Wavelet Based Estimators of Long-range Dependencies in Traffic Traces
نویسندگان
چکیده
It has been shown that Internet traffic possesses a high degree of self-similarity and long-range dependency. Hence, classical statistical models cannot adequately model the fundamental properties of such traffic. The degree of long-range dependency is captured by the Hurst parameter. Various statistical estimators can be used to estimate the long-range dependency and consequently, the Hurst (H) parameter. Such estimation is always based on o bserving mostly the second moment of the processes on various time scales. Wavelets are a mathematical technique that can be used to observe an arbitrary signal on various time scales. In the first part of this project, we applied wavelet based monofractal and multifractal estimators to long-range dependent video traffic traces in order to evaluate the performances of the estimators. In the second part, the same video traces were used as an input to ns-2 simulator in order to observe the packet loss processes. By analyzing the results obtained we are able to conclude that both wavelet estimators provide consistent and useful graphical output. By observing the graphical output we could determine the time scale in the trace, where long range dependency is present. However, the values of H produced by estimator are somewhat unreliable and need to be compared to values of H obtained by other means. By examining the algorithms and procedures used in estimators, we conjecture that the iv underlying assumptions of Gaussian distribution of wavelet coefficient may be the cause of the unstable performance of the estimator. Also, by considering the multifractal properties of the traces, we conjecture that there is a possibility that a single H parameter may not be sufficient to fully describe the self-similarity of a statistical process In the case of the loss process we shown that the loss process, being strongly influenced by the traffic, also possesses a strong degree of self-similarity and long-range dependency. Such long-range dependant behaviour remains unchanged regardless of the buffer size.
منابع مشابه
Wavelet-based Estimation of Long-range Dependence in Video and Network Traffic Traces
Correct and efficient estimation of the Hurst parameter H of long-range dependent (LRD) traffic is important in traffic analysis. The low computational cost and the wavelets’ scale invariance make wavelet transform suitable for analysis of LRD processes. In this thesis, we apply wavelet-based estimation of H to MPEG-1 and MPEG4 encoded video sequences. Frequency-domain estimators (periodogram a...
متن کاملMeasurement and Analysis of Traffic in a Hybrid Satellite-terrestrial Network
Measurement and analysis of traffic traces are important for better understanding of network behaviour. In this research, we collected traffic traces from a hybrid satelliteterrestrial network operated by Chinasat, a commercial satellite Internet service provider. We performed traffic analysis on packet, connection, protocol, and application layers. We investigated self-similar and long-range d...
متن کاملWavelet Analysis of Long-Range-Dependent Traffic
A wavelet-based tool for the analysis of long-range dependence and a related semi-parametric estimator of the Hurst parameter is introduced. The estimator is shown to be unbiased under very general conditions, and efficient under Gaussian assumptions. It can be implemented very efficiently allowing the direct analysis of very large data sets, and is highly robust against the presence of determi...
متن کاملRobust estimation of the self-similarity parameter in network traffic using wavelet transform
This article studies the problem of estimating the self-similarity parameter of network traffic traces. A robust wavelet-based procedure is proposed for this estimation task of deriving estimates that are less sensitive to some commonly encountered non-stationary traffic conditions, such as sudden level shifts and breaks. Two main ingredients of the proposed procedure are: (i) the application o...
متن کاملRobust estimation of self-similarity parameter in network traffic using wavelet transform
This article studies the problem of estimating the self-similarity parameter of network traffic traces. In order to guard against possible departures from standard modelling assumptions, a robust wavelet-based procedure is proposed for this estimation task. Two main ingredients of the proposed procedure are: (i) the application of a robust regression technique for estimating the parameter from ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2002