基于RMON2协议的网络流量监测与预测研究

发布时间：2018-03-19 23:32

本文选题：流量监测　切入点：流量预测　出处：《西安电子科技大学》2014年硕士论文　论文类型：学位论文

【摘要】：随着互联网的快速发展,网络新型应用逐渐丰富,网络规模不断增大。网络流量监测与预测技术作为增强网络控制性的有效技术,不仅能够获取网络流量数据,而且对网络的监督管理、服务质量、安全管理、故障检测、容量规划等都有很重要的影响。SNMP作为一种简单网络管理协议,已经广泛应用于各种网络管理系统。但其轮询MIB节点会产生大量的管理报文,这对网络带宽和处理能力提出更高的要求,且只支持集中式管理。而RMON协议能很容易地解决这些问题,RMON协议体系包括RMON1和RMON2标准,与RMON1相比,RMON2能够周期性监控更丰富的流量信息。本文实现了基于RMON2协议的网络流量监测系统,并在此基础上对两种神经网络的流量预测算法进行性能仿真。主要工作如下：1.对于流量监测,介绍了RMON协议的标准和工作方式,重点论述了RMON1和RMON2之间的区别。对于流量预测,基于BP神经网络模型和小波神经网络模型,推导了两种网络算法的步骤,经理论分析可得出小波神经网络具有更好的流量预测性能。2.分析了RMON2系统的需求并提出了该系统的总体设计。首先将系统总体划分成子模块,主要包括系统的零层、一层和二层分解模块。然后,由一层模块和二层模块的运行设计完成整个系统方案的设计。最后,使用C语言实现了RMON2系统。3.构建RMON2系统后,分别从功能测试、性能测试、规格测试、组合压力测试和兼容性测试等方面对RMON2系统进行测试,测试结果表明该系统功能稳定。4.利用一种TCL脚本语言完成了RMON2自动化工具,给出了RMON2自动化工具监测网络流量的方法,并分析了自动化测试的优缺点。5.使用RMON2自动化工具对网络运营商的设备进行流量监测,周期性采样接口上行和下行流量值,并以此数据作为后续流量的预测样本,分别对BP神经网络和小波神经网络的流量预测模型进行性能仿真实验。仿真实验表明,两种预测模型都能很好地对网络流量进行预测,且在同一仿真条件下,小波神经网络的流量预测算法可获得更小的预测误差。
[Abstract]:With the rapid development of the Internet, the new network applications are becoming more and more abundant and the network scale is increasing. As an effective technology to enhance the network control, the network traffic monitoring and forecasting technology can not only obtain the network traffic data. Moreover, SNMP has a very important influence on network supervision and management, quality of service, security management, fault detection, capacity planning and so on. SNMP is a simple network management protocol. It has been widely used in various network management systems, but its polling of MIB nodes will produce a large number of management packets, which puts forward higher requirements for network bandwidth and processing capability. The RMON protocol can easily solve these problems, including RMON1 and RMON2 standards. Compared with RMON1, RMON2 can monitor more abundant traffic information periodically. A network traffic monitoring system based on RMON2 protocol is implemented in this paper. The main work is as follows: 1. For traffic monitoring, the standard and working mode of RMON protocol are introduced, and the difference between RMON1 and RMON2 is emphasized. Based on BP neural network model and wavelet neural network model, the steps of two network algorithms are deduced. Through theoretical analysis, it can be concluded that wavelet neural network has better flow prediction performance. 2. The demand of RMON2 system is analyzed and the overall design of the system is put forward. Firstly, the whole system is divided into sub-modules, including the zero layer of the system. The first layer and the second layer decompose the module. Then, the operation design of the first layer module and the second layer module completes the design of the whole system scheme. Finally, the RMON2 system. 3. After constructing the RMON2 system, the function test and the performance test are carried out, respectively. The RMON2 system is tested in the aspects of specification test, combined stress test and compatibility test. The test results show that the system functions stably .4.Using a TCL script language to complete the RMON2 automation tool, This paper presents a method of monitoring network traffic with RMON2 automation tools, and analyzes the advantages and disadvantages of automatic testing. Using RMON2 automation tools to monitor the traffic of network operators' equipment, periodically sampling the upstream and downlink traffic value of the interface. The simulation experiments on the traffic prediction models of BP neural network and wavelet neural network show that the two models can predict the network traffic very well. Under the same simulation condition, the traffic prediction algorithm of wavelet neural network can obtain smaller prediction error.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.06

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