本文選題：鐵路數(shù)據(jù)網(wǎng) + NetFlow��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：隨著鐵路信息化的快速發(fā)展和業(yè)務(wù)多樣化的需要,鐵路數(shù)據(jù)網(wǎng)成為承載鐵路各MIS系統(tǒng)數(shù)據(jù)交互、視頻會議、視頻監(jiān)控等通信信息的主要平臺,構(gòu)建在鐵路數(shù)據(jù)網(wǎng)上的應(yīng)用越來越多,其復(fù)雜程度和對網(wǎng)絡(luò)的依賴程度日益增高。各業(yè)務(wù)對帶寬需求也越來越大,如何更好地了解網(wǎng)絡(luò)流量狀況對網(wǎng)絡(luò)進行合理的帶寬分配成為了鐵路數(shù)據(jù)網(wǎng)網(wǎng)絡(luò)資源管理研究中的關(guān)鍵問題�；诹鲗傩缘木W(wǎng)絡(luò)流量模型是網(wǎng)絡(luò)性能分析和網(wǎng)絡(luò)帶寬分配的基礎(chǔ),精準(zhǔn)的網(wǎng)絡(luò)流量模型對于業(yè)務(wù)流量預(yù)測、網(wǎng)絡(luò)拓?fù)湓O(shè)計、網(wǎng)絡(luò)性能都有重要的意義。因此,通過統(tǒng)計分析鐵路業(yè)務(wù)流量和各業(yè)務(wù)子網(wǎng)流量行為特性建立高效的網(wǎng)絡(luò)流量分析預(yù)測模型是實現(xiàn)網(wǎng)絡(luò)帶寬分配的前提,也是實現(xiàn)鐵路數(shù)據(jù)網(wǎng)智能化調(diào)配的首要研究課題。但是,目前鐵路數(shù)據(jù)網(wǎng)流量分析停留在簡單粗略的監(jiān)控,流量精確統(tǒng)計、流量預(yù)測技術(shù)等還有待研究。本文基于網(wǎng)絡(luò)流量采集以及流量建模的研究,針對鐵路數(shù)據(jù)網(wǎng)承載業(yè)務(wù)特性,設(shè)計基于業(yè)務(wù)的流量統(tǒng)計方案并且采用分?jǐn)?shù)差分自回歸和滑動平均模型對鐵路實際網(wǎng)絡(luò)流量數(shù)據(jù)進行建模分析和預(yù)測。主要研究內(nèi)容包括以下幾個方面:(1)分析歸納了鐵路數(shù)據(jù)網(wǎng)業(yè)務(wù)承載特性及IP地址分配規(guī)律,在此基礎(chǔ)上,研究分析相關(guān)流量統(tǒng)計技術(shù)方法的優(yōu)劣性,重點分析了 NetFlow技術(shù)的數(shù)據(jù)采集、緩存、老化機制以及聚合策略。設(shè)計基于源IP地址前綴匹配聚合策略方法,建立鐵路業(yè)務(wù)系統(tǒng)流量統(tǒng)計方案,為未來基于業(yè)務(wù)流量預(yù)測的帶寬分配提供數(shù)據(jù)基礎(chǔ)。(2)根據(jù)鐵路數(shù)據(jù)網(wǎng)流量的自相似性和復(fù)雜性特點,分析網(wǎng)絡(luò)流量模型的優(yōu)缺點,選擇分?jǐn)?shù)自回歸整合滑動平均(Fractal Autoregressive Integrated Moving Average,FARIMA)模型作為文章的建模分析技術(shù)。為了簡化FARIMA模型分析算法的復(fù)雜度,本文將FARIMA模型分解為差分過程和ARMA過程。通過仿真驗證了 FARIMA模型的長相關(guān)性,并且對鐵路數(shù)據(jù)網(wǎng)實際流量數(shù)據(jù)進行FARIMA建模預(yù)測。通過與ARMA模型預(yù)測擬合對比分析驗證FARIMA模型預(yù)測的精準(zhǔn)度,實驗結(jié)果證明FARIMA建模預(yù)測分析擬合度較高,能夠作為鐵路數(shù)據(jù)網(wǎng)網(wǎng)絡(luò)流量趨勢預(yù)測分析。本文得出的預(yù)測數(shù)據(jù)能夠?qū)W(wǎng)絡(luò)進行動態(tài)帶寬分配,對于鐵路數(shù)據(jù)網(wǎng)業(yè)務(wù)而言,預(yù)測出每一個業(yè)務(wù)流量趨勢能夠?qū)崿F(xiàn)各業(yè)務(wù)子系統(tǒng)VPN帶寬分配,在業(yè)務(wù)網(wǎng)絡(luò)繁忙時,能夠提前預(yù)知并且擴大帶寬,避免丟包、延時等網(wǎng)絡(luò)性能的劣化,保障鐵路數(shù)據(jù)網(wǎng)運行安全。在業(yè)務(wù)網(wǎng)絡(luò)空閑時,能夠合理規(guī)劃帶寬分配,節(jié)省帶寬資源。
[Abstract]:With the rapid development of railway information and the need of diversification of business, railway data network has become the main platform for carrying the communication information of MIS system, such as data exchange, video conference, video surveillance and so on. There are more and more applications in railway data network, and its complexity and dependence on the network are increasing day by day. The demand for bandwidth of various services is also increasing. How to better understand the network traffic status and how to allocate the bandwidth to the network has become a key problem in the research of network resource management in railway data network. Network traffic model based on flow attributes is the basis of network performance analysis and network bandwidth allocation. Accurate network traffic model is of great significance for traffic prediction, network topology design and network performance. Therefore, the establishment of efficient network traffic analysis and prediction model through statistical analysis