本文選題：云計(jì)算 + 服務(wù)質(zhì)量��； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：隨著計(jì)算機(jī)科學(xué)和網(wǎng)絡(luò)技術(shù)的不斷發(fā)展,近年來(lái)出現(xiàn)了以云計(jì)算為代表的新興技術(shù),云計(jì)算技術(shù)在提供大量高性能服務(wù)的同時(shí),也對(duì)承載著這些服務(wù)的傳輸網(wǎng)絡(luò)提出了新的要求。對(duì)高性能網(wǎng)絡(luò)監(jiān)控系統(tǒng)的需求也應(yīng)運(yùn)而生。本文針對(duì)上述需求,以云計(jì)算最關(guān)注的性能指標(biāo)之一,帶寬為研究課題,深入理解網(wǎng)絡(luò)監(jiān)控,重點(diǎn)研究了帶寬測(cè)量技術(shù)的原理和方法、網(wǎng)絡(luò)監(jiān)控技術(shù)的種類和特點(diǎn)以及適用于網(wǎng)絡(luò)帶寬的預(yù)測(cè)分析模型等,主要研究?jī)?nèi)容分為以下幾個(gè)部分。首先,對(duì)傳統(tǒng)的帶寬測(cè)量工具的原理和采用的技術(shù)進(jìn)行了分析�；谔綔y(cè)包時(shí)延的帶寬測(cè)量技術(shù)開(kāi)啟了帶寬測(cè)量的先河,但該技術(shù)忽略了網(wǎng)絡(luò)背景流量的突發(fā)性,且多次采樣求平均平滑誤差的效果不明顯,導(dǎo)致該類算法精確度較低。為了提高測(cè)量算法的精確度,引入了基于統(tǒng)計(jì)學(xué)原理的探測(cè)技術(shù),該方法具有接入帶寬門檻低、受背景流量影響小的特點(diǎn),能夠提高測(cè)量精度。其次,云計(jì)算環(huán)境下的應(yīng)用大多對(duì)服務(wù)質(zhì)量敏感且對(duì)可用帶寬的要求較高,基于統(tǒng)計(jì)的測(cè)量算法雖然測(cè)量精度高,但無(wú)法根據(jù)實(shí)時(shí)的可用帶寬來(lái)調(diào)節(jié)自身的測(cè)量力度,在可用帶寬不足時(shí)有可能會(huì)發(fā)生探測(cè)流量干擾正常業(yè)務(wù)流的情況。因此,提出了自適應(yīng)探測(cè)速率的可用帶寬測(cè)量算法,該算法能夠根據(jù)實(shí)時(shí)可用帶寬來(lái)調(diào)節(jié)探測(cè)流量,還能在可用帶寬低于閾值時(shí)發(fā)出預(yù)警。第三,研究了網(wǎng)絡(luò)監(jiān)控和預(yù)測(cè)技術(shù)。為了實(shí)時(shí)地監(jiān)控網(wǎng)絡(luò)中各段鏈路的可用帶寬,并且預(yù)測(cè)可用帶寬的變化趨勢(shì),設(shè)計(jì)出了網(wǎng)絡(luò)帶寬監(jiān)控預(yù)測(cè)模型。該模型根據(jù)監(jiān)控需求對(duì)監(jiān)控功能進(jìn)行了模塊的劃分,測(cè)量模塊借助帶寬測(cè)量算法對(duì)可用帶寬進(jìn)行監(jiān)控,分析模塊通過(guò)數(shù)據(jù)處理對(duì)測(cè)量結(jié)果進(jìn)行監(jiān)控,而反饋模塊則根據(jù)監(jiān)控結(jié)果和預(yù)測(cè)分析技術(shù)對(duì)將來(lái)一段時(shí)間內(nèi)可用帶寬的變化趨勢(shì)進(jìn)行了判斷。最后,為了實(shí)現(xiàn)網(wǎng)絡(luò)帶寬監(jiān)控預(yù)測(cè)模型,對(duì)云操作系統(tǒng)中的云資源監(jiān)控模型進(jìn)行了擴(kuò)展,將模型中的各個(gè)模塊重新封裝,增加了可用帶寬測(cè)量和預(yù)測(cè)的功能,提升了云平臺(tái)運(yùn)行的可靠性。為了驗(yàn)證該模型,在服務(wù)器中搭建實(shí)驗(yàn)環(huán)境,模擬了多個(gè)實(shí)驗(yàn),分別驗(yàn)證了模型的監(jiān)控準(zhǔn)確性、自適應(yīng)性和預(yù)測(cè)分析功能。實(shí)驗(yàn)結(jié)果表明,該模型在可用帶寬監(jiān)控和可用帶寬預(yù)測(cè)方面都具有良好的性能。
[Abstract]:With the continuous development of computer science and network technology, cloud computing technology, represented by cloud computing, has emerged in recent years. Cloud computing technology provides a large number of high-performance services at the same time. It also puts forward new requirements for the transmission network carrying these services. The demand for high performance network monitoring system also arises at the historic moment. In order to meet the above requirements, this paper focuses on the principle and method of bandwidth measurement technology, which is one of the most concerned performance indexes of cloud computing, and takes bandwidth as the research topic, deeply understanding network monitoring, and focusing on the principle and method of bandwidth measurement technology. The types and characteristics of network monitoring technology and the prediction and analysis model suitable for network bandwidth are mainly studied in the following parts. Firstly, the principle and technology of traditional bandwidth measurement tools are analyzed. The bandwidth measurement technology based on the detection packet delay opens the first step of the bandwidth measurement, but this technique ignores the sudden occurrence of the network background flow, and the effect of multiple sampling to average smoothing error is not obvious, which leads to the low accuracy of this kind of algorithm. In order to improve the accuracy of the measurement algorithm, the detection technology based on the principle of statistics is introduced. This method has the characteristics of low threshold of access bandwidth and small influence of background flow, which can improve the accuracy of measurement. Secondly, most applications in cloud computing environment are sensitive to the quality of service and require higher available bandwidth. Although the measurement algorithm based on statistics has high measurement accuracy, it can not adjust its measurement intensity according to the real-time available bandwidth. Detection traffic may interfere with normal traffic when the available bandwidth is insufficient. Therefore, an adaptive available bandwidth measurement algorithm for detection rate is proposed. The algorithm can adjust the detection flow according to the real-time available bandwidth, and also can give an early warning when the available bandwidth is below the threshold. Thirdly, the technology of network monitoring and prediction is studied. In order to monitor the available bandwidth of each segment of the network in real time and predict the trend of the available bandwidth, a network bandwidth monitoring and forecasting model is designed. The model divides the monitoring function into modules according to the monitoring requirements. The measurement module monitors the available bandwidth with the help of bandwidth measurement algorithm, and the analysis module monitors the measurement results through data processing. The feedback module judges the trend of available bandwidth in the future according to the monitoring results and predictive analysis techniques. Finally, in order to realize the network bandwidth monitoring and prediction model, the cloud resource monitoring model in the cloud operating system is extended, each module in the model is re-encapsulated, and the function of available bandwidth measurement and prediction is added. Improved the reliability of cloud platform operation. In order to verify the model, an experimental environment was built in the server, and several experiments were simulated, respectively, to verify the monitoring accuracy, adaptability and predictive analysis function of the model. Experimental results show that the model has good performance in both available bandwidth monitoring and available bandwidth prediction.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.06

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本文編號(hào)：1953276