突发业务流的TES建模方法

高速通信网络存在大量诸如可变速率(VBR)视频这类具有突发性的业务流。突发性,在数学上主要通过业务流的到达时间间隔的边缘概率分布和自相关函数来描述。TES(Transform-Expand-Sample)是一种建模静态随机过程的非参数化技术,能准确匹配边缘概率分布并很好地近似自相关函数。本文简要介绍了TES方法的基本原理,再详细讨论突发业务流的TES建模及其软件实现。对MPEG视频业务的建模的仿真结果表明,TES模型能够很好地表征网络业务流的突发性。     

VBR视频流多重分形建模

该文在小波域多重分形基础上,研究了基于分布、点集(PM)分布的多重分形小波模型(MWM)的性能,并提出了一种具有更好的逼近性能的混合PM-分布形式;同时,针对VBR视频流的I,P,B帧周期分布特性,充分利用异种帧相关性,建立了考虑帧间相关性的混合多重分形小波VBR视频流量模型CMWM(Composite MWM)。仿真试验表明,与传统的短相关和长相关模型相比,具有多重分形特性的CMWM能更加精确地描述MPEG视频业务的统计特性和排队性能。     

一种新的基于场景的VBR MPEG压缩视频源统计模型

由于VBRMPEG视频流可以获得统计复用增益和恒定的图像质量,它已经成为网络视频业务的主流,但是也给网络服务质量的控制带来了困难。因此,我们很有必要针对这些视频业务流对承载它们的网络带来的影响进行研究和评估。精确的MPEGVBR视频源统计模型不但有助于提高网络仿真的准确度,而且有助于研究网络其它方面的问题,如网络延时、分组丢失、延迟扰动等。本文提出了一种新的基于场景的VBRMPEG压缩视频源统计模型,该模型不仅计算简便,能快速产生符合要求的视频流样本,还能同时拟合原始视频业务数据的概率分布情况、自相关函数和自相似特性。     

自相似VBR视频源业务流的建模和仿真

该文在对实际VBR MPEG视频源统计特性分析的基础上,参照分形高斯噪声自相似(Fractional Gaussian Noise Self-Similar)模型产生方法,实现了对ATM网络中最主要业务流VBR视频源流的建模,提出了改进方法,使得对实际源的仿真不仅考虑到了长期相关性,同时也兼顾到了短期相关性。仿真结果表明,经改进的自相似VBR视频源模型是一种较理想的模型。     

ATM网络中自相似业务下复接器性能的研究

近期对大量实际网络的测量表明,现代网络的业务流特性和部分信源的特性,更适于采用具有长期相关性的自相似或分形模型来描述。已有研究表明长期相关业务的排队特性与基于短期相关模型的有很大不同。但对于自相似业务下ATM网络中复接器性能的研究尚未深入进行。本文在提出一种准自相似(QSSP)的长期相关业务流模型的基础上,求得了N路同参数QSSP输入时复接器的信元丢失率和复接增益,并对非同参数输入的情况提出了一种快速的数值求解方法,用于得到复接器的信元丢失率上限。计算机仿真表明了分析的准确性。     

VBR视频流量的小波包分解及其长时预测

长时预测是VBR视频流量预测领域中的难点问题.针对其时变、非线性以及长相关性等特点,提出一种多尺度分解的VBR视频业务的特征提取方法.选择具有任意多分辨分解特性的小波包,对其进行空间划分并求解适合视频信号特征提取的最优分解基.基于最优基对视频信号进行快速多尺度分解,得到了各级节点的小波系数矩阵,建立了基于最小二乘支持向量机与最小均方的小波系数预测方法.最后,根据预测小波系数,进一步提出了基于小波系数逆变换的视频流量长时预测方法.仿真结果验证了此算法的有效性.     

Gamma Poisson混合业务流的自相似性及排队性能研究

参考MMPP的构造过程,提出模型参数更为简化的Gamma Poisson混合模型,通过仿真计算的方法考查业务流的自相似性,研究分析单排机情况下的排队性能。经与几种典型模型的对比分析,结果表明,Gamma Poisson混合模型能更适合于对实际业务流的建模。     

基于径向基小波核的多尺度小波支持向量机

普通支持向量机(SVM)方法用于多尺度回归建模时不能取得满意的精度,而现有的多尺度SVM算法存在只适合均匀分布的样本并可能收敛于局部极值等问题.为解决上述问题,本文提出了一种基于径向基小波核的多尺度小波支持向量机学习算法.文中提出并证明了一种新的径向基小波支持向量核,可提高小波SVM的训练速度和逼近精度.在此基础上,通过解一个二次优化问题可求出多尺度回归建模问题的全局最优解.最终得出的多尺度回归模型能够有效地逼近多尺度信号.仿真结果验证了所提算法的有效性.     

