In a video-on-demand streaming system, each user watch dufferent video frame according to their arrival time. In the thesis, the concept of multi-source and the forward error correction scheme are implemented to decrease the workload bandwidth of server and reduce the packet loss probability of each peer in the peer-to-peer video-on-demand system. Here, in order to share data to some peers arrive later, each peer must cache part of video data that recently viewed. Each new arrival peer needs to contact multiple sources who have initial part of video data to get video streaming data, and each source transmits different part of video streaming. Once receiving all of these partial stream, the peer will get completely video data. Simulation shows that a suitable preserve time of peers in the system can be used. Thus, the workload bandwidth of server used and memory used by peers can be reduced, too. Furthermore, the packet loss probability is decreased by the sources diversity and the FEC recovery.

In this thesis we explore precision in measurement of buffer overflow and loss probability. We see how buffer overflow probability compares with queuing delay measurements covered in the literature. More specifically, we measure the overflow probability of a packet buffer for various sampling rates to see the effect of sampling rate on the estimation. There are various reasons for measurement in networks; one key context assumed here is Measurement Based Admission Control. We conduct simulation experiments with analytically derived VoIP and bursty traffic parameters, in Matlab, while treating the buffer under consideration as a two-state Markov Chain. We note that estimation error decreases with increase in sampling gap (or in other words precision improves/variance decreases with decrease in sampling rate). We then perform experiments for VoIP and bursty data using NS-2 simulator and record the buffer states generated therein. We see a similar trend of increase in precision with increase in sampling gap. In our simulations, we have mainly considered static traffic passing through the buffer, and we use elastic traffic (TCP) for comparison. We see from our results that the sampling error becomes constant beyond certain asymptotic level. We thus look into asymptotic error in estimation,for the lowest sampling gap, to establish a lower bound on estimation error for buffer loss probability measurement. We use formulae given in recent literature for computing the experimental and theoretic asymptotic variance of the buffer state traces in our scenarios. We find that the theoretical and experimental asymptotic variance of overflow probability match when sampling a trace of buffer states modelled as a two-state Markov Chain in Matlab. We claim that this is a new approach to computing the lower bound on the measurement of buffer overflow probability, when the buffer states are modelled as a Markov process. Using Markov Chain modelling for buffer overflow we further explore the relationship between sampling rate and accuracy. We find that there is no relationship between sampling gap and bias of estimation. Crucially we go on to show that a more realistic simulation of a packet buffer reveals that the distribution of buffer overflow periods is not always such as to allow simple Markov modelling of the buffer states; while the sojourn periods are exponential for the smaller burst periods, the tail of the distribution does not fit to the same exponential fitting. While our work validates the use of a two-state Markov model for a useful approximation modelling the overflow of a buffer, we have established that earlier work which relies on simple Markovian assumptions will thereby underestimate the error in the measured overflow probabilities.

Orientador: Lee Luan Ling / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-08T01:26:33Z (GMT). No. of bitstreams: 1 Vieira_FlavioHenriqueTeles_D.pdf: 4214611 bytes, checksum: 755dfe9865aff1214f8e551afde7541d (MD5) Previous issue date: 2006 / Resumo: A modelagem multifractal generaliza os modelos de tráfego existentes na literatura e se mostra apropriada para descrever as características encontradas nos fluxos de tráfego das redes atuais. A presente tese investiga abordagens para alocação de banda, predição de tráfego e estimação de probabilidade de perda de bytes considerando as características multifractais de tráfego. Primeiramente, um Modelo Multifractal baseado em Wavelets (MMW) é proposto. Levando em consideração as propriedades deste modelo, são derivados o parâmetro de escala global, a função de autocorrelação e a banda efetiva para processos multifractais. A capacidade de atualização em tempo real do MMW aliada à banda efetiva proposta permite o desenvolvimento de um algoritmo de estimação adaptativa de banda efetiva. Através deste algoritmo é introduzido um esquema de provisão adaptativo de banda efetiva. Estuda-se também a alocação de banda baseada em predição de tráfego. Para este fim, propõe-se um preditor adaptativo fuzzy de tráfego, o qual é aplicado em uma nova estratégia de alocação de banda. O preditor fuzzy adaptativo proposto utiliza funções de base ortonormais baseadas nas propriedades do MMW. Com relação à probabilidade de perda para tráfego multifractal, derivase uma expressão analítica para a estimação da probabilidade de perda de bytes considerando que o tráfego obedece ao MMW. Além disso, uma caracterização mais completa do comportamento de fila é efetuada pela obtenção de limitantes para a probabilidade de perda e para a ocupação média do buffer em termos da banda efetiva do MMW. Por fim, é apresentado um esquema de controle de admissão usando o envelope efetivo proposto para o MMW oriundo do cálculo de rede estatístico, que garante que os fluxos admitidos obedeçam simultaneamente aos requisitos de perda e de retardo. As simulações realizadas evidenciam a relevância das propostas apresentadas / Abstract: Multifractal modeling generalizes the existing traffic models and is believed to be appropriate to describe the characteristics of traffic flows of modern communication networks. This thesis investigates some novel approaches for bandwidth allocation, traffic prediction and byte loss probability estimation, by considering the multifractal characteristics of the network traffic. Firstly, a Wavelet based Multifractal Model (WMM) is proposed. Taking into account the properties of this multifractal model, we derive the global scaling parameter, the autocorrelation function and the effective bandwidth for multifractal processes. The real time updating capacity of the WMM in connection with our effective bandwidth proposal allows us to develop an algorithm for adaptive effective bandwidth estimation. Then, through this algorithm, an adaptive bandwidth provisioning scheme is introduced. In this work, we also study a prediction-based bandwidth allocation case. For this end, we develop an adaptive fuzzy predictor, which is incorporated into a novel bandwidth allocation scheme. The proposed adaptive fuzzy predictor makes use of orthonormal basis functions based on the properties of the WMM. Additionally, we derive an analytical expression for the byte loss probability estimation assuming that the traffic obeys the MMW. Besides, a more complete characterization of the queuing behavior is carried out through the estimation of the bounds for the loss probability and mean queue length in buffer in terms of the WMM based effective bandwidth. Finally, an admission control scheme is presented that uses the WMM based effective envelope derived through the statistical network calculus, guaranteeing that the admitted flows simultaneously attend the loss and delay requirements. The computer simulation results confirm the relevance of the presented proposals / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

