本文選題：數(shù)據(jù)中心網(wǎng)絡(luò) + 流量矩陣估計(jì)�。� 參考：《西南大學(xué)》2017年碩士論文

【摘要】：隨著云計(jì)算、電子商務(wù)、網(wǎng)絡(luò)游戲等Internet應(yīng)用領(lǐng)域的不斷延伸和擴(kuò)展,目前越來越多的應(yīng)用需要進(jìn)行大規(guī)模的數(shù)據(jù)存儲(chǔ)和應(yīng)用處理,網(wǎng)絡(luò)中的數(shù)據(jù)已然出現(xiàn)爆炸式的增長(zhǎng)。數(shù)據(jù)中心網(wǎng)絡(luò)就是隨著人們對(duì)海量數(shù)據(jù)的高效存儲(chǔ)和處理要求應(yīng)運(yùn)而生的。然而,擴(kuò)展的網(wǎng)絡(luò)規(guī)模和種類繁多的應(yīng)用服務(wù)類型加重了網(wǎng)絡(luò)操作員對(duì)數(shù)據(jù)中心網(wǎng)絡(luò)管理的負(fù)擔(dān)。流量矩陣可以完整描述網(wǎng)絡(luò)中的全部流量狀態(tài)信息,它不僅可以為學(xué)者們研究網(wǎng)絡(luò)中流量問題提供基本的網(wǎng)絡(luò)參數(shù),還是多個(gè)重要領(lǐng)域的關(guān)鍵輸入。但是因?yàn)閿?shù)據(jù)中心網(wǎng)絡(luò)中網(wǎng)絡(luò)規(guī)模較大、結(jié)構(gòu)復(fù)雜,網(wǎng)絡(luò)中的流的行為不穩(wěn)定,流交互非常頻繁,所以直接測(cè)量數(shù)據(jù)中心網(wǎng)絡(luò)中端到端的流量是非常困難的,并且需要花費(fèi)較大的開銷。網(wǎng)絡(luò)層析成像技術(shù)是近年來提出的一種新的推斷網(wǎng)絡(luò)端到端的測(cè)量技術(shù),它是通過易取得的鏈路數(shù)據(jù)推斷端到端的流量,目前在傳統(tǒng)的計(jì)算機(jī)網(wǎng)絡(luò)中已有大量的研究成果,然而由于數(shù)據(jù)中心網(wǎng)絡(luò)與傳統(tǒng)網(wǎng)絡(luò)在流量特征、交換機(jī)扮演角色、大量冗余路徑等方面的不同,該技術(shù)不能直接應(yīng)用在當(dāng)前的數(shù)據(jù)中心網(wǎng)絡(luò)。目前針對(duì)樹型的數(shù)據(jù)中心網(wǎng)絡(luò)結(jié)構(gòu)中獨(dú)特的分層特點(diǎn),采用分解網(wǎng)絡(luò)的方式可以降低估計(jì)整個(gè)網(wǎng)絡(luò)流量矩陣的復(fù)雜性。然而,樹形結(jié)構(gòu)的對(duì)稱性又容易使得收集鏈路數(shù)據(jù)過程中存在數(shù)據(jù)的不完整和不準(zhǔn)確性,鏈路測(cè)量誤差會(huì)對(duì)估計(jì)誤差造成一定的影響。因此,本文主要將層析成像技術(shù)和流量矩陣估計(jì)作為核心研究問題,提出了拓?fù)浞纸庀碌幕趯游鲋亓臻g的數(shù)據(jù)中心網(wǎng)絡(luò)流量矩陣估計(jì)算法。本文的主要研究?jī)?nèi)容如下:首先,為了降低數(shù)據(jù)中心網(wǎng)絡(luò)流量矩陣估計(jì)的復(fù)雜性,提出將整個(gè)網(wǎng)絡(luò)分解為多個(gè)相對(duì)獨(dú)立的網(wǎng)絡(luò)單元,稱之為簇,從而將估計(jì)整個(gè)網(wǎng)絡(luò)的流量矩陣降解為估計(jì)多個(gè)小的網(wǎng)絡(luò)單元的流量矩陣。其次,結(jié)合鏈路信息和重力模型結(jié)合得到數(shù)據(jù)中心網(wǎng)絡(luò)的粗粒度流量特征和簡(jiǎn)單的流量矩陣估計(jì),通過加入附加的鏈路信息和采用類馬氏距離衡量估計(jì)誤差,提出基于流量特征的層析重力空間的迭代算法(ICGA)。此外,考慮到樹形數(shù)據(jù)中心網(wǎng)絡(luò)結(jié)構(gòu)具有的對(duì)稱性和收集得到的鏈路數(shù)據(jù)存在適量數(shù)據(jù)丟失和錯(cuò)誤的情況,提出未使用數(shù)據(jù)中心網(wǎng)絡(luò)先驗(yàn)流量特征的簡(jiǎn)單層析重力空間流量矩陣估計(jì)算法(SAWP)。最后,搭建了Network Simulator2(NS-2)仿真平臺(tái)模擬整個(gè)實(shí)驗(yàn)環(huán)境。結(jié)果表明:通過對(duì)比分析算法的時(shí)間復(fù)雜度,表明在適量數(shù)據(jù)丟失下第二種方法比第一種方法更加簡(jiǎn)單。其次,仿真表明所提的算法在實(shí)際測(cè)量數(shù)據(jù)中比其他算法估計(jì)更加準(zhǔn)確;在少量數(shù)據(jù)丟失的情景下,提出的兩種算法在簇間的流量矩陣估計(jì)下性能更相似;當(dāng)對(duì)獲得的測(cè)量數(shù)據(jù)加入不同層次的噪聲之后,可以發(fā)現(xiàn)估計(jì)的誤差隨著噪聲層次的增加而增加,但是因?yàn)榉纸庵蟮木W(wǎng)絡(luò)流量相對(duì)更穩(wěn)定,簇內(nèi)的誤差增加更緩慢。
[Abstract]:With cloud computing, e-commerce, online games and other Internet applications continue to extend and expand, more and more applications at present the need for data storage and processing of large-scale application, the data in the network has emerged. The explosive growth of data center network is people with efficient storage and processing of massive data requirements however came into being. The expansion of network scale, and a wide variety of application service types increased the network operator to the data center network management burden. Traffic matrix can be a complete description of all of the traffic state information, it can not only provide the basic parameters of the network flow problem for scholars in the network, or a number of important areas. But the key input because the size of the network data center network in large, complex structure, network flow behavior of unstable flow interaction is very frequent, So the direct measurement