自相似业务流建模及其合成性能分析

基于通信网络仿真软件平台OPNET中的RPG模型，本文实现了一个通用的、与协议无关的自相似业务流发生模型，并通过仿真实验和小波分析的方法验证了该模型的正确性和可用性。在此模型的基础上，着重对自相似业务流合成过程的性能和行为特性进行了大量的实验和分析。研究结果表明，自相似业务流的合成过程仍具有自相似性，而且合成流的自相似参数接近于各独立输入业务流中所具有的最大自相似参数。     

基于认知无线网络的移动终端业务流的分析方法

针对认知移动终端业务流在多网络层具有不同复杂性的特点,提出了一种基于多时空尺度的业务特性分析方法。该方法首先建立多空间尺度业务模型,然后利用多尺度熵方法对终端业务流信息进行特征提取,对比分析不同时空尺度上网络行为的结构复杂度,探索其随时空尺度的变化规律,从而预测下一时段的业务量。实验数据分析的结果表明,该方法能够有效的实现业务流的在线监测。     

Adaptive wavelet graph model for Bayesian tomographic reconstruction

We introduce an adaptive wavelet graph image model applicable to Bayesian tomographic reconstruction and other problems with nonlocal observations. The proposed model captures coarse-to-fine scale dependencies in the wavelet tree by modeling the conditional distribution of wavelet coefficients given overlapping windows of scaling coefficients containing coarse scale information. This results in a graph dependency structure which is more general than a quadtree, enabling the model to produce smooth estimates even for simple wavelet bases such as the Haar basis. The inter-scale dependencies of the wavelet graph model are specified using a spatially nonhomogeneous Gaussian distribution with parameters at each scale and location. The parameters of this distribution are selected adaptively using nonlinear classification of coarse scale data. The nonlinear adaptation mechanism is based on a set of training images. In conjunction with the wavelet graph model, we present a computationally efficient multiresolution image reconstruction algorithm. This algorithm is based on iterative Bayesian space domain optimization using scale recursive updates of the wavelet graph prior model. In contrast to performing the optimization over the wavelet coefficients, the space domain formulation facilitates enforcement of pixel positivity constraints. Results indicate that the proposed framework can improve reconstruction quality over fixed resolution Bayesian methods.     

A multifractal wavelet model with application to network traffic

We develop a new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a special multiplicative structure on the wavelet and scaling coefficients to ensure positive results, the model provides a rapid O(N) cascade algorithm for synthesizing N-point data sets. We study both the second-order and multifractal properties of the model, the latter after a tutorial overview of multifractal analysis. We derive a scheme for matching the model to real data observations and, to demonstrate its effectiveness, apply the model to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close fit to the real data statistics (variance-time plots and moment scaling) and queuing behavior. Although for illustrative purposes we focus on applications in network traffic modeling, the multifractal wavelet model could be useful in a number of other areas involving positive data, including image processing, finance, and geophysics     

Modeling video traffic using wavelets

In this letter we establish a wavelet model for video traffic. Different from the existing methods which model the video traffic in the time domain, we model the wavelet coefficients in the wavelet domain. The strength of the wavelet model includes: (1) an unified approach to model both the long-range and the short-range dependence in the video traffic simultaneously; (2) a computationally efficient method for developing the model and generating high quality video traffic; and (3) feasibility of performance analysis using the model     

基于小波支持向量机的VBR视频流量预测

：VBR视频流量具有时变性、突发性和非线性等变化特点,为了提高VBR视频流量的预测精度,提出一种小波支持向量机的VBR视频流量预测模型(WSVM)。首先对VBR视频流量时间序列进行相空间重构,然后将其输入到小波支持向量机进行学习,建立VBR视频流量预测模型,最后采用仿真实验对模型性能进行测试,并与支持向量机、小波神经网络进行对比。仿真结果表明,相对于其它预测模型,WSVM模型提高了VBR视频流量预测精度,能够更加准确反映VBR视频流量的复杂变化规律。     

一种基于小波变换和PSR-LSSVM的P2P网络流量预测模型

提出一种基于小波变换、相空间重构理论和LS-SVM的P2P流量预测模型。首先将P2P流量分解为小波系数和尺度系数,然后分别对各个系数进行相空间重构,将重构的分量分别通过LS-SVM模型进行预测,最后用小波方法将各个分量的预测值进行再重构,得到原始流量的预测结果。仿真结果表明该模型的预测结果较传统的LS-SVM模型有更高的精度。     

Improved multi-fractal network traffic model and its performance analysis

This paper demonstrates the limitation of the traditional multi-fractal wavelet model(MWM).Through analyzing the multi-resolution behaviors of the real video traffic,we propose an improved MWM model.It synthesizes the traffic traces using another wavelet basis,and can adjust wavelet coefficients and multiplicative coefficients at each time scale,based on the network measurement.Subsequently,multifractal spectra and queue performances of the new model have been analyzed.The simulation proves it can capture the multifractal behaviors of network traces.     

