本文選題：流量測(cè)量　切入點(diǎn)：抽樣　出處：《曲阜師范大學(xué)》2014年碩士論文

【摘要】：隨著新一代互聯(lián)網(wǎng)的建設(shè)和發(fā)展，網(wǎng)絡(luò)行為變得十分復(fù)雜，針對(duì)網(wǎng)絡(luò)的異常攻擊也變得更加嚴(yán)重，這些現(xiàn)狀在很大程度上威脅著網(wǎng)絡(luò)的管理和安全。網(wǎng)絡(luò)測(cè)量是對(duì)網(wǎng)絡(luò)性能進(jìn)行分析和建模的基礎(chǔ)，在網(wǎng)絡(luò)管理中扮演著越來越重要的角色。然而，由于高速網(wǎng)絡(luò)中數(shù)據(jù)量較大，獲取每個(gè)報(bào)文信息或者流信息進(jìn)行存儲(chǔ)和測(cè)量已變的不可能，且流量存在很大的突變性，給系統(tǒng)資源帶來過多的消耗，抽樣技術(shù)的引入成功的解決了該性能瓶頸問題，成為網(wǎng)絡(luò)流量工程研究的重點(diǎn)之一。 本文首先介紹了網(wǎng)絡(luò)測(cè)量與流量分析技術(shù)，闡述高速網(wǎng)絡(luò)測(cè)量中遇到的困難，指出抽樣技術(shù)在網(wǎng)絡(luò)測(cè)量中的重要作用。接著對(duì)抽樣技術(shù)的詳細(xì)內(nèi)容進(jìn)行概述，討論了幾種常用的抽樣方法，系統(tǒng)全面地分析了與抽樣測(cè)量相關(guān)的關(guān)鍵技術(shù)和重要算法，如Bloom filter算法和超時(shí)策略等。最后，通過研究目前網(wǎng)絡(luò)特性，本文將抽樣技術(shù)與Bloom filter算法和動(dòng)態(tài)的超時(shí)策略相結(jié)合，提出了新的抽樣測(cè)量算法應(yīng)用于流量測(cè)量中，其中，Bloom filter實(shí)現(xiàn)簡(jiǎn)單，能快速進(jìn)行資源查找和匹配；超時(shí)策略作為判斷流輸出的標(biāo)志之一，對(duì)流特性的測(cè)量精度和流cache的利用率有很大的影響。經(jīng)性能分析和實(shí)驗(yàn)仿真證明，本論文提出的算法能夠在提高測(cè)量準(zhǔn)確性的同時(shí)，，提高系統(tǒng)的資源利用率。具體研究?jī)?nèi)容如下： （1）本文對(duì)Bloom filter算法和改進(jìn)的CBF算法進(jìn)行了深入研究，針對(duì)目前CBF算法在流量過大時(shí)會(huì)造成計(jì)數(shù)器溢出的缺陷，設(shè)計(jì)了一種動(dòng)態(tài)計(jì)數(shù)型布魯姆過濾器（DCBF）算法。該算法使用了多層CBF，可在流量較大時(shí)自適應(yīng)增加新的CBF，防止CBF溢出造成測(cè)量誤差。將DCBF與基于報(bào)文的流抽樣算法相結(jié)合，可以在減少測(cè)量個(gè)數(shù)的同時(shí)提高測(cè)量精度。通過實(shí)驗(yàn)仿真對(duì)該算法與基于CBF和FCBF的抽樣算法進(jìn)行了測(cè)量誤差方面的比較，分析可知，本文提出的算法提高了抽樣的準(zhǔn)確性，降低了空間利用率。 （2）隨著網(wǎng)絡(luò)規(guī)模的不斷擴(kuò)張，網(wǎng)絡(luò)流量的特征變得異常復(fù)雜且難以預(yù)測(cè)，靜態(tài)的抽樣方法已不能滿足高速網(wǎng)絡(luò)測(cè)量的要求。本文提出了一種自適應(yīng)流抽樣算法，該算法利用時(shí)間對(duì)報(bào)文進(jìn)行分層，在層內(nèi)使用固定的最大數(shù)量的抽樣，這樣可以在網(wǎng)絡(luò)流量較小時(shí)保持測(cè)量準(zhǔn)確性，而在流量劇增時(shí)保證資源的可控性。然后，針對(duì)固定超時(shí)策略在網(wǎng)絡(luò)測(cè)量應(yīng)用中存在的缺陷，采用了兩層自適應(yīng)超時(shí)（TSAT）策略來控制流的輸出。TSAT策略采用了兩層流空間，為系統(tǒng)中廣泛存在的單包流維護(hù)獨(dú)立的流空間，并對(duì)其使用較小超時(shí)。通過對(duì)該算法與基于NetFlow的抽樣算法進(jìn)行仿真比較，驗(yàn)證了算法具有自適應(yīng)性、較高的準(zhǔn)確性和資源可控性。
[Abstract]:With the construction and development of the new generation of Internet, the network behavior becomes very complex, and the abnormal attacks against the network become more serious. Network measurement is the basis of network performance analysis and modeling, and plays an increasingly important role in network management. However, because of the large amount of data in high-speed network, network measurement plays a more and more important role in network management. It is impossible to obtain every message information or stream information to store and measure, and there is a great mutation of traffic, which brings excessive consumption to system resources. The introduction of sampling technology successfully solves the performance bottleneck problem. It has become one of the emphases of network traffic engineering. This paper first introduces the network measurement and flow analysis technology, expounds the difficulties encountered in high-speed network measurement, points out the important role of sampling technology in network measurement, and then summarizes the detailed contents of sampling technology. Several common sampling methods are discussed, and the key technologies and important algorithms related to sampling measurement, such as Bloom filter algorithm and timeout strategy, are systematically analyzed. In this paper, we combine sampling technique with Bloom filter algorithm and dynamic timeout strategy, and propose a new sampling measurement algorithm for traffic measurement, in which Bloom filter is easy to implement and can find and match resources quickly. As one of the criteria for judging the flow output, the measurement accuracy of convection characteristics and the utilization rate of flow cache are greatly affected by the time-out strategy. The performance analysis and experimental simulation show that the proposed algorithm can improve the accuracy of the measurement at the same time. The specific research contents are as follows:. 1) in this paper, the Bloom filter algorithm and the improved CBF algorithm are studied in depth. Aiming at the defects of the current CBF algorithm, when the flow is too large, the counter overflow will be caused. In this paper, a dynamic counting Bloom filter (DCBF) algorithm is designed. The algorithm uses multi-layer CBFs, which can adaptively add new CBFs when the flow is large, and prevent CBF overflow from causing measurement error. DCBF is combined with the packet-based stream sampling algorithm. It can reduce the number of measurements and improve the accuracy of measurement. The comparison between this algorithm and the sampling algorithm based on CBF and FCBF is carried out through experimental simulation. The analysis shows that the algorithm proposed in this paper improves the accuracy of sampling. Reduced space utilization. 2) with the continuous expansion of network scale, the characteristics of network traffic become extremely complex and difficult to predict. The static sampling method can no longer meet the requirements of high-speed network measurement. In this paper, an adaptive flow sampling algorithm is proposed. The algorithm uses time to stratify packets, uses a fixed maximum number of samples in the layer, so that the measurement accuracy can be maintained when the network traffic is small, and the controllability of the resource can be guaranteed when the traffic increases sharply. In view of the defects of fixed timeout policy in network measurement, a two-layer adaptive time-out (TSAT) strategy is adopted to control the output of flow. TSAT strategy adopts two-layer flow space to maintain the independent flow space for the widely existing single-envelope flow in the system. By comparing the algorithm with the sampling algorithm based on NetFlow, it is proved that the algorithm is adaptive, accurate and resource controllable.
【學(xué)位授予單位】：曲阜師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.06