data center network end-to-end flow is very difficult, and takes a large overhead. Network tomography is a new inference of network end to end measurement technology, it is easy to get through the link data from end to end flow, at present in the traditional the computer network has a large number of research results, however, because the data center network and traditional network traffic characteristics play a role in the exchange, a large number of redundant paths, and so different, the technology can not be directly applied in the data center network at present. The unique characteristics of the hierarchical network structure of data center according to the type of tree, the decomposition of the network the method can reduce the complexity of the whole network traffic matrix estimation. However, the symmetry of the structure of the tree and easily makes the data are collected in the process of data link Incomplete and inaccurate link, the measurement error will cause a certain impact on the estimation error. Therefore, in this paper the tomography technology and traffic matrix estimation is the core issue, put forward the topological decomposition algorithm under gravity space chromatography data center network based on traffic matrix estimation. The main contents of this paper are as follows: firstly, in order to to reduce the complexity of the data center network traffic matrix estimation, the whole network is divided into a plurality of independent network unit, called clusters, so as to estimate traffic matrix of the whole network solution for reducing the traffic matrix estimation of multiple small network elements. Secondly, combined with the link information and the gravity model with coarse grain flow characteristics get the data center network and traffic matrix simple estimation, by adding additional link information and using the Mahalanobis distance measure estimation error is proposed Gravity flow chromatography iterative algorithm based on feature space (ICGA). In addition, taking into account the amount of data loss and error of the existing link data with tree network structure of data center symmetry and collected, this simple chromatography gravity space traffic matrix without the use of data center network traffic characteristics a priori estimation algorithm (SAWP) finally, set up the Network Simulator2 (NS-2) in the experimental environment simulation platform. The results show that: through the comparative analysis of the time complexity of the algorithm, that is more simple than the first method of the two methods in the amount of lost data. Secondly, the simulation shows that the proposed algorithm in the actual measurement data in the estimation is more accurate than other algorithms in a small amount; data loss situation, traffic matrix, the two algorithms in the inter cluster estimation performance is more similar to the measured data obtained; when the addition of After the same level of noise, it is found that the estimation error increases with the increase of noise level. However, because the network traffic after decomposition is relatively stable, the error in the cluster increases more slowly.

【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.06

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本文編號(hào)：1759180