On the impact of time scales on tail behavior of long-range dependent Internet traffic
On the impact of time scales on tail behavior of long-range dependent Internet traffic
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时间尺度对远程依赖互联网流量尾部行为的影响
DOI：
10.1109/icon.2003.1266160
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
S. Asano
中科院分区：
文献类型：
--
作者：
Yusheng Ji;T. Fujino;S. Abe;J. Matsukata;S. Asano
传统上，互联网流量是使用经典的基于泊松的模型建模的。最近的研究提出了分数布朗运动等分数维模型。然而，由于分数布朗运动的简单性，它只能有效地逼近一类完全自相似业务的性能，其相关性可以用单个Hurst参数来描述。在这篇文章中，我们研究了具有更一般相关性的长相关互联网流量的尾部行为。通过分析时间尺度对排队性能的影响，提出了一种分析方法。业务数据的属性是从诸如广域骨干网和局域网的真实网络的业务轨迹中提取的。用实际交通数据模拟得到的结果与用我们的方法得到的分析结果进行了比较。
Conventionally, Internet traffic has been modeled using classical Poisson-based models. More recent studies have proposed fractal models such as fractional Brownian motion. However, due to its simplicity, fractional Brownian motion is only efficient for approximating the performance of a class of exactly self-similar traffic, whose correlation property can be described by a single Hurst parameter. In this paper, we examine the tail behavior of long-range dependent Internet traffic, which has a more general correlation property. We propose an analytical method by focusing on the impact of time scales on queueing performance. The properties of traffic data are extracted from traffic traces of real networks, such as a wide area backbone network and a LAN. Results produced by simulation using real traffic data are compared with analytical results obtained by our method.