【作者】 高兴；

【导师】 谭献海；

【作者基本信息】 西南交通大学 ， 计算机应用技术， 2007， 硕士

【摘要】 网络业务自相似性的发现对网络业务建模、性能评价和网络控制技术的研究产生了重要影响。传统的网络模型在描述实际网络业务时，认为网络通信量具有Markov性，并在此基础上建立了以Poisson过程为主的数学描述模型，这种模型具有短程相关结构。大量实际测量发现网络通信量具有普遍的自相似性(或长程相关性)。长程相关性在多个时间尺度上存在，并且在大时间尺度上对网络时延、抖动、丢包率以及吞吐量等网络性能具有重大的影响。传统的网络业务模型忽略了自相似的重要特性，不能真实地刻画实际的网络业务。本文深入研究自相似业务网络仿真和性能评价问题。论文首先介绍自相似的常见定义，描述自相似过程在数学和物理上的若干特征；研究网络自相似业务的建模与流量数据生成方法，并对这些业务模型的性能进行了分析；通过仿真实验研究了自相似特性对网络性能的影响。论文在归纳总结自相似基本概念的基础上，研究了常见自相似业务模型如ON/OFF、FGN、FBM、FARIMA等的实现过程，并分析了基于这些模型所产生的自相似流量序列的准确性。研究结果表明ON/OFF模型生成的序列接近期望值，但序列的Hurst系数是不稳定的，随序列长度改变而改变。相比其它模型，FGN模型产生的业务序列比较稳定和准确。FARIMA模型具有长短交织的相关函数结构，与实际中的大部分流量的分形相关函数结构吻合，是目前为止较为理想的一种算法。论文接着对网络性能评价的相关模型、指标进行了讨论，重点分析了FBM、FARIMA模型的排队性能。然后对本论文使用的仿真模型和实验数据来源进行了讨论，通过仿真实验对自相似网络排队性能进行了分析总结。在网络性能分析中排队性能是一个重要的指标。本文采用自相似业务模型产生的不同数据来驱动OPNET的G/M/1模型进行仿真，讨论了影响排队时延、队长和包丢失概率的因素。研究结果表明发包间隔时间的分布特征决定了排队性能，ON/OFF模型的排队性能与ON/OFF的Pareto分布有直接关系，自相似序列比短相关序列有更大的排队时延和丢包率，而且变化也更剧烈。方差对排队性能指标有重要影响。更多还原

【Abstract】 The discovery of the self-similar characteristic of network traffic has great influence on network traffic modeling, performance evaluation and network control. Traditional Poisson-based models of network traffic are based on the hypothesis of Markov which has the nature of short-range dependence (SRD). Recent traffic analysis from various packet networks shows that network traffic processes exhibit ubiquitous properties of self-similarity and long range dependence (LRD), i.e. of correlation over a wide range of time scales. LRD exists on multiple time scales and has great influences on network performances such as delay, jitter, cell loss rate and throughput on the large time scale. The traditional Poisson-based models neglect the important characteristic of self-similariy. They can not capture the actual characteristic of network traffic accurately.In this thesis, the author studies the problems of network simulation and performance evaluation of self-similar traffic with depth. Firstly, several mathematical definitions of self-similarity are given. Some mathematical and physical features describing the self-similar processes are described. The methods of modeling and generation of the self-similar traffic are researched and discussed. The performance of these models is analyzed. The influence of network performance on self-similariy is studied through simulation.Based on the conclusion of the concepts of self-similarity, the implementation of the self-similar models, such as ON/OFF, Fractional Gaussian Noise (FGN), Fractional Brownian Motion (FBM), Fractional Auto-Regressive Integrated Moving Average (FARIMA), are introduced. The precision of self-similarity traffic series generated by these models is analyzed. The relults reveal that although the Hurst coefficient of traffic series generated by ON/OFF model is close to the expected value, its Hurst coefficient does not remain stable and changes according to the length of the series. Compared to other models, FGN model is more precise and stable. FARIMA model is consisting with both long and short range dependent structure and suit to the actual fractional structure. So it is much better than other modelsSecondly, relative models, parameters and indexes of network performance evaluation are analyzed. The author focuses on the queuing performance of FBM and FARIMA. The simulation model and the source of simulation are discussed. The performance of self-similar traffic is analyzed and summed up through extensive simulation.Queuing performance is a key index of network performances analysis. In this thesis, based on the OPNET simulation which is driven by the traces generated with self-similar traffic model described above, the performances and its influence factors of G/M/1 queuing model are analyzed. The author focuses on the study of influence factors on network performances, such as queuing delay, queue length and cell loss rate. The research results demonstrate that the queuing performance is determined by the distribution of packet interval time. The queuing performance of ON/OFF traffic model is related to Pareto distribution of ON or OFF period. Self-similar traffic results in much worse queuing performance than short range dependence traffic, and the variation of the performance of self-similar traffic is more violent. Variance of the self-similar traffic has a great affect on the queuing performance.更多还原