面向城域网的网络流量建模研究
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摘要
网络流量模型是网络性能分析与网络规划设计的基础，精确的网络流量模型对设计高性能网络协议、高性能的网络设备、高效网络拓扑结构、流量预测与网络规划、拥塞控制与负载均衡等都具有重要意义。随着近年来互联网的日益普及和应用的急剧增加，骨干网流量的特征也发生了很大改变，传统的网络流量模型已经不能适应当前的骨干网流量。因而，研究当前骨干网流量的规律，设计能反映其特征的模型，是今后一段时间网络流量领域研究的一个重要方向。
论文以陕西省自然科学基金项目(基金编号：2005F43)及2003年、2004年西安宽带多媒体城域网优化项目为背景，对城域网网络流量进行了深入研究，取得了预期的效果，在自相似网络流量理论和实际应用之间架设了一个桥梁。论文的主要内容和创新点包括：
1、通过对城域网大量真实网络流量数据的分析和严格检验，发现城域网网络流量的概率密度分布服从gamma分布，探索出一条从概率密度分布特征出发来研究网络流量的新途径。基于上述结论，对美国Rice大学RudolgH Riedi所提出的、在网络流量研究领域具有重要影响的MWM模型进行改造，建立了一个基于gamma分布和小波的网络流量仿真模型(GWM)。仿真实验显示，该模型在仿真流量的特征、算法的合理性等方面，均较MWM模型有了显著的提高。
2、发现了城域网流量聚合过程的规律，揭示了城域网流量之所以服从gamma分布的成因，把对网络流量特征的认识提升到一个新的高度。利用上述规律，预测了城域网网络流量的演化趋势，为提高城域网网络规划的前瞻性奠定了基础。
3、发现了网络流量的概率密度分布与观测尺度之间的量化关系，刻画了网络流量复杂而细致的自相似结构，从而打破了10多年来仅能依靠方差和数学期望等这样简单的统计特征来定义网络流量自相似性的现状，深化了对自相似现象的理解。作为应用实例，这些研究成果在网络设备的缓存设计工作中，对于网络流量排队分析起了重要作用。
4、建立了一个基于gamma分布和多重随机二分法的网络流量仿真模型(GIRDM)。该模型的仿真实验显示，其仿真流量能够精确地反映实际网络流量的特征，并可以通过调整输入参数的值，方便地改变仿真流量自相似的程度，从而性能较GWM模型又有了进一步提高。该模型发表在2005年8月的《中国通信学报(英)》上，详细的算法设计将发表在2007年的《系统仿真学报》。华为公司在2006年基金项目中，也提出了基于层叠模型的网络流量异常检测等课题，旁证了该模型的实际价值。
5、设计的基于SNMP的网络流量采样工具，最小尺度达到10毫秒，远高于目前同样采用SNMP方法的流量采样所能达到的精度，有能力捕捉到流量在微小尺度上的精细特征。流量样本直接采自一个实际城域网，样本代表性强。
Network traffic model acts as the fundamental issue of network performance analysis and network planning. Especially, precise network traffic model is essential to high performance network protocol constructing, network device designing, effective network topology deployment, traffic forecasting, congestion control, load balance, and so on. Due to the surging popularity and increasing utility of the Internet, backbone network is rapidly growing in size, speed and scope, accompanied with the evolving of their traffic properties. As a result, the traditional network traffic model does no longer accordant with traffic of current backbone network. Consequentially, it is a matter of great urgency to research and model the characteristics of backbone network traffic in the near future.
In this dissertation, the network traffic of metropolitan area network (MAN) are researched thoroughly on a background of the natural science fund of shannxi province (2005F43) and the project of Xi'An broadband multimedia MAN optimization in 2003 and 2004. The result achieves the expectation and sets up a bridge between the self-similar theory and the practice of network traffic. Main research works and creative contributions in the dissertation include:
1. The network traffic is discovered and verified to be accordant with gamma distribution based on analysis of a great deal of traces collected from a real MAN. This result provides a new approach using probability density distribution property for the study of network traffic. Based on it, the influential MWM set up by Rudolf.H Riedi of Rice University (US) is rebuilded to be a gamma distribution and wavelet based model (GWM). Performance of the improved model is showed to be more effective than Riedi's MWM by the experiment, espeically in aspects of statistcal properties of the traffic generated by the model and rationality of the algorithm used in the model.
2. Laws appeared in the process of traffic aggregating are exposed and the cause why the MAN network traffic appears gamma distribution is also discovered. These achievements enriche the knowledge of network traffic properties greatly and provide a necessary basis for network deployment with foresight.
3. Quantificational relationship between the probability density distribution of the network traffic and the related time scale is discovered, thus, the complex but delicate structure of the MAN traffic is well illustrated. The improvement greatly deepens the understanding on self-similar phenomena and notablely ends the status lasted for more then ten years that the self-similarity of the network traffic can only be described with the simple statistical variables such as the variance and the expectation. As an application instance, it plays important role in the queue anaysis, in the buffer design of network device.
4. A gamma distribution and iterative random dichotomy based model (GIRDM) is set up. The experiment shows that the model not only accurately maps the probability density distribution property of the real network traffic accurately, but also has the ability to alter the self-similar degree of the generated network traffic through adjusting the values of the input parameters. Consequentially, performance of the GIRDM is improved greatly than GWM. The model is published at china communication in Aug 2005 and its detailed algorithm will appeared in Journal of System Simulation. Furthermore, its worthiness is emphasized by the fund of Huawei Corp. in 2006, in which, practical projects such as abnormal network traffic detection based on cascade are supported.
