Characterisation and modelling of VBR video traffic

This paper presents a new MPEG-4 video traffic model, which efficiently exploits the frame pattern of MPEG-4 video by using IBB and PBB sequences as the basic units and modeling their dependence in the orthogonal domain after Hadamard transform. This model can accurately capture both short range dependence (SRD) and long range dependence (LRD) characteristics of MPEG-4 video data. Simulation results demonstrate that the probability density function and the autocorrelation function of model data are highly close to those of real data. And in the leaky bucket performance test, using the model data as input source obtains almost the same estimates of the required buffer size or bit loss rate as using real data. Furthermore, our new model needs very few parameters and has high computational efficiency, thus is very suitable to be used as the video source in the study such as cellular network design and evaluation, wireless resource management and so on.

Time series modeling and analysis provides means of predicting the future and has been widely used in a variety of fields ranging from seismology for predicting earthquake and volcanic eruption, to finance for risk assessment, and to quantum information processing. The conventional integer order models can only capture short-range dependence; for example, Poisson processes, Markov processes, autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) processes. In time series analysis, one of the conventional assumptions is that the coupling between values at different time instants decreases rapidly as the time difference or distance increases. However, there are situations where strong coupling between values at different times exhibit properties of long range dependence which cannot be processed by the conventional time series analysis. Typical examples of long range dependence signals include financial time series, underwater noise, electroencephalography (EEG) signal, etc. ARFIMA, a fractional order signal processing technique, is the generalization of the conventional integer order techniques, namely, ARIMA and ARMA methods. Hence, it is capable of capturing both short-range dependence and long-range dependence in signals. Compared to conventional integer order models, the ARFIMA model gives a better fit and result when dealing with the data which possess the long range dependence property. In this paper, we investigate the application of the ARFIMA as well as AR methods to model EEG signals obtained from different brain channels. We analyze the resulting correlations for comparison the benefits of ARFIMA over AR on the EEG data exhibiting the long range dependency property. The results showed that the prediction results have a better performance compared to the conventional ARMA models.

Intuition suggests that a finite buffer limits the effect of traffic auto-correlations on the queue length. We investigate the extent to which finite buffers can, therefore, be expected to mitigate the effects of long-range dependence (LRD). With traffic sequences generated by the fractional autoregressive integrated moving average (f-ARIMA) models for LRD, and by AR models for short range dependence (SRD), we investigate the traffic performance for a range of finite buffers, both for single and multiplexed streams. For design, the aim is to 'match' a given LRD auto-correlation function with a suitable SRD function whose performance provides, for a wide range of traffic intensities and buffer sizes, a conservative bound on the performance associated with the LRD function. The results suggest that by suitably 'dominating' an LRD auto-correlation function by an SRD function, one can obtain conservative performance bounds for a realistic range of traffic intensities and buffer sizes (delays). Also, in several cases, a 'cross-over' phenomenon is observed for cell-loss probabilities as the buffer size increases, i.e., the loss probabilities are smaller for LRD than for SRD for small buffers, with the converse true for large buffers. This suggests that finite buffers, can in some cases, counteract the effects of LRD in traffic arrivals, and thus enable conservative designs to be based on Markovian traffic models.

Network traffic exhibits fractal properties, such as self- similarity and impulsiveness, and posseses a complex correlation structure, i. e., both long and short-range dependence are present. Models such as Fractional Gaussian Noise can capture the long-range dependence but not the short-range dependence. Therefore, a key issue that must be adressed when using synthesized traffic for network simulations is the incorporation of short-range dependence into the generated trace. This work develops a filter-based method for the introduction of short- range dependence into long-range dependent sample paths that are synthesized via wavelets and studies a method of mapping from a given time series to a packet stream. The generator does not employ training sequences, i. e., it is not based on the use of real traffic traces as training sequences.

It is widely accepted that the video frame size process is long range dependent (LRD), which has been indicated by a number of statistical tests. This approach suggests the use of LRD traffic models for traffic engineering, which in many cases leads to subexponential queue tails in the network buffers. Basically, there are two concepts that are considered as the origin of LRD in video modeling: self-similarity and heavy tailed level durations. In this paper we argue for a new approach, where the frame size sequence is described by three processes representing three explicit time scales, all of them having a physical meaning. We demonstrate that this concept serves as an explanation for the outcome of the LRD tests. The properties that are attributed to long range dependence in fact seem to be the result of non-stationarity of the large time scale process. We also show that the high frequency component is short range dependent, thus questioning the presence of long range dependence in video traffic.

We investigate transport layer processing intended to improve the communication of multimedia data over wireless and wireline networks. Specifically, we consider data striping and thinning techniques, which are applicable to the multipath/multiflow transmission of multimedia (e.g., MPEG-4). We also introduce the shuffling procedure, which reorders the data at the network edges. We show that all three techniques break up short-term correlations in data traffic thus improving its queueing performance. We demonstrate that while both long range dependence (LRD) and short range dependence (SRD) influence the queueing performance for some timescale of the queueing system, it is the short-range statistical properties of multimedia traffic within the critical time scale that are dominant in determining the buffer efficiency of the queue. We also show that estimation of LRD for thinned and striped data may lead to a misleading notion of LRD reduction, when none is warranted. We further outline the ideas for a new transport layer protocol that explores the combination of thinning, striping and shuffling approaches to multimedia data transmission.

