發(fā)布時(shí)間：2018-03-05 23:20

  本文選題：云數(shù)據(jù)中心　切入點(diǎn)：分布式　出處：《大連海事大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：自軟件定義網(wǎng)絡(luò)(Software Defined Networking)技術(shù)出現(xiàn)以來,經(jīng)過學(xué)術(shù)界和產(chǎn)業(yè)界的不斷推動(dòng),軟件定義網(wǎng)絡(luò)的應(yīng)用不斷發(fā)展和深入,數(shù)據(jù)中心的軟件定義網(wǎng)絡(luò)化也正在從理論走向?qū)嵺`。本文旨在數(shù)據(jù)中心環(huán)境下研究軟件定義網(wǎng)絡(luò)部署及應(yīng)用的相關(guān)關(guān)健問題。隨著云數(shù)據(jù)中心的不斷發(fā)展,尤其是分布式云數(shù)據(jù)中心的出現(xiàn),今天的數(shù)據(jù)中心已經(jīng)演變成涉及十萬甚至百萬規(guī)模的服務(wù)器,跨越廣域網(wǎng)地理位置分散的,集大數(shù)據(jù)運(yùn)算和存儲(chǔ)為一體的高性能計(jì)算場(chǎng)所。分布式云數(shù)據(jù)中心的發(fā)展帶來了一系列新的問題：首先,對(duì)大規(guī)模且分布式的網(wǎng)絡(luò)需要集中統(tǒng)一管理,以提高數(shù)據(jù)中心的維護(hù)效率和可用性。其次,以承載云計(jì)算業(yè)務(wù)為主要目的的云數(shù)據(jù)中心,其流量工程也面臨嚴(yán)峻的考驗(yàn)。對(duì)于云計(jì)算,數(shù)據(jù)備份等大流量應(yīng)用的分類,直接影響了數(shù)據(jù)中心對(duì)外提供服務(wù)的質(zhì)量。再次,跨越互聯(lián)網(wǎng)分布式的特點(diǎn)以及虛擬化等技術(shù)的發(fā)展,使得云數(shù)據(jù)中心網(wǎng)絡(luò)安全面臨新形勢(shì)的挑戰(zhàn)。本文深入分析和總結(jié)了分布式云數(shù)據(jù)中心的新特征,從分布式云數(shù)據(jù)中心網(wǎng)絡(luò)中的軟件定義網(wǎng)絡(luò)部署問題、大小流分類問題,以及DDoS檢測(cè)問題出發(fā),緊緊圍繞上述三個(gè)問題展開研究。本文的主要研究工作如下：首先,針對(duì)分布式云數(shù)據(jù)中心拓?fù)浣Y(jié)構(gòu)復(fù)雜、SDN域劃分和控制器部署等問題,本文提出了基于譜的軟件定義網(wǎng)絡(luò)部署算法,即利用譜聚類思想對(duì)拓?fù)浣Y(jié)構(gòu)進(jìn)行分析,并利用矩陣擾動(dòng)和本征間隙理論對(duì)拓?fù)溥M(jìn)行挖掘以找出其內(nèi)在特征實(shí)現(xiàn)SDN域的自動(dòng)劃分,最后得出其控制器部署位置。為了驗(yàn)證我們所提出的算法,我們還設(shè)計(jì)了一個(gè)基于Cbench的測(cè)試框架。實(shí)驗(yàn)表明基于譜的軟件定義網(wǎng)絡(luò)部署算法具有一定的優(yōu)勢(shì),其部署為后續(xù)的SDN應(yīng)用研究奠定了基礎(chǔ)。其次,針對(duì)分布式數(shù)據(jù)中心下大小流的特點(diǎn)和流量工程流量分類的需求,本文提出了一個(gè)基于軟件定義網(wǎng)絡(luò)的大小流分類算法。為了提高算法的實(shí)時(shí)性,本文采用兩層分類策略來進(jìn)行快速分類。即先基于首包檢測(cè)快速排除大量的小流,減少了后續(xù)計(jì)算,再對(duì)可能的大流基于流特征進(jìn)行C4.5決策樹分類,以提高算法的精確度。為了進(jìn)一步提高算法的分類精確度,本文除了對(duì)訓(xùn)練集合進(jìn)行相容性分析之外,還基于數(shù)據(jù)中心自身流量特點(diǎn)和大流分類更重要的特點(diǎn),在算法中引入了Cost-Sensitive思想,有效提高了算法的分類精確度和彌補(bǔ)了現(xiàn)有算法的不足。最后基于SDN技術(shù)進(jìn)行實(shí)驗(yàn),驗(yàn)證了算法的有效性。最后,針對(duì)分布式云數(shù)據(jù)中心下新型的DDoS攻擊鏈路洪泛攻擊檢測(cè)存在的問題,本文提出了一種基于軟件定義網(wǎng)絡(luò)的DDoS檢測(cè)算法。其核心思想是從提高實(shí)時(shí)性和數(shù)據(jù)存儲(chǔ)效率出發(fā),將SDN流表技術(shù)應(yīng)用到鏈路洪泛攻擊檢測(cè)當(dāng)中,節(jié)約了流統(tǒng)計(jì)的時(shí)間并提高了實(shí)時(shí)性；將Bloom Filter應(yīng)用于軟件定義網(wǎng)絡(luò)的DDoS檢測(cè)中,解決了鏈路洪泛攻擊存儲(chǔ)空間和時(shí)間效率要求高等問題。與傳統(tǒng)的檢測(cè)算法相比,該算法不但克服了傳統(tǒng)算法收集信息實(shí)時(shí)性差問題,而且解決了海量數(shù)據(jù)存儲(chǔ)效率問題。最后利用SDN技術(shù)實(shí)現(xiàn)了該算法,實(shí)驗(yàn)結(jié)果表明該算法具有一定的優(yōu)勢(shì)。
[Abstract]:Since the software defined network (Software Defined Networking) technology emerged, after constantly promote academia and industry, the application of software defined network development and deepening, software defined network data center is from theory to practice. This paper aims to Guan Jian data center environment of software defined network deployment and application with the continuous development of the cloud data center, especially the emergence of distributed cloud data center, the data center has evolved into today's scale involving one hundred thousand or even millions of servers across wide area network geographically dispersed, high performance computing places large data storage and computing technology. The development of distributed cloud data center brings a series of new problems: first, the large-scale and distributed network requires a centralized and unified management, to improve data center efficiency and maintenance Availability. Secondly, cloud data center as the main purpose to support cloud computing business, the traffic engineering is also facing serious challenges. For cloud computing, classification of data backup and other large flow applications, directly affects the quality of the data center to provide services. Thirdly, development across the Internet and distributed features of virtualization the cloud data center technology, network security is facing new challenges. This paper analyses and summarizes the new features of distributed cloud data center, from the software defined network deployment of distributed cloud data center network, classification of the size of the stream, starting and DDoS detection problems, focusing on these three issues. The research work of this paper is as follows: firstly, according to the distributed cloud data center complex topology, SDN domain and controller deployment problem is proposed in this paper based on spectrum Software defined network deployment algorithm, namely the use of spectral clustering for topology analysis, and using the matrix perturbation and automatic classification eigengap theory of topological mining in order to find the inherent characteristics of SDN domain, and finally got the controller located. To verify our proposed algorithm, we also design a based on Cbench test framework. The experimental results show that the spectrum of software defined network deployment algorithm has certain advantages based on the deployment of laid the foundation for the research on the application of SDN in the future. Secondly, according to the characteristics of distributed data center under the size of the stream and traffic engineering classification requirements, this paper proposes a classification algorithm software defined network size based on. In order to improve the real-time performance, this paper adopts two layer classification strategy for rapid classification. Firstly, based on the first packet fast detection to exclude small flow a lot, To reduce the subsequent calculation, and then on the flow characteristics of the flow may be C4.5 based on decision tree classification, in order to improve the algorithm accuracy. In order to further improve the classification accuracy of the algorithm in this paper, in addition to the training set for compatibility analysis, has its own data center flow characteristics and flow characteristics of the more important classification based on Cost-Sensitive introduced in the algorithm, effectively improve the classification accuracy of the algorithm and make up for the shortcomings of existing algorithms. Finally, based on the SDN technology experiment, verify the effectiveness of the algorithm. Finally, according to the distributed cloud data center DDoS attack link flooding attack detection model of the existing problems, this paper presents a DDoS detection algorithm based on software defined networking. Its core idea is from the perspective of improving the real-time and efficiency of data storage, SDN flow table technology to link flooding attack detection, To save time and improve the flow statistics in real-time; DDoS detection of Bloom Filter is applied to the software defined network, solve the link flooding attack storage space and time efficiency requirements higher. Compared with the traditional detection algorithm, this algorithm not only overcomes the traditional algorithm of collecting information in real-time, but also solves the problem the massive data storage efficiency. Finally, the algorithm is realized by using SDN technology, the experimental results show that this algorithm has certain advantages.

