高突发性自相似网络业务流量理论及建模分析研究
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摘要
近年来,随着各种网络多媒体应用的不断开展,网络业务流量建模理论研究以及以此为基础的网络流量分析研究得到了当前计算机网络通信研究领域相关研究者的广泛关注。本文在国家自然科学基金项目“基于共变正交和联合优化的多媒体网络性能预测模型(60502023)”和与珠海移动公司合作项目“城域网数据业务接入网络拓扑实验研究”的资助下,对网络业务流量特性的精确刻画以及基于此的网络流量分析等核心问题进行了研究,其中的关键问题包括对网络业务流量的建模、对相应分布参数的准确估计以及流量模型的分析方法等等。
“自相似”是当前网络业务流量模型研究中不得不提的一个概念。自相似性是多媒体网络业务流量不同于电话网业务流量的本质特性。对于现代网络业务流量,采用具有自相似特性的模型比传统的泊松模型更接近实际网络业务流的特性。因而以自相似性为突破口深入研究,将有助于深刻理解互联网业务流的本质特性,同时从根本上保证研究的可行性和准确性。
本文首先深入研究了自相似的基本理论,并对比分析了当前各种真实网络环境的网络业务流量数据研究结论,从而确立了通过自相似理论研究网络业务流量的研究方向。本文亦将此作为全文的主线。紧接着,本文深入研究讨论了当前主要的自相似Hurst参数估计方法。在绪论最后一部分,结合网络业务流量模型和网络业务流量突发性、长相关、自相似性的特性,确定了Alpha-stable分布及与之对应的自相似随机过程作为本文研究的主要理论基础,并分析了当前Alpha-stable分布用于网络流量建模,网络资源调度等领域的研究现状。
为了更好的刻画描述网络业务流量的特性,本文对Alpha-stable分布的定义,概率密度函数PDF、Alpha-stable分布基本性质等进行了详细的研究,其中着重解决了无闭形式的Alpha-stable分布PDF的表示,服从Alpha-stable分布的随机数模拟等问题。Alpha-stable分布基础理论的研究为解决网络流量的模拟,研究网络流量高突发特性等问题奠定了基础。
对Alpha-stable分布理论和性质的研究解决了理论分析的需要,但是对实际网络业务流量的分析需要对其分布进行深入研究。对网络业务流量分布的研究,必须借助参数估计理论工具。因此,本文特别对Alpha-stable分布参数估计进行了着重研究。通过研究分析,本文将Alpha-stable分布的参数估计方法归纳为五大类:特征函数估计方法、分位数法、极大似然估计法、极大值估计法和矩估计方法。紧接着,通过理论研究和实验比较分析,对各种方法的优、缺点进行了详细讨论,并得到了关于极大似然估计法、极大值估计法的两个新结论。
针对发现的Alpha-stable分布参数估计极大值估计方法的新结论,在充分分析了Alpha-stable分布拖尾的渐进Pareto特性和不对称性的基础之上,本文提出了一种新的Alpha-stable分布极大值估计方法。该方法通过极大值截尾和偏斜分类提高了的Alpha-stable分布极大值估计方法性能。仿真实验结果分析表明该方法提高了参数估计的准确性、一致性和稳定性。
在具备了相关理论研究方法的基础之上,本文分析比较了基于平稳增量的自相似网络业务流量模型,针对网络业务流量高突发性的特点,利用平稳增量服从Alpha-stable分布的线性分形稳定过程对高突发性网络业务流量进行了自相似模型建模分析。这种一般化自相似网络流量分析新方法无需对网络业务流量分布进行假设,从而更具普遍意义。实验结果分析验证了该方法的有效性。
最后一部分为对全文主要研究成果的总结和概括,并综合分析了网络业务流量建模研究领域中需要进一步研究的问题和最新的研究探索方向。
Recently, with kinds of mass multi-media applications being carried over the network, theories of network traffic model and researches on network traffic analysis have caught researchers’attentions in the fields of computer network and communication. With the support of project of National Nature Science Foundation of China(NSFC),“Based on Covariation-orthogonality and Combined Optimization multimedia network performance prediction model (60502023)”and project of“experiment research of access network topology of MAN data service”carried out by the department of electronics and information engineering of Huazhong University of Science and Technology(HUST) and Zhuhai mobile company, this dissertation is focused on modeling and analysising the network traffic. The key problems in this research field, such as the network traffic modeling, parameter estimation and model analysis are studied in this dissertation.