Accurate modeling of VoIP traffic QoS parameters in current and future networks with multifractal and Markov models

The paper deals with the applicability of fractional AutoRegressive Integrated Moving Average (f-ARIMA) processes to traffic modelling. This study is suggested by the ability of f-ARIMA processes in separately capturing both the long range dependence (LRD) and short range dependence (SRD) features of actual traffic. Indeed, the f-ARIMA models permit one to produce synthetic traces with a well-controlled LRD/SRD component ratio. The practical relevance of this feature is tested analytically, by means of discrete event simulations, the queueing performance in several working conditions for two different actual-traffic data sets, respectively related to a LAN-to-LAN interconnection and a videoconference service. In particular, the latter data set, characterised by a strong low frequency SRD component, emphasises the improvement on queueing performance evaluation introduced by a low-order f-ARIMA (p,d,0) model with respect to a pure LRD one. On the contrary, the inadequacy of the f-ARIMA model is highlighted when considering the LAN traffic characterised by a quite complex SRD component.

Starting from a consideration of the Long Range Dependence (LRD) behavior of sea SAR image spectra, an overview is given of the LRD approaches currently being used to achieve reliable sea surface anomalies discrimination from high resolution sea SAR images. In this paper, the problem of SAR image analysis to discriminate oil slicks from low wind areas on the sea surface is addressed by employing fractional exponential (FEXP) models and short range dependence (SRD) parameters. The presented method demonstrated reliable results when applied to European remote sensing 2 (ERS-2) SAR precision images (PRI) and ERS-2 SAR ellipsoid geocoded images (GEC) of the Atlantic and the Pacific Oceans.

The objective of this paper is to demonstrate the limitations of the variance-time (VT) test as a statistical tool for inferring long-range dependence (LRD) in network traffic. Since the early Bellcore studies, LRD has been in the center of a continuous debate within the teletraffic community. The controversy is typically focused on the utility of LRD models to predict the performance at network buffers. Our work here is not meant to advocate one modeling approach over another, but to point out (experimentally and theoretically) to the caveats in using the VT test as a tool for detecting LRD. We do that by deriving simple analytical expressions for the slope of the aggregated variance in three autocorrelated traffic models: M/G//spl infin/ process (short-range dependent (SRD) but non-Markovian), the discrete autoregressive of order one model (SRD Markovian), and the fractional ARIMA process (LRD). Our main result is that the VT test often indicates, falsely, the existence of an LRD structure (i.e., H>0.5) in synthetically generated traces from the two SRD models. The bias in the VT test, however, diminishes monotonically with the length of the trace. We provide some guidelines on selecting the minimum trace length so that the bias is negligible.

In recent years, empirical studies on network traffic both in various network configurations including local area networks (LAN) and wide area networks (WAN) convincingly show that the actual traffic exhibit self-similarity and long range dependence (LRD), which are very different from that predicted by traditional telecommunication traffic models, such as Poisson process. Self-similarity in nature brings about the long range dependent burstiness of network traffic. The experimental evidences reveal that the heavy tailness is the key cause for the self-similarity of the network traffic. We present two distinctive predictors based on α-stable innovation for the LRD traffic. The two predictors can minimize the dispersion according to the minimum dispersion criteria with infinite variance. The final predicted values are obtained by combining the previous two individual predicted values. The predicted results for the actual traces show that the two individual predictors are precise and effective, the last compound predictors can enhance the final predicted accuracy.

This study evaluates the time-varying long range dependence behaviors of the S&P 500 volatility, index using the modified rescaled adjusted range (R/S) statistic which takes into account the possible covariances of the lags in the data. 5916 Chin Wen Cheong et al. Instead of a single estimation for the whole time span, the variations of the long range dependence are computed using the moving window rolling estimates approach. For a better computational result, a high frequency data set under the representation of bipower variation realized volatility is used to avoid possible abrupt jump. As for the empirical study, we have selected the period covered before and after the subprime mortgage crisis. The empirical results show that the long range dependence is influenced by the related economic events across the studied period. This time varying long range dependence analysis allow us to understand the informationally market efficiency before and after the subprime mortgage crisis.

Heavy traffic analysis for a multiplexer driven by M∨GI∨∞ input processes

Network study finds simultaneous presentation of long-range dependence and short-range dependence in Network traffic., which makes the performance of the traditional model used to describe the short-range dependence for prediction lower. And in different scales of these two characteristics of network traffic have different impact for network performance. In small enough scale the short-range dependence has a greater impact on network performance, while in large enough scale, long-range dependence characteristics play a leading role. Therefore, in order to obtain better prediction this paper makes network traffic sequence more smooth and easy to be fitted by the traditional model, through weakening the dependence not affording a major role in small scale and enhancing the dependence playing a leading role in the impact of network.

Markov-modulated Poisson process (MMPP) is one of the popular non-self-similar schemes for modeling network traffic. They are used for modeling both short-range dependent (SRD) and long-range dependent (LRD) traffic. The paper verifies Algorithm LAMBDA for fitting MMPP to synthetic traffic and evaluates the accuracy of the fitted MMPP model. We provide evidence that, for self-similar traffic, the performance highly depends on Hurst parameter. In some cases, the algorithm exhibits some inaccuracy in mean queuing delays. We try to modify it by a simple change in order to make the algorithm adaptive, and consequently, make the results more realistic.