时间序列分析技术在网络流量监控中的应用研究

发布时间：2018-06-15 09:56

本文选题：网络流量监控 + 时间序列　；参考：《天津理工大学》2014年硕士论文

【摘要】：计算机网络的发展，使得信息的交流和资源的共享更加便捷。为了教师教学和学生学习的方便，校园网带宽逐年扩大，访问的速度也得到了很大的提高。但是，目前校园网带宽的有效利用率并不高，大部分带宽用于游戏、视频、即时通信以及P2P应用。由于这些与工作关系不大的应用，抢占、消耗大量的网络带宽资源，降低了正常通信的质量，影响了正常的工作和学习。因此，如何准确地掌控网络运行状况，分析网络应用服务，进行有效地网络监控，提高网络带宽有效利用率，成为亟需解决的问题，具有重要的研究意义。本文旨在通过研究校园网流量基本特征，构建流量预测模型，并利用流量的历史数据预测未来流量变化趋势。运用应用层协议分析技术对不同种类的网络应用服务进行统计分析，管理员根据预测和分析结果进行网络运行控制，对P2P应用、游戏以及视频等应用服务进行带宽限制。本文的主要研究内容是在认真分析了校园网络流量自相似性以及时间序列分析技术的基本概念、理论方法和建模步骤的基础上，进一步深入研究了自回归移动平均模型(ARMA)、季节性自回归移动平均模型(SARMA)和广义自回归条件异方差模型(GARCH)的建模过程；其次，对网络流量监控与分析技术进行了研究，包括应用层协议分析技术、多模式状态机匹配技术和简单网络管理协议(SNMP)。通过实验效果对比，最终提出了一种运用SARMA模型对网络流量历史数据进行建模，并利用GARCH模型对其残差进行修正的网络流量预测模型建模方法。论文运用时间序列分析技术建立网络流量预测模型，预测网络流量下一个时间段内的变化趋势。本文还采用端口技术、深度包检测技术(DPI)、深度流检测技术(DFI)进行应用层协议分析，在协议分析过程中采用多模式状态机匹配算法，进行特征匹配，，提高了匹配的速度和准确度，最后通过SNMP对网络设备进行设置，从而达到对某一IP或应用协议进行控制的目的。结合使用以上各项技术，设计并实现了网络流量预测与监控系统。系统基于B/S架构，前台页面使用ExtJS4.0进行页面渲染，后台在SSH(Struts2+Spring+Hibernate)框架下进行MVC模式开发，前后台数据交互过程使用JSON传输，整个管理系统在Web方式下以Desktop样式呈现。论文介绍了网络流量预测与监控系统的设计方法，并对各个功能模块的实现方法进行了详尽地阐述，最后对系统部署以及性能分析进行了描述。
[Abstract]:With the development of computer network, the exchange of information and the sharing of resources are more convenient. For the convenience of teachers' teaching and students' study, the bandwidth of campus network has been enlarged year by year, and the speed of access has been greatly improved. However, the current campus network bandwidth utilization is not high, most of the bandwidth for games, video, instant messaging and P2P applications. Because of these little work related applications, preemption, consumption of a large number of network bandwidth resources, reduce the quality of normal communications, affect the normal work and learning. Therefore, how to accurately control the operation of the network, analyze the network application services, effectively monitor the network, improve the effective utilization of network bandwidth, become an urgent problem, which has an important research significance. The purpose of this paper is to build a traffic forecasting model by studying the basic characteristics of campus network traffic, and to predict the trend of traffic change in the future by using the historical data of traffic. The application layer protocol analysis technology is used to analyze the different kinds of network application services. The administrator controls the network operation according to the prediction and analysis results, and restricts the bandwidth of P2P applications, games, video and other application services. The main research content of this paper is based on the analysis of the basic concepts, theoretical methods and modeling steps of the campus network traffic self-similarity and time series analysis technology. The modeling process of autoregressive moving average model, seasonal autoregressive moving average model and generalized autoregressive conditional heteroscedasticity model (GARCH) are further studied. Secondly, the network traffic monitoring and analysis techniques are studied. It includes application layer protocol analysis technology, multi-mode state machine matching technology and simple network management protocol (SNMP). Through the comparison of experimental results, a modeling method of network traffic prediction model using SARMA model is proposed, and the residual error is modified by GARCH model. In this paper, the time series analysis technique is used to establish a network traffic prediction model to predict the trend of network traffic in the next time period. This paper also uses port technology, depth packet detection technology and depth flow detection technology to analyze the protocol in application layer. In the process of protocol analysis, multi-mode state machine matching algorithm is used to match features, and the speed and accuracy of matching are improved. Finally, the network device is set up by SNMP to control a certain IP or application protocol. A network traffic forecasting and monitoring system is designed and implemented by using the above technologies. The system is based on the structure of B / S, the front page is rendered by Ext JS4.0, the background is developed by MVC pattern under the framework of SSH / Struts2 Spring hibernate, the process of front and back data exchange is transmitted by JSON, and the whole management system is presented as Desktop style in Web mode. This paper introduces the design method of network traffic prediction and monitoring system, and describes the implementation of each functional module in detail. Finally, the system deployment and performance analysis are described.
【学位授予单位】：天津理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.06;O211.61

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