Video traffic modeling and delivery

Video is becoming a major component of the network traffic, and thus there has been a great interest to model video traffic. It is known that video traffic possesses short range dependence (SRD) and long range dependence (LRD) properties, which can drastically affect network performance. By decomposing a video sequence into three parts, according to its motion activity, Markov-modulated self-similar process model is first proposed to capture autocorrelation function (ACF) characteristics of MPEG video traffic. Furthermore, generalized Beta distribution is proposed to model the probability density functions (PDFs) of MPEG video traffic. It is observed that the ACF of MPEG video traffic fluctuates around three envelopes, reflecting the fact that different coding methods reduce the data dependency by different amount. This observation has led to a more accurate model, structurally modulated self-similar process model, which captures the ACF of the traffic, both SRD and LRD, by exploiting the MPEG structure. This model is subsequently simplified by simply modulating three self-similar processes, resulting in a much simpler model having the same accuracy as the structurally modulated self-similar process model. To justify the validity of the proposed models for video transmission, the cell loss ratios (CLRs) of a server with a limited buffer size driven by the empirical trace are compared to those driven by the proposed models. The differences are within one order, which are hardly achievable by other models, even for the case of JPEG video traffic. In the second part of this dissertation, two dynamic bandwidth allocation algorithms are proposed for pre-recorded and real-time video delivery, respectively. One is based on scene change identification, and the other is based on frame differences. The proposed algorithms can increase the bandwidth utilization by a factor of two to five, as compared to the constant bit rate (CBR) service using peak rate assignment

A critical look at power law modelling of the Internet

This paper takes a critical look at the usefulness of power law models of the Internet. The twin focuses of the paper are Internet traffic and topology generation. The aim of the paper is twofold. Firstly it summarises the state of the art in power law modelling particularly giving attention to existing open research questions. Secondly it provides insight into the failings of such models and where progress needs to be made for power law research to feed through to actual improvements in network performance.Comment: To appear Computer Communication

Camouflaging Timing Channels in Web Traffic

Web traffic accounts for more than half of Internet traffic today. Camouflaging covert timing channels in Web traffic would be advantageous for concealment. In this paper, we investigate the possibility of disguising network covert timing channels as HTTP traffic to avoid detection. Extensive research has shown that Internet traffic, including HTTP traffic, exhibits self-similarity and long range persistence. Existing covert timing channels that mimic i.i.d. legitimate traffic cannot imitate HTTP traffic because these covert traffic patterns are not long range dependent. The goal of this work is to design a covert timing channel that can be camouflaged as HTTP traffic. To this end, we design a covert timing channel whose inter-arrival times are long range dependent and have the same marginal distribution as the interarrival times for new HTTP connection traffic. These inter-arrival times are constructed by combining a Fractional Auto-Regressive Integrated Moving Average (FARIMA) time series and an i.i.d. cryptographically secure random sequence. Experiments are conducted on PlanetLab, and the results are validated against recent real traffic trace data. Our experiments demonstrate that the traffic from this timing channel traffic is statistically indistinguishable from legitimate HTTP traffic and undetectable by all current detection schemes for timing channels

CoLoRaDe: A Novel Algorithm for Controlling Long-Range Dependent Network Traffic

Long-range dependence characteristics have been observed in many natural or physical phenomena. In particular, a significant impact on data network performance has been shown in several papers. Congested Internet situations, where TCP/IP buffers start to fill, show long-range dependent (LRD) self-similar chaotic behaviour. The exponential growth of the number of servers, as well as the number of users, causes the performance of the Internet to be problematic since the LRD traffic has a significant impact on the buffer requirements. The Internet is a large-scale, wide-area network for which the importance of measurement and analysis of traffic is vital. The intensity of the long-range dependence (LRD) of communications network traffic can be measured using the Hurst parameter. A variety of techniques (such as R/S analysis, aggregated variance-time analysis, periodogram analysis, Whittle estimator, Higuchi's method, wavelet-based estimator, absolute moment method, etc.) exist for estimating Hurst exponent but the accuracy of the estimation is still a complicated and controversial issue. Earlier research (Rezaul et al., 2006) introduced a novel estimator called the Hurst exponent from the autocorrelation function (HEAF) and it was shown why lag 2 in HEAF (i.e. HEAF (2)) is considered when estimating LRD of network traffic. HEAF estimates H by a process which is simple, quick and reliable. In this research we extend these concepts by introducing a novel algorithm for controlling the long-range dependence of network traffic, named CoLoRaDe which is shown to reduce the LRD of packet sequences at the router buffer

