| 自相似网络通信量模型研究综述 |
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| 引用本文： | 邵立松,窦文华.自相似网络通信量模型研究综述[J].电子与信息学报,2005,27(10):1671-1676. |
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| 作者姓名： | 邵立松 窦文华 |
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| 作者单位： | 国防科学技术大学计算机学院并行与分布处理实验室,长沙,410073 |
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| 摘 要： | 越来越多的研究表明网络通信量不是Markov过程，而是在任意时间尺度上都具有突发特性，即自相似特性。描述网络通信量的数学模型主要有自相似和长相关结构。网络的某些参数服从重尾分布，从而导致网络通信量时间尺度上的突发特性。该文分析了传统网络通信量模型和性能分析的弊端，描述了新型网络通信量模型应该具有的基本特征。本文重点研究了网络自相似通信量相关的ON/OFF模型、用户访问概率模型和网络流量闭环模型，讨论了相关的研究方向，并总结了在研究网络通信量模型的过程中应该注意的原则和问题。
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| 关 键 词： | 网络通信量 自相似 重尾 |
| 文章编号： | 1009-5896(2005)10-1671-06 |
| 收稿时间： | 2004-03-12 |
| 修稿时间： | 2005-03-31 |
Survey on Self-similar Network Traffic Model |
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| Shao Li-song,Dou Wen-hua.Survey on Self-similar Network Traffic Model[J].Journal of Electronics & Information Technology,2005,27(10):1671-1676. |
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| Authors: | Shao Li-song Dou Wen-hua |
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| Affiliation: | National Laboratory for Parallel & Distributed Processing, School of Computer,National University of Defense Technology, Changsha 410073, China |
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| Abstract: | More and more researches show that network traffic is not Markovian process, but shows the burst nature called "self-similarity" at any time scale .The mathematic models describing network traffic mainly include self-similar process and long range dependence structure. Due to some network parameters obeying heavy tail distribution, network traffic shows the burst nature at large time scale. This paper analyses the drawbacks of the classical network traffic models and performance evaluation, and describes the basic trademarks of evolutionary traffic models. This paper studies three important models of self-similar network traffic: ON/OFF model, user access probability model and fluid flow close loop model, and discusses relative research directions. Some issues and principles that shall be noticed during studying and modeling network traffic are given in the end. |
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| Keywords: | Network traffic Self-similarity Heavy tail |
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