5. A network traffic sampling tool based on SNMP is elaborately developed to ensure the time scale as precise as ten milliseconds, which is more accurate than before. This advantage makes it possible to catch the details of the traffic porperty in small scale. Traces in the study are collected directly from a real MAN network.
论文以陕西省自然科学基金项目(基金编号：2005F43)及2003年、2004年西安宽带多媒体城域网优化项目为背景，对城域网网络流量进行了深入研究，取得了预期的效果，在自相似网络流量理论和实际应用之间架设了一个桥梁。论文的主要内容和创新点包括：
1、通过对城域网大量真实网络流量数据的分析和严格检验，发现城域网网络流量的概率密度分布服从gamma分布，探索出一条从概率密度分布特征出发来研究网络流量的新途径。基于上述结论，对美国Rice大学RudolgH Riedi所提出的、在网络流量研究领域具有重要影响的MWM模型进行改造，建立了一个基于gamma分布和小波的网络流量仿真模型(GWM)。仿真实验显示，该模型在仿真流量的特征、算法的合理性等方面，均较MWM模型有了显著的提高。
2、发现了城域网流量聚合过程的规律，揭示了城域网流量之所以服从gamma分布的成因，把对网络流量特征的认识提升到一个新的高度。利用上述规律，预测了城域网网络流量的演化趋势，为提高城域网网络规划的前瞻性奠定了基础。
3、发现了网络流量的概率密度分布与观测尺度之间的量化关系，刻画了网络流量复杂而细致的自相似结构，从而打破了10多年来仅能依靠方差和数学期望等这样简单的统计特征来定义网络流量自相似性的现状，深化了对自相似现象的理解。作为应用实例，这些研究成果在网络设备的缓存设计工作中，对于网络流量排队分析起了重要作用。
4、建立了一个基于gamma分布和多重随机二分法的网络流量仿真模型(GIRDM)。该模型的仿真实验显示，其仿真流量能够精确地反映实际网络流量的特征，并可以通过调整输入参数的值，方便地改变仿真流量自相似的程度，从而性能较GWM模型又有了进一步提高。该模型发表在2005年8月的《中国通信学报(英)》上，详细的算法设计将发表在2007年的《系统仿真学报》。华为公司在2006年基金项目中，也提出了基于层叠模型的网络流量异常检测等课题，旁证了该模型的实际价值。
5、设计的基于SNMP的网络流量采样工具，最小尺度达到10毫秒，远高于目前同样采用SNMP方法的流量采样所能达到的精度，有能力捕捉到流量在微小尺度上的精细特征。流量样本直接采自一个实际城域网，样本代表性强。
Network traffic model acts as the fundamental issue of network performance analysis and network planning. Especially, precise network traffic model is essential to high performance network protocol constructing, network device designing, effective network topology deployment, traffic forecasting, congestion control, load balance, and so on. Due to the surging popularity and increasing utility of the Internet, backbone network is rapidly growing in size, speed and scope, accompanied with the evolving of their traffic properties. As a result, the traditional network traffic model does no longer accordant with traffic of current backbone network. Consequentially, it is a matter of great urgency to research and model the characteristics of backbone network traffic in the near future.
In this dissertation, the network traffic of metropolitan area network (MAN) are researched thoroughly on a background of the natural science fund of shannxi province (2005F43) and the project of Xi'An broadband multimedia MAN optimization in 2003 and 2004. The result achieves the expectation and sets up a bridge between the self-similar theory and the practice of network traffic. Main research works and creative contributions in the dissertation include:
1. The network traffic is discovered and verified to be accordant with gamma distribution based on analysis of a great deal of traces collected from a real MAN. This result provides a new approach using probability density distribution property for the study of network traffic. Based on it, the influential MWM set up by Rudolf.H Riedi of Rice University (US) is rebuilded to be a gamma distribution and wavelet based model (GWM). Performance of the improved model is showed to be more effective than Riedi's MWM by the experiment, espeically in aspects of statistcal properties of the traffic generated by the model and rationality of the algorithm used in the model.
2. Laws appeared in the process of traffic aggregating are exposed and the cause why the MAN network traffic appears gamma distribution is also discovered. These achievements enriche the knowledge of network traffic properties greatly and provide a necessary basis for network deployment with foresight.
3. Quantificational relationship between the probability density distribution of the network traffic and the related time scale is discovered, thus, the complex but delicate structure of the MAN traffic is well illustrated. The improvement greatly deepens the understanding on self-similar phenomena and notablely ends the status lasted for more then ten years that the self-similarity of the network traffic can only be described with the simple statistical variables such as the variance and the expectation. As an application instance, it plays important role in the queue anaysis, in the buffer design of network device.
4. A gamma distribution and iterative random dichotomy based model (GIRDM) is set up. The experiment shows that the model not only accurately maps the probability density distribution property of the real network traffic accurately, but also has the ability to alter the self-similar degree of the generated network traffic through adjusting the values of the input parameters. Consequentially, performance of the GIRDM is improved greatly than GWM. The model is published at china communication in Aug 2005 and its detailed algorithm will appeared in Journal of System Simulation. Furthermore, its worthiness is emphasized by the fund of Huawei Corp. in 2006, in which, practical projects such as abnormal network traffic detection based on cascade are supported.
5. A network traffic sampling tool based on SNMP is elaborately developed to ensure the time scale as precise as ten milliseconds, which is more accurate than before. This advantage makes it possible to catch the details of the traffic porperty in small scale. Traces in the study are collected directly from a real MAN network.
引文
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