjective tests in which a panel of human testers rates the quality of the service under evaluation. This approach returns a quality score, so-called Mean Opinion Score (MOS), for the considered service in a scale 1 - 5. This methodology presents several drawbacks such as its high expenses and the impossibility of performing tests at real time. For those reasons, several mathematical models have been presented in order to provide an estimation of the QoE (MOS) reached by different multimedia services In this thesis, the focus is on evaluating and understanding the multimedia-content transmission-process in wireless networks from a QoE perspective. To this end, firstly, the QoE paradigm is explored aiming at understanding how to evaluate the quality of a given multimedia service. Then, the influence of the impairments introduced by the wireless transmission channel on the multimedia communications is analyzed. Besides, the functioning of different WLAN schemes in order to test their suitability to support highly demanding traffic such as the multimedia transmission is evaluated. Finally, as the main contribution of this thesis, new mechanisms or strategies to improve the quality of multimedia services distributed over IEEE 802.11 networks are presented. Concretely, the distribution of multimedia services over ad-hoc networks is deeply studied. Thus, a novel opportunistic routing protocol, so-called JOKER (auto-adJustable Opportunistic acK/timEr-based Routing) is presented. This proposal permits better support to multimedia services while reducing the energy consumption in comparison with the standard ad-hoc routing protocols.Universidad Politécnica de CartagenaPrograma Oficial de Doctorado en Tecnologías de la Información y Comunicacione

IPTV data reduction strategy to measure real users’ behaviours

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaThe digital IPTV service has evolved in terms of features, technology and accessibility of their contents. However, the rapid evolution of features and services has brought a more complex offering to customers, which often are not enjoyed or even perceived. Therefore, it is important to measure the real advantage of those features and understand how they are used by customers. In this work, we present a strategy that deals directly with the real IPTV data, which result from the interaction actions with the set-top boxes by customers. But this data has a very low granularity level, which is complex and difficult to interpret. The approach is to transform the clicking actions to a more conceptual and representative level of the running activities. Furthermore, there is a significant reduction in the data cardinality, enhanced in terms of information quality. More than a transformation, this approach aims to be iterative, where at each level, we achieve a more accurate information, in order to characterize a particular behaviour. As experimental results, we present some application areas regarding the main offered features in this digital service. In particular, is made a study about zapping behaviour, and also an evaluation about DVR service usage. It is also discussed the possibility to integrate the strategy devised in a particular carrier, aiming to analyse the consumption rate of their services, in order to adjust them to customer real usage profile, and also to study the feasibility of new services introduction

Measuring And Improving Internet Video Quality Of Experience

Streaming multimedia content over the IP-network is poised to be the dominant Internet traffic for the coming decade, predicted to account for more than 91% of all consumer traffic in the coming years. Streaming multimedia content ranges from Internet television (IPTV), video on demand (VoD), peer-to-peer streaming, and 3D television over IP to name a few. Widespread acceptance, growth, and subscriber retention are contingent upon network providers assuring superior Quality of Experience (QoE) on top of todays Internet. This work presents the first empirical understanding of Internet’s video-QoE capabilities, and tools and protocols to efficiently infer and improve them. To infer video-QoE at arbitrary nodes in the Internet, we design and implement MintMOS: a lightweight, real-time, noreference framework for capturing perceptual quality. We demonstrate that MintMOS’s projections closely match with subjective surveys in accessing perceptual quality. We use MintMOS to characterize Internet video-QoE both at the link level and end-to-end path level. As an input to our study, we use extensive measurements from a large number of Internet paths obtained from various measurement overlays deployed using PlanetLab. Link level degradations of intra– and inter–ISP Internet links are studied to create an empirical understanding of their shortcomings and ways to overcome them. Our studies show that intra–ISP links are often poorly engineered compared to peering links, and that iii degradations are induced due to transient network load imbalance within an ISP. Initial results also indicate that overlay networks could be a promising way to avoid such ISPs in times of degradations. A large number of end-to-end Internet paths are probed and we measure delay, jitter, and loss rates. The measurement data is analyzed offline to identify ways to enable a source to select alternate paths in an overlay network to improve video-QoE, without the need for background monitoring or apriori knowledge of path characteristics. We establish that for any unstructured overlay of N nodes, it is sufficient to reroute key frames using a random subset of k nodes in the overlay, where k is bounded by O(lnN). We analyze various properties of such random subsets to derive simple, scalable, and an efficient path selection strategy that results in a k-fold increase in path options for any source-destination pair; options that consistently outperform Internet path selection. Finally, we design a prototype called source initiated frame restoration (SIFR) that employs random subsets to derive alternate paths and demonstrate its effectiveness in improving Internet video-QoE