Queuing Models for Dimensioning Interactive and Streaming Services in High-Speed Downlink Packet Access Networks

We consider modeling the statistical behavior of interactive and streaming traffics in high-speed downlink packet access (HSDPA) networks. Two important applications in these traffic categories are web-browsing (interactive service) and video streaming (streaming service). Web-browsing is characterized by its important sensitivity to delay. Video streaming on the other hand is less sensitive to delay, however, due to its large frame sizes, video traffic is more affected by the packet loss resulting from a limited buffer size at the base station. Taking these characteristics into account, we consider modeling the queuing delay probability density function (PDF) of the Web-browsing traffic, and modeling the queuing buffer size distribution of video streaming traffic. Specifically, we show that the queuing delay of the Web-browsing traffic follows an exponential distribution and that the queuing buffer size of video streaming traffic follows a weighted Weibull distribution. Model fitting based on simulated data is used to provide simple mathematical formulations for the different parameters that characterize the PDFs under consideration. The provided equations could be used, directly, in HSDPA network dimensioning and, as a reference, to satisfy a certain quality of service (QoS).     

A novel framework for G/M/1 queuing system based on scheduling-cum-polling mechanism to analyze multiple classes of self-similar and LRD traffic

Provisioning guaranteed Quality of Service (QoS) in multiservice wireless internet is challenging due to diverse nature of end-user traffic (e.g., voice, streaming video, interactive gaming) passing through heterogeneous interconnected domains with their own policies and procedures. Numerous studies have shown that multimedia traffic carried in wireless internet possesses self-similar and long-range dependent characteristics. Nonetheless, published work on wireless traffic modeling is merely based on traditional Poisson traffic distribution which fails to capture these characteristics and hence yield misleading results. Moreover, existing work related to self-similar traffic modeling is primarily based on conventional queuing and scheduling combinations which are simple approximations.This paper presents a novel analytical framework for G/M/1 queuing system based on realistic internet traffic distribution to provide guaranteed QoS. We analyze the behavior of multiple classes of self-similar traffic based on newly proposed scheduling-cum-polling mechanism (i.e., combination of priority scheduling and limited service polling model). We formulate the Markov chain for G/M/1 queuing system and present closed form expressions for different QoS parameters i.e., packet delay, packet loss rate, bandwidth, jitter and queue length. We develop a customized discrete event simulator to validate the performance of the proposed analytical framework. The proposed framework can help in building comprehensive service level agreements for heterogeneous wireless domains.     

Modeling heterogeneous network traffic in wavelet domain

Heterogeneous network traffic possesses diverse statistical properties which include complex temporal correlation and non-Gaussian distributions. A challenge to modeling heterogeneous traffic is to develop a traffic model which can accurately characterize these statistical properties, which is computationally efficient, and which is feasible for analysis. This work develops wavelet traffic models for tackling these issues. We model the wavelet coefficients rather than the original traffic. Our approach is motivated by a discovery that although heterogeneous network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are all “short-range” dependent. Therefore, a simple wavelet model may be able to accurately characterize complex network traffic. We first investigate what short-range dependence is important among the wavelet coefficients. We then develop the simplest wavelet model, i.e., the independent wavelet model for Gaussian traffic. We define and evaluate the (average) autocorrelation function and the buffer loss probability of the independent wavelet model for fractional Gaussian noise (FGN) traffic. This assesses the performance of the independent wavelet model, and the use of which for analysis. We also develop (low-order) Markov wavelet models to capture additional dependence among the wavelet coefficients. We show that an independent wavelet model is sufficiently accurate, and a Markov wavelet model only improves the performance marginally. We further extend the wavelet models to non-Gaussian traffic through developing a novel time-scale shaping algorithm. The algorithm is tested using real network traffic and shown to outperform FARIMA in both efficiency and accuracy. Specifically, the wavelet models are parsimonious, and have a computational complexity O(N) in developing a model from a training sequence of length N, and O(M) in generating a synthetic traffic trace of length M     

Can multiscale traffic analysis be used to differentiate Internet applications?

An accurate mapping of Internet traffic to applications can be important for a broad range of network management and measurement tasks, including traffic engineering, service differentiation, performance/failure monitoring and security. Traditional mapping approaches have become increasingly inaccurate because many applications use non-default or ephemeral port numbers, use well-known port numbers associated with other applications, change application signatures or use traffic encryption. In this paper we will demonstrate that multiscale traffic analysis based on multi-order wavelet spectrum can be used as a discriminator of Internet applications traffic profiles. By performing clustering analysis over the multiscale wavelet spectrum coefficients that are inferred from the measured traffic, the proposed methodology is able to efficiently differentiate different IP applications without using any payload information. This characteristic will allow the differentiation of traffic flows in unencrypted and encrypted scenarios. In order to compare the differentiating potential of different traffic application data, upload, download and joint upload and download flow statistics are considered to evaluate the identification approach for each selected protocol. Moreover, we also evaluate which timescales and spectrum orders are more relevant for the traffic differentiation. From the analysis of the obtained results we can conclude that the proposed methodology is able to achieve good identification results using a small set of timescales of a single order wavelet spectrum of a general raw traffic statistic.