Evaluating the impact of traffic sampling in network analysis

Dissertação de mestrado integrado em Engenharia InformáticaThe sampling of network traffic is a very effective method in order to comprehend the behaviour and flow of a network, essential to build network management tools to control Service Level Agreements (SLAs), Quality of Service (QoS), traffic engineering, and the planning of both the capacity and the safety of the network. With the exponential rise of the amount traffic caused by the number of devices connected to the Internet growing, it gets increasingly harder and more expensive to understand the behaviour of a network through the analysis of the total volume of traffic. The use of sampling techniques, or selective analysis, which consists in the election of small number of packets in order to estimate the expected behaviour of a network, then becomes essential. Even though these techniques drastically reduce the amount of data to be analyzed, the fact that the sampling analysis tasks have to be performed in the network equipment can cause a significant impact in the performance of these equipment devices, and a reduction in the accuracy of the estimation of network state. In this dissertation project, an evaluation of the impact of selective analysis of network traffic will be explored, at a level of performance in estimating network state, and statistical properties such as self-similarity and Long-Range Dependence (LRD) that exist in original network traffic, allowing a better understanding of the behaviour of sampled network traffic.A análise seletiva do tráfego de rede é um método muito eficaz para a compreensão do comportamento e fluxo de uma rede, sendo essencial para apoiar ferramentas de gestão de tarefas tais como o cumprimento de contratos de serviço (Service Level Agreements - SLAs), o controlo da Qualidade de Serviço (QoS), a engenharia de tráfego, o planeamento de capacidade e a segurança das redes. Neste sentido, e face ao exponencial aumento da quantidade de tráfego presente causado pelo número de dispositivos com ligação à rede ser cada vez maior, torna-se cada vez mais complicado e dispendioso o entendimento do comportamento de uma rede através da análise do volume total de tráfego. A utilização de técnicas de amostragem, ou análise seletiva, que consiste na eleição de um pequeno conjunto de pacotes de forma a tentar estimar, ou calcular, o comportamento expectável de uma rede, torna-se assim essencial. Apesar de estas técnicas reduzirem bastante o volume de dados a ser analisado, o facto de as tarefas de análise seletiva terem de ser efetuadas nos equipamentos de rede pode criar um impacto significativo no desempenho dos mesmos e uma redução de acurácia na estimação do estado da rede. Nesta dissertação de mestrado será então feita uma avaliação do impacto da análise seletiva do tráfego de rede, a nível do desempenho na estimativa do estado da rede e a nível das propriedades estatísticas tais como a Long-Range Dependence (LRD) existente no tráfego original, permitindo assim entender melhor o comportamento do tráfego de rede seletivo

Robustness of HEAF(2) for Estimating the Intensity of Long-Range Dependent Network Traffic

The intensity of Long-Range Dependence (LRD) for communications network traffic can be measured using the Hurst parameter. LRD characteristics in computer networks, however, present a fundamentally different set of problems in research towards the future of network design. There are various estimators of the Hurst parameter, which differ in the reliability of their results. Getting robust and reliable estimators can help to improve traffic characterization, performance modelling, planning and engineering of real networks. Earlier research [1] introduced an estimator called the Hurst Exponent from the Autocorrelation Function (HEAF) and it was shown why lag 2 in HEAF (i.e. HEAF (2)) is considered when estimating LRD of network traffic. This paper considers the robustness of HEAF(2) when estimating the Hurst parameter of data traffic (e.g. packet sequences) with outliers

Variable bit rate video time-series and scene modeling using discrete-time statistically self-similar systems

This thesis investigates the application of discrete-time statistically self-similar (DTSS) systems to modeling of variable bit rate (VBR) video traffic data. The work is motivated by the fact that while VBR video has been characterized as self-similar by various researchers, models based on self-similarity considerations have not been previously studied. Given the relationship between self-similarity and long-range dependence the potential for using DTSS model in applications involving modeling of VBR MPEG video traffic data is presented. This thesis initially explores the characteristic properties of the model and then establishes relationships between the discrete-time self-similar model and fractional order transfer function systems. Using white noise as the input, the modeling approach is presented using least-square fitting technique of the output autocorrelations to the correlations of various VBR video trace sequences. This measure is used to compare the model performance with the performance of other existing models such as Markovian, long-range dependent and M/G/(infinity) . The study shows that using heavy-tailed inputs the output of these models can be used to match both the scene time-series correlations as well as scene density functions. Furthermore, the discrete-time self-similar model is applied to scene classification in VBR MPEG video to provide a demonstration of potential application of discrete-time self-similar models in modeling self-similar and long-range dependent data. Simulation results have shown that the proposed modeling technique is indeed a better approach than several earlier approaches and finds application is areas such as automatic scene classification, estimation of motion intensity and metadata generation for MPEG-7 applications