“Self-similarity”is a keyword in current network traffic model researches. Compared with telephone traffic, it is main characteristic of multi-media network traffic. Self-similar models are more suitable for current network traffic because the network traffic characteristics can be present better by these self-similar models. Thus network traffic research based on self-similarity theory can catch network traffic essences better.
First of all, kinds of researches on network traffic in real network environments are analysised and self-similarity theory is introduced generally. Then research on network traffic by self-similarity theory is decided, which is focused on self-similarity definitions and Hurst parameter estimation methods. At the end of introduction, based on theory research and network traffic characteristics, burstiness, long correlation and self-similarity, Alpha-stable distribution and corresponding self-similar stochastic processes is thought of as theory foundation. Subsequently, applications of Alpha-stable distribution in traffic model and network resource scheduling fields are studied.
To be more accurate on analyzing network traffic characteristics, definitions and PDF of Alpha-stable distribution and properties of Alpha-stable distribution are analysised and studied. Especially, problems, such as how to get expression for the PDF of Alpha-stable distribution and how to get random variables conforming to Alpha-stable distribution, are studied. These researches on Alpha-stable distribution are key for network traffic simulation and bursty network traffic research.
Researches on Alpha-stable distribution theory and properties in sections above are for theory analysis. On the other hand, researches on characteristic of network traffic need their distributions, which are gotten by parameter estimation methoeds.Thus Alpha-stable distribution parameter estimation is studied respectively. There are many methods for parameter estimation of Alpha-stable distribution, each of which comes from different theory. Here, primary parameter estimation methods of Alpha-stable distribution are studied and classified into five types: Characteristic Function Method, Quantile Method, Maximum Likelihood Method, Extreme Value Method, Moment Method. By analysis and comparison, each type method is studied particularly. Two problems neglected in the past researches, the impact of sample numbers to maximum likelihood method based on FFT and approximation accuracy of extreme value method, have been gotten.
With the conclusions above, based on analysis of Alpha-stable distribution and Alpha-stable distribution parameter estimation methods, a new Alpha-stable distribution parameter estimation method based on extreme value theory is advanced. The new method makes use of truncated extreme value and classifying skewness to improve performance. Simulation results show this new method is more precise and stable.
Futher more, analysis of Alpha-stable distribution is used for analyzing self-similar network traffic model based on stable increasement. Because of the burstiness of network traffic, network traffic is analyzed based on linear fractional stable process whose increasement is stable. This new method without any hypothesis is a general method. Simulations show this new method could work well.
In the last section, main research conclusions are summed up and listed. Also, The some new directions and further problems in network traffic research fields are given out and analyzed.
“自相似”是当前网络业务流量模型研究中不得不提的一个概念。自相似性是多媒体网络业务流量不同于电话网业务流量的本质特性。对于现代网络业务流量,采用具有自相似特性的模型比传统的泊松模型更接近实际网络业务流的特性。因而以自相似性为突破口深入研究,将有助于深刻理解互联网业务流的本质特性,同时从根本上保证研究的可行性和准确性。
本文首先深入研究了自相似的基本理论,并对比分析了当前各种真实网络环境的网络业务流量数据研究结论,从而确立了通过自相似理论研究网络业务流量的研究方向。本文亦将此作为全文的主线。紧接着,本文深入研究讨论了当前主要的自相似Hurst参数估计方法。在绪论最后一部分,结合网络业务流量模型和网络业务流量突发性、长相关、自相似性的特性,确定了Alpha-stable分布及与之对应的自相似随机过程作为本文研究的主要理论基础,并分析了当前Alpha-stable分布用于网络流量建模,网络资源调度等领域的研究现状。
为了更好的刻画描述网络业务流量的特性,本文对Alpha-stable分布的定义,概率密度函数PDF、Alpha-stable分布基本性质等进行了详细的研究,其中着重解决了无闭形式的Alpha-stable分布PDF的表示,服从Alpha-stable分布的随机数模拟等问题。Alpha-stable分布基础理论的研究为解决网络流量的模拟,研究网络流量高突发特性等问题奠定了基础。
对Alpha-stable分布理论和性质的研究解决了理论分析的需要,但是对实际网络业务流量的分析需要对其分布进行深入研究。对网络业务流量分布的研究,必须借助参数估计理论工具。因此,本文特别对Alpha-stable分布参数估计进行了着重研究。通过研究分析,本文将Alpha-stable分布的参数估计方法归纳为五大类:特征函数估计方法、分位数法、极大似然估计法、极大值估计法和矩估计方法。紧接着,通过理论研究和实验比较分析,对各种方法的优、缺点进行了详细讨论,并得到了关于极大似然估计法、极大值估计法的两个新结论。
针对发现的Alpha-stable分布参数估计极大值估计方法的新结论,在充分分析了Alpha-stable分布拖尾的渐进Pareto特性和不对称性的基础之上,本文提出了一种新的Alpha-stable分布极大值估计方法。该方法通过极大值截尾和偏斜分类提高了的Alpha-stable分布极大值估计方法性能。仿真实验结果分析表明该方法提高了参数估计的准确性、一致性和稳定性。
