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Identifying Long-range Dependent Network Traffic through Autocorrelation Functions

机译:通过自相关函数识别远程相关网络流量

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摘要

For over a decade researchers have been reporting the impact of self-similar long-range dependent network traffic. Long-range dependence (LRD) is of great significance in traffic engineering problems such as measurement, queuing strategy, buffer sizing and admission and congestion control. In this research, in order to determine the existence of LRD, we apply three different robust versions of the autocorrelation function (ACF), namely weighted ACF (WACF), trimmed ACF (TACF) and variance-ratio of differences and sums, known as the D/S variance estimator (DACF), in conjunction with the sample ACF (which is moment based). Here we define the moment based ACF as MACF. In telecommunications, LRD traffic defines that a similar pattern of traffic persists for a longer span of time. Through ACF, it is possible to detect how long the traffic lasts. The aim of this research is to investigate the performance of ACF in identifying the existence of LRD traffic.
机译:十多年来,研究人员一直在报告自相似的远程依赖网络流量的影响。远程依赖关系(LRD)在流量工程问题中具有重要意义,例如测量,排队策略,缓冲区大小以及准入和拥塞控制。在这项研究中,为了确定LRD的存在,我们应用了三种不同的自相关函数(ACF)鲁棒版本,即加权ACF(WACF),修整ACF(TACF)以及差和和的方差比,称为D / S方差估计器(DACF)以及样本ACF(基于矩)。在这里,我们将基于矩的ACF定义为MACF。在电信中,LRD流量定义类似的流量模式会持续较长的时间。通过ACF,可以检测流量持续时间。这项研究的目的是调查ACF在识别LRD交通是否存在方面的性能。

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