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 肖明忠,代亞非,李曉明;拆分型Bloom Filter[J];電子學(xué)報(bào);2004年02期

2 謝鯤;趙姣姣;張大方;畢夏安;;基于計(jì)數(shù)布魯姆過濾器的快速多維包分類算法[J];電子學(xué)報(bào);2010年05期

3 陳松;王珊;周明天;;基于實(shí)時(shí)分析的網(wǎng)絡(luò)測(cè)量抽樣統(tǒng)計(jì)模型[J];電子學(xué)報(bào);2010年05期

4 張震;汪斌強(qiáng);陳庶樵;郭通;;幾何布魯姆過濾器的設(shè)計(jì)與分析[J];電子學(xué)報(bào);2012年09期

5 程光 ,龔儉 ,丁偉;網(wǎng)絡(luò)測(cè)量及行為學(xué)研究綜述[J];計(jì)算機(jī)工程與應(yīng)用;2004年27期

6 劉衛(wèi)江,龔儉,丁偉,程光;流測(cè)量算法綜述[J];計(jì)算機(jī)工程與應(yīng)用;2005年29期

7 劉淵;劉元珍;李小航;;一種新的基于SCBF的流抽樣測(cè)量算法研究[J];計(jì)算機(jī)工程與應(yīng)用;2007年29期

8 劉衛(wèi)江;白磊;景泉;;基于Sample-CBF技術(shù)的長(zhǎng)流識(shí)別實(shí)現(xiàn)[J];計(jì)算機(jī)工程;2007年20期

9 張峰,譚興曄,雷振明;一種基于FCBF的流信息抽樣測(cè)量框架及算法[J];計(jì)算機(jī)應(yīng)用研究;2005年06期

10 談杰;李星;;網(wǎng)絡(luò)測(cè)量綜述[J];計(jì)算機(jī)應(yīng)用研究;2006年02期

本文編號(hào)：1665595