of railway traffic and traffic behavior characteristics of each service subnet is the premise of realizing network bandwidth allocation, and is also the first research topic to realize the intelligent allocation of railway data network. However, at present, the traffic analysis of railway data network remains in the simple rough monitoring, accurate flow statistics, flow prediction technology and so on. Based on the research of network traffic collection and traffic modeling, this paper aims at the characteristics of railway data network carrying service. The traffic statistics scheme based on service is designed, and the fractional differential autoregressive model and moving average model are used to model and predict the actual traffic data of railway network. The main research contents include the following aspects: 1) analyzing and summarizing the bearing characteristics of railway data network service and the law of IP address allocation. On the basis of this, the advantages and disadvantages of the related flow statistics techniques are studied and analyzed. The data acquisition, cache, aging mechanism and aggregation strategy of NetFlow technology are analyzed in detail. Based on the source IP address prefix matching aggregation strategy, the traffic statistics scheme of railway service system is established. This paper provides a data basis for bandwidth allocation based on traffic prediction. (2) according to the characteristics of self-similarity and complexity of traffic in railway data network, the advantages and disadvantages of network traffic model are analyzed. The fractional autoregressive integrated moving average Autoregressive Integrated Moving (FRMA) model is selected as the modeling and analysis technique in this paper. In order to simplify the complexity of FARIMA model analysis algorithm, the FARIMA model is decomposed into differential process and ARMA process in this paper. The long correlation of FARIMA model is verified by simulation, and the actual flow data of railway data network is predicted by FARIMA modeling. The accuracy of FARIMA model prediction is verified by comparing with ARMA model prediction and fitting analysis. The experimental results show that FARIMA model prediction analysis has a high fitting degree and can be used as the network flow trend prediction analysis of railway data network. The predicted data in this paper can allocate the dynamic bandwidth of the network. For the railway data network service, it is predicted that each service flow trend can realize the VPN bandwidth allocation of each service subsystem, and when the service network is busy, It can predict and enlarge the bandwidth in advance, avoid the deterioration of network performance such as packet loss and delay, and ensure the safety of railway data network operation. When the service network is idle, the bandwidth allocation can be reasonably planned and the bandwidth resources can be saved.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U29-39;TP393.06

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