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首页> 外文期刊>ACM Transactions on Modeling and Computer Simulation >Discrete-Time Heavy-Tailed Chains, and Their Properties in Modeling Network Traffic
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Discrete-Time Heavy-Tailed Chains, and Their Properties in Modeling Network Traffic

机译：离散时间重尾链及其在网络流量建模中的属性

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摘要

The particular statistical properties found in network measurements, namely self-similarity and long-range dependence, cannot be ignored in modeling network and Internet traffic. Thus, despite their mathematical tractability, traditional Markov models are not appropriate for this purpose, since their memoryless nature contradicts the burstiness of transmitted packets. However, it is desirable to find a similarly tractable model which is, at the same time, rigorous at capturing the features of network traffic.rnThis work presents discrete-time heavy-tailed chains, a tractable approach to characterize network traffic as a superposition of discrete-time "on/off" sources. This is a particular case of the generic "on/off" heavy-tailed model, thus shows the same statistical features as the former, particularly self-similarity and long-range dependence, when the number of aggregated sources approaches infinity.rnThe model is then applicable to characterize a number of discrete-time communication systems, for instance, ATM and optical packet switching, to further derive meaningful performance metrics such as average burst duration and the number of active sources in a random instant.
机译：在对网络和Internet流量进行建模时，不能忽略网络测量中发现的特定统计属性，即自相似性和远程依赖性。因此，尽管传统的马尔可夫模型具有数学上的易处理性，但由于其无记忆的特性与传输数据包的突发性相矛盾，因此不适用于该目的。但是，理想的是找到一个类似的易于处理的模型，同时严格地捕获网络流量的特征。这项工作提出了离散时间的重尾链，这是一种易于处理的方法，可将网络流量表征为网络流量的叠加。离散时间“开/关”源。这是通用“开/关”重尾模型的一个特例，因此当聚合源的数量接近无穷大时，显示出与前者相同的统计特征，尤其是自相似性和长期依赖性。然后可用于表征许多离散时间通信系统，例如ATM和光分组交换，以进一步得出有意义的性能指标，例如平均突发持续时间和随机瞬间中的活动源数。

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