【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.0;TP308

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 汪正康;周鵬;肖俊超;武延軍;;基于SDN的數(shù)據(jù)中心網(wǎng)絡(luò)資源調(diào)度機(jī)制[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2015年08期

2 彭宏玉;陳剛;張英海;王朝煒;;SDN架構(gòu)下數(shù)據(jù)中心內(nèi)存能耗優(yōu)化策略[J];北京郵電大學(xué)學(xué)報(bào);2015年02期

3 饒少陽;陳運(yùn)清;馮明;;基于SDN的云數(shù)據(jù)中心[J];電信科學(xué);2014年08期

4 李丹;劉方明;郭得科;何源;黃小猛;;軟件定義的云數(shù)據(jù)中心網(wǎng)絡(luò)基礎(chǔ)理論與關(guān)鍵技術(shù)[J];電信科學(xué);2014年06期

5 劉娜;路瑩;唐曉君;王海文;李明霞;;自動(dòng)確定單詞-文檔譜聚類最佳聚類數(shù)目的研究[J];小型微型計(jì)算機(jī)系統(tǒng);2014年03期

6 左青云;陳鳴;蔣培成;;基于排隊(duì)模型的OpenFlow控制平面時(shí)延評(píng)估[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年08期

7 魏祥麟;陳鳴;范建華;張國敏;盧紫毅;;數(shù)據(jù)中心網(wǎng)絡(luò)的體系結(jié)構(gòu)[J];軟件學(xué)報(bào);2013年02期

8 趙慧玲;馮明;史凡;;SDN——未來網(wǎng)絡(luò)演進(jìn)的重要趨勢(shì)[J];電信科學(xué);2012年11期

9 ;Spectral clustering based on matrix perturbation theory[J];Science in China(Series F:Information Sciences);2007年01期

相關(guān)博士學(xué)位論文 前1條

1 王健;基于軟件定義網(wǎng)絡(luò)架構(gòu)的數(shù)據(jù)中心網(wǎng)絡(luò)若干關(guān)鍵問題研究[D];北京郵電大學(xué);2015年

相關(guān)碩士學(xué)位論文 前1條

1 王s，

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