IPTV log events profiling

Tese de mestrado em Segurança Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2010O objectivo central da análise de logs é adquirir novos conhecimentos que ajudem os administradores de sistemas a compreender melhor o modo como os seus sistemas estão sendo usados. As redes de telecomunicações constituem uma das áreas onde a quantidade de dados de log registados é enorme e onde, na maioria das vezes, apenas uma ínfima parte desses dados é analisada. Isso coloca importantes perguntas a um gestor de rede: Devem continuam a registar esses dados? Existe alguma informação relevante que possa ser extraída a partir desses dados? Os métodos para a extrair já existem? Poderemos melhorar a operação e a gestão, se essa informação estivesse disponível? Só após responder a estas perguntas é que um gestor pode decidir o que fazer com um log de dados que consuma imensos recursos. A finalidade principal do nosso trabalho é analisar o tipo de informações adicionais que possam ser extraídas dos arquivos de log dos servidores de uma plataforma de IPTV. O nosso foco específico é tentar compreender se é possível determinar quais as sequências de eventos que são despoletadas na plataforma quando um cliente executa uma determinada acção. Essas sequências são desconhecidas para nós e não são documentadas pelo fornecedor de software. Para este trabalho escolhemos a sequência de “arranque" que o utilizador despoleta quando inicializa a sua set‐top‐box (STB). Caracterizamos totalmente essa sequência de pedidos web services que uma STB realiza durante a fase de arranque e autenticação na plataforma IPTV. De seguida, desenvolvemos um método que pode ser aplicado automaticamente para isolar essas sequências nos logs de dados em bruto. Esse método começa por definir o evento de início e depois tenta identificar o evento final da sequência aplicando um conjunto de regras empíricas definidas por nós. Todos os eventos entre aqueles dois constituem a sequência e, embora a maioria deles sejam mandatórios, existem alguns eventos que ocorrem apenas em certos casos, devido às características particulares de cada STB. Finalmente, e depois de isolar as sequências, realizamos uma análise estatística a fim de validar a exactidão do nosso método de identificação/isolamento. A metodologia é desenvolvida e avaliada utilizando um conjunto de dados reais de uma plataforma de IPTV pertencente a uma empresa de telecomunicações. Os resultados confirmam que o método escolhido pode produzir um resultado com um alto nível de precisão, tendo em conta as características específicas dos dados de entrada. Com o conhecimento adicional que este método pode extrair dos logs de dados, os gestores e os engenheiros de rede podem caracterizar melhor os vários utilizadores que estão usando a plataforma, estabelecendo perfis típicos para as sequências de eventos e isolando as sequências anormais para que as mesmas possam ser alvo de análise adicional em termos técnicos ou de segurança.The central goal of log analysis is to acquire new knowledge which will help systems administrators to better understand how their systems are being used. Telecommunications networks are one of those areas where the amount of logged data is huge and, most of the times, only parts of it are analyzed. That presents big questions to a network manager: Should they continue to log the data? Is there any relevant information that can be extracted from that data? The methods to extract it already exist? Could operations and management be improved if that information was available? Only by answering these questions can a manager decide what to do with the logging of data that consumes lots of resources. The main objective of our work is to analyze what type of additional information can be extracted from the server log files of an IPTV platform. Our specific focus is in trying to understand if it is possible to determine what sequences of events are triggered at the platform when a client executes a certain action. These sequences are unknown to us and are not documented by the software provider. For this work, we choose the “boot‐up action” that the user performs when he powers‐on his set‐top‐box (STB). We fully characterize that sequence of web service requests that an STB performs while booting up and logging into the IPTV platform. Then, we develop a method that can be automatically applied to isolate those sequences in the logs raw data. This method starts by defining the start event and then tries to identify the sequence’s end event by applying a set of empirical rules defined by us. The events in between constitute the sequence and while most of them are mandatory there are some events that only occur in certain cases due to the characteristics of a particular STB. Finally, after isolating the sequences, we perform a statistical analysis in order to validate the accuracy of our identification/isolation method. The methodology is developed and evaluated using a real dataset from a telecommunications company’s IPTV platform. The results confirm that the chosen method can produce an output with a high level of accuracy, taking into account the specific characteristics of the input logs data. With the additional knowledge that this method can extract from the logs data, network managers and engineers can better characterize the many users that are using the platform, establishing the typical sequence of events profiles and isolating the anomalous ones for further inspection focused both on technical and security concerns