一种基于瑞利分布的VBR视频流的小波模型

本文提出了一种新型的视频业务流模型,以Haar小波的多分辨率分析为基础,在尺度空间和小波空间分别建模,然后通过小波反变换得出仿真业务流.在最"粗"的尺度空间里,我们根据视频流的概率分布特点,采用基于瑞利(Rayleigh)分布的AR模型对尺度系数建模;在各个小波空间里,采用一般的高斯不相关小波模型(WIG,Wavelet Independent Guassian)建模.由于在尺度空间和小波空间针对各自的特点作了不同的处理,本文模型不但能较好拟合复杂业务流在各个时间尺度的概率分布特性,也能拟合其长时相关的特性.另外,在多尺度排队分析(MSQ,MultiScale Queue)的框架下,我们还推导出了基于本文模型的排队分析的理论结果.最后,通过对实际视频业务流数据仿真实验与排队分析验证了本文模型的有效性.     

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     

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

As a special type of denial of service (DoS) attacks, the TCP‐targeted low‐rate denial of service (LDoS) attacks have the characteristics of low average rate and strong concealment, so it is difficult to identify such attack traffic. As multifractal characteristics exist in network traffic, a new identification approach based on wavelet transform and combined neural network is proposed to classify normal network traffic and LDoS attack traffic. Wavelet energy spectrum coefficients extracted from the sampled traffic are used for multifractal analysis of traffic over different time scale. The combined neural network is designed to classify these multiscale spectrum coefficients that show different multifractal characteristics belonging to normal network traffic and LDoS attack traffic. Test results of test‐bed experiments indicate that the proposed approach can identify LDoS attack traffic accurately.     

A reconstructing approach to end‐to‐end network traffic based on multifractal wavelet model

This paper studies the reconstructing method of end‐to‐end network traffic. Due to the development of current communication networks, our networks become more complex and heterogeneous. Meanwhile, because of time‐varying nature and spatio‐temporal correlations of the end‐to‐end network traffic, to obtain it accurately is a great challenge. We propose to exploit discrete wavelet transforms and multifractal analysis to reconstruct the end‐to‐end network traffic from time–frequency domain. First, its time–frequency properties can be characterized in detail by discrete wavelet transforms. And then, we combine discrete wavelet transforms and multifractal analysis to reconstruct end‐to‐end network traffic from link loads. Furthermore, our method needs to measure end‐to‐end network traffic to build the statistical model named multifractal wavelet model. Finally, simulation results from the real backbone networks suggest that our method can reconstruct the end‐to‐end network traffic more accurately than previous methods. Copyright © 2013 John Wiley & Sons, Ltd.     

一种基于小波变换和FIR神经网络的广域网网络流量预测模型

该文提出了一种基于小波变换和FIR神经网络的广域网网络流量预测模型,首先采用小波分解把网络流量数据分解成小波系数和尺度系数,即高频系数和低频系数,将这些不同频率成分的系数单支重构为高频流量分量和低频流量分量,利用FIR神经网络对这些分量分别进行预测,将合成之后的结果作为原始网络流量的预测。实验结果表明:采用该模型对实际的广域网网络流量数据进行预测,不仅可以得到较快的收敛效果,而且预测性能比现有的小波神经网络和FIR神经网络要好得多。     

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     

MPEG-4视频业务的多重分形分析与建模

基于对象编码的MPEG-4视频业务将是网络业务中的主流业务，因而研究MPEG-4视频业务的特性对网络设计，容量规划，性能评估，接入控制与性能分析有重要的意义。通过应用多重分形分析方法对MPEG-4视频业务的分形行为进行分析，结果表明MPEG-4视频业务多重分形特性的存在，因而采用多重分形小波模型对MPEG-4视频业务进行建模分析，通过仿真试验结果的比较和分析，表明该模型能够真实反映MPEG-4视频业务的突发特性。     

Adaptive envelope process and minimum service curve applied to resource block allocation in the LTE downlink

In this work, we propose a resource allocation algorithm for the LTE downlink that makes use of an adaptive multifractal envelope process and a minimum service curve. The proposed scheduling algorithm aims to improve some network parameters while guaranteeing a maximum delay to the user by considering the following information: backlog, channel condition and user traffic behavior. In order to estimate the maximum network delay, we propose an adaptive minimum service curve for the LTE network that can be used for admission control purposes in the resource allocation algorithm. The performance of the proposed scheduling algorithm is compared to those of several scheduling schemes known in the literature through computational simulations of the LTE downlink. In order to develop a new adaptive envelope process and to precisely describe network flows, we propose an adaptive algorithm to estimate the parameters of the Multifractal Wavelet Model (MWM). The proposed envelope process is compared to the main traffic model based envelope processes known in the literature. Simulations of the LTE downlink considering AMC (Adaptive Modulation and Coding) are carried out showing the efficiency of the proposed resource allocation approach that considers adaptive estimation of network traffic parameters.     

WPANFIS: combine fuzzy neural network with multiresolution for network traffic prediction

A novel methodology for prediction of network traffic,WPANFIS,which relies on wavelet packet transform(WPT)for multi-resolution analysis and adaptive neuro-fuzzy inference system(ANFIS)is proposed in this article.The widespread existence of self-similarity in network traffic has been demonstrated in earlier studies,which exhibits both long range dependence(LRD)and short range dependence(SRD).Also,it has been shown that wavelet decomposition is an effective tool for LRD decorrelation.The new method uses WPT as extension of wavelet transform which can decoorrelate LRD and make more precisely partition in the high-frequency section of the original traffic.Then ANFIS which can extract useful information from the original traffic is implemented in this study for better prediction performance of each decomposed non-stationary wavelet coefficients.Simulation results show that the proposed WPANFIS can achieve high prediction accuracy in real network traffic environment.     