在具备了相关理论研究方法的基础之上,本文分析比较了基于平稳增量的自相似网络业务流量模型,针对网络业务流量高突发性的特点,利用平稳增量服从Alpha-stable分布的线性分形稳定过程对高突发性网络业务流量进行了自相似模型建模分析。这种一般化自相似网络流量分析新方法无需对网络业务流量分布进行假设,从而更具普遍意义。实验结果分析验证了该方法的有效性。
最后一部分为对全文主要研究成果的总结和概括,并综合分析了网络业务流量建模研究领域中需要进一步研究的问题和最新的研究探索方向。
Recently, with kinds of mass multi-media applications being carried over the network, theories of network traffic model and researches on network traffic analysis have caught researchers’attentions in the fields of computer network and communication. With the support of project of National Nature Science Foundation of China(NSFC),“Based on Covariation-orthogonality and Combined Optimization multimedia network performance prediction model (60502023)”and project of“experiment research of access network topology of MAN data service”carried out by the department of electronics and information engineering of Huazhong University of Science and Technology(HUST) and Zhuhai mobile company, this dissertation is focused on modeling and analysising the network traffic. The key problems in this research field, such as the network traffic modeling, parameter estimation and model analysis are studied in this dissertation.
“Self-similarity”is a keyword in current network traffic model researches. Compared with telephone traffic, it is main characteristic of multi-media network traffic. Self-similar models are more suitable for current network traffic because the network traffic characteristics can be present better by these self-similar models. Thus network traffic research based on self-similarity theory can catch network traffic essences better.
First of all, kinds of researches on network traffic in real network environments are analysised and self-similarity theory is introduced generally. Then research on network traffic by self-similarity theory is decided, which is focused on self-similarity definitions and Hurst parameter estimation methods. At the end of introduction, based on theory research and network traffic characteristics, burstiness, long correlation and self-similarity, Alpha-stable distribution and corresponding self-similar stochastic processes is thought of as theory foundation. Subsequently, applications of Alpha-stable distribution in traffic model and network resource scheduling fields are studied.
To be more accurate on analyzing network traffic characteristics, definitions and PDF of Alpha-stable distribution and properties of Alpha-stable distribution are analysised and studied. Especially, problems, such as how to get expression for the PDF of Alpha-stable distribution and how to get random variables conforming to Alpha-stable distribution, are studied. These researches on Alpha-stable distribution are key for network traffic simulation and bursty network traffic research.
Researches on Alpha-stable distribution theory and properties in sections above are for theory analysis. On the other hand, researches on characteristic of network traffic need their distributions, which are gotten by parameter estimation methoeds.Thus Alpha-stable distribution parameter estimation is studied respectively. There are many methods for parameter estimation of Alpha-stable distribution, each of which comes from different theory. Here, primary parameter estimation methods of Alpha-stable distribution are studied and classified into five types: Characteristic Function Method, Quantile Method, Maximum Likelihood Method, Extreme Value Method, Moment Method. By analysis and comparison, each type method is studied particularly. Two problems neglected in the past researches, the impact of sample numbers to maximum likelihood method based on FFT and approximation accuracy of extreme value method, have been gotten.
With the conclusions above, based on analysis of Alpha-stable distribution and Alpha-stable distribution parameter estimation methods, a new Alpha-stable distribution parameter estimation method based on extreme value theory is advanced. The new method makes use of truncated extreme value and classifying skewness to improve performance. Simulation results show this new method is more precise and stable.
Futher more, analysis of Alpha-stable distribution is used for analyzing self-similar network traffic model based on stable increasement. Because of the burstiness of network traffic, network traffic is analyzed based on linear fractional stable process whose increasement is stable. This new method without any hypothesis is a general method. Simulations show this new method could work well.
In the last section, main research conclusions are summed up and listed. Also, The some new directions and further problems in network traffic research fields are given out and analyzed.
引文
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