Made available in DSpace on 2014-07-29T15:08:17Z (GMT). No. of bitstreams: 1 Dissertacao Flavio Geraldo Coelho Rocha.pdf: 3399518 bytes, checksum: feb6250bd7ead671989ce282786ce64e (MD5) Previous issue date: 2011-03-29 / In this dissertation, the multifractal analysis is used in the context of traffic modeling of communications networks by proposing algorithms and techniques for improving the performance of these networks. Thus, in order to better describe traffic networks, a model based on multifractal multiplicative cascade that uses autoregressive processes for multipliers modeling is proposed, the model is called CMAM (Multifractal Cascade with Multipliers Autoregressive Modeling). Simulations are carried out to evaluate the model performance on describing real video traffic traces and wired and wireless real network traffic traces. In addition, considering data loss probability as an important quality of service measure, a mathematical expression for estimating loss probability that takes into account wavelet coefficients associated to the traffic process is proposed. The expression is evaluated, among other ways, through simulations in a wireless network scenario based on OFDM / TDMA technologies. Then, an admission control scheme for network flows which is based on the proposed loss probability estimation is presented and applied to the scenario considered. Simulations show efficiency of the proposed scheme in comparison to other approaches found in the literature. In addition, an adaptive transmission rate allocation scheme that considers the characteristics of multifractal network traffic is proposed, which one is compared to an approach that considers the monofractal traffic properties. Finally, using CMAM for video traffic modeling, a rate control algorithm for video traffic in the source is presented based on exponential modeling of the scale function associated with the quantization parameter Q of the video encoder. / Nesta dissertação, a análise multifractal é utilizada no contexto de modelagem do tráfego de redes de comunicações, propondo algoritmos e técnicas para a melhoria do desempenho destas redes. Assim, com o propósito de descrever melhor o tráfego de redes, propõe-se um modelo multifractal baseado em cascata multiplicativa que utiliza processos autorregressivos para modelagem dos multiplicadores da cascata, o modelo é chamado de CMAM (Cascata Multifractal com Modelagem Autorregressiva para os Multiplicadores). Simulações utilizando o CMAM são conduzidas para avaliar o desempenho deste modelo em descrever séries reais de tráfego de vídeo, de redes com fio, e de redes sem fio. Adicionalmente, considerando a probabilidade de perda de dados como uma importante métrica de qualidade de serviço, uma expressão matemática para estimação de probabilidade de perda, que leva em consideração coeficientes wavelets associados ao processo de tráfego, é proposta. Os resultados da expressão proposta são analisados, dentre outras formas, por meio de simulações realizadas em um cenário de rede sem fio baseado no OFDM/TDMA. Em seguida, é apresentado um esquema de controle de admissão de fluxos baseado na expressão para estimação de probabilidade de perda proposta e aplicado ao cenário considerado. Simulações comprovam a eficiência do esquema proposto em comparação com outras abordagens presentes na literatura. Além disso, um esquema de alocação adaptativa de taxa de transmissão que considera as características multifractais do tráfego de redes é proposto, o qual é comparado com uma abordagem existente na literatura que considera as propriedades monofractais do tráfego. Por fim, utilizando o CMAM para modelagem multifractal do tráfego de vídeo, um algoritmo de controle de taxa de tráfego de vídeo na fonte é apresentado com base na modelagem exponencial da função de escala associada ao parâmetro de quantização Q do codificador de vídeo.

No / Traffic congestion in communication networks can dramatically deteriorate user-perceived Quality-of-Service (QoS). The integration of the Random Early Detection (RED) and priority scheduling mechanisms is a promising scheme for congestion control and provisioning of differentiated QoS required by multimedia applications. Although analytical modelling of RED congestion control has received significant research efforts, the performance models reported in the current literature were primarily restricted to the RED algorithm only without consideration of traffic scheduling scheme for QoS differentiation. Moreover, for analytical tractability, these models were developed under the simplified assumption that the traffic follows Short-Range-Dependent (SRD) arrival processes (e.g., Poisson or Markov processes), which are unable to capture the self-similar nature (i.e., scale-invariant burstiness) of multimedia traffic in modern communication networks. To fill these gaps, this paper presents a new analytical model of RED congestion control for prioritized multi-class self-similar traffic. The closed-form expressions for the loss probability of individual traffic classes are derived. The effectiveness and accuracy of the model are validated through extensive comparison between analytical and simulation results. To illustrate its application, the model is adopted as a cost-effective tool to investigate the optimal threshold configuration and minimize the required buffer space with congestion control.