自相似網(wǎng)絡(luò)通信量模型研究綜述
Survey on Self-similar Network Traffic Model
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摘要: 越來越多的研究表明網(wǎng)絡(luò)通信量不是Markov過程，而是在任意時間尺度上都具有突發(fā)特性，即自相似特性。描述網(wǎng)絡(luò)通信量的數(shù)學(xué)模型主要有自相似和長相關(guān)結(jié)構(gòu)。網(wǎng)絡(luò)的某些參數(shù)服從重尾分布，從而導(dǎo)致網(wǎng)絡(luò)通信量時間尺度上的突發(fā)特性。該文分析了傳統(tǒng)網(wǎng)絡(luò)通信量模型和性能分析的弊端，描述了新型網(wǎng)絡(luò)通信量模型應(yīng)該具有的基本特征。本文重點研究了網(wǎng)絡(luò)自相似通信量相關(guān)的ON/OFF模型、用戶訪問概率模型和網(wǎng)絡(luò)流量閉環(huán)模型，討論了相關(guān)的研究方向，并總結(jié)了在研究網(wǎng)絡(luò)通信量模型的過程中應(yīng)該注意的原則和問題。
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關(guān)鍵詞:
- 網(wǎng)絡(luò)通信量;自相似; 重尾
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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