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。     

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     

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.     

一种概率自适应图像去噪模型

从小波变换入手,提出了一种概率自适应去噪模型.该模型包括尺度层间模型和层内模型.去噪方法首先利用小波域层间模型,将小波系数分成两类:有意义系数和无意义系数;然后在层内概率模型下运用最大后验概率估计方法,从有意义系数中恢复出原始系数.我们还将这种模型引入复数小波变换域.实验结果及分析表明了该去噪模型的有效性.     

Multiplicative multifractal modelling of long‐range‐dependent network traffic

We present a multiplicative multifractal process to model traffic which exhibits long‐range dependence. Using traffic trace data captured by Bellcore from operations across local and wide area networks, we examine the interarrival time series and the packet length sequences. We also model the frame size sequences of VBR video traffic process. We prove a number of properties of multiplicative multifractal processes that are most relevant to their use as traffic models. In particular, we show these processes to characterize effectively the long‐range dependence properties of the measured processes. Furthermore, we consider a single server queueing system which is loaded, on one hand, by the measured processes, and, on the other hand, by our multifractal processes (the latter forming a MF_e/MF_g/1 queueing system model). In comparing the performance of both systems, we demonstrate our models to effectively track the behaviour exhibited by the system driven by the actual traffic processes. We show the multiplicative multifractal process to be easy to construct. Through parametric dependence on one or two parameters, this model can be calibrated to fit the measured data. We also show that in simulating the packet loss probability, our multifractal traffic model provides a better fit than that obtained by using a fractional Brownian motion model. Copyright © 2001 John Wiley & Sons, Ltd.     

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。     

Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction

In this paper, a multiresolution finite-impulse-response (FIR) neural-network-based learning algorithm using the maximal overlap discrete wavelet transform (MODWT) is proposed. The multiresolution learning algorithm employs the analysis framework of wavelet theory, which decomposes a signal into wavelet coefficients and scaling coefficients. The translation-invariant property of the MODWT allows alignment of events in a multiresolution analysis with respect to the original time series and, therefore, preserving the integrity of some transient events. A learning algorithm is also derived for adapting the gain of the activation functions at each level of resolution. The proposed multiresolution FIR neural-network-based learning algorithm is applied to network traffic prediction (real-world aggregate Ethernet traffic data) with comparable results. These results indicate that the generalization ability of the FIR neural network is improved by the proposed multiresolution learning algorithm.     

A novel traffic identification approach based on multifractal analysis and combined neural network

An accurate identification of Internet traffic of different applications is highly relevant for a broad range of network management and measurement tasks, including traffic engineering, service differentiation, performance monitoring, and security. Traditional traffic identification approaches have become increasingly inaccurate due to restrictions of port numbers, protocol signatures, traffic encryption, and etc. In this paper, a new traffic identification approach based on multifractal analysis of wavelet energy spectrum and classification of combined neural network models is proposed. The proposed approach is able to achieve the identification of different Internet application traffic by performing classification over the wavelet energy spectrum coefficients that were inferred from the original traffic. Without using any payload information, the proposed approach has more advantages over traditional methods. The experiment results illustrate that the proposed approach has satisfactory identification results.     

基于小波域统计建模及显著性修正的SAR图像相干斑抑制

该文提出了一种基于小波域统计建模与小波系数显著性修正相结合的斑点噪声滤波方法。这种方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,对对数变换后的图像进行小波变换并对小波域的高频子带系数用混合高斯模型与隐马尔可夫树模型进行建模,并采用EM算法来估计模型参数。在模型参数估计的基础上;利用贝叶斯最小均方误差准则来估计干净的小波系数。在此基础上引入基于显著性准则的小波系数修正,最后通过小波逆变换与指数变换获得抑制斑点噪声后的图像。用真实SAR图像实验表明,该文提出的方法能够有效地抑制斑点噪声,同时能够很好地保存边缘细节结构与强散射中心。     

一种基于小波的网络流量发生器设计

网络流量发生器在网络性能分析和协议实现中具有重要的作用。文章基于多分形小波模型，设计了一种自相似网络流量发生器。其中对随机数的生成、序列的截断、自相似序列的生成以及自相似流量的生成等主要设计部分进行了论述。与常用的基于分形布朗运动的模型的流量发生器相比，该发生器生成的流量具有更准确的自相似流量特性。     

基于Laplace分布的非下采样小波包脉冲星信号消噪

为了提高脉冲星信号的去噪效果,提出了一种基 于非下采样小波包(NWP)分解的局部Laplace模型消噪方法。 首先对真实脉冲星信号进行NWP分解,统计真实脉冲星信号NWP系数的分布特性, 建立真实脉冲星信号小波包系数的Laplace分布模型;然后在Laplace先验概率分布的基础 上,根据最大后 验概率(MAP)估计准则,利用含噪脉冲星信号的小波包系数对真实脉冲星信号的小波包系数 进行有效估算;最后 对估算出的小波包系数进行NWP重构,得到消噪后的脉冲星信号。采用不同 的脉冲星信号进行实 验分析的结果表明,与经典的基于高斯分布的非下采样小波(NSW)消噪和NWP消噪相比,本文 方法可以 更有效地去除噪声,同时更好地保留信号中的微脉冲等细节信息,在信噪比(SNR)、均方根误差(RMSE)、相关系数(CC)和峰值相对误差(REPV)等都 有较好的改善。