本文選題：云計算 + 服務質量��； 參考：《電子科技大學》2014年碩士論文

【摘要】：隨著計算機科學和網絡技術的不斷發(fā)展,近年來出現(xiàn)了以云計算為代表的新興技術,云計算技術在提供大量高性能服務的同時,也對承載著這些服務的傳輸網絡提出了新的要求。對高性能網絡監(jiān)控系統(tǒng)的需求也應運而生。本文針對上述需求,以云計算最關注的性能指標之一,帶寬為研究課題,深入理解網絡監(jiān)控,重點研究了帶寬測量技術的原理和方法、網絡監(jiān)控技術的種類和特點以及適用于網絡帶寬的預測分析模型等,主要研究內容分為以下幾個部分。首先,對傳統(tǒng)的帶寬測量工具的原理和采用的技術進行了分析�；谔綔y包時延的帶寬測量技術開啟了帶寬測量的先河,但該技術忽略了網絡背景流量的突發(fā)性,且多次采樣求平均平滑誤差的效果不明顯,導致該類算法精確度較低。為了提高測量算法的精確度,引入了基于統(tǒng)計學原理的探測技術,該方法具有接入帶寬門檻低、受背景流量影響小的特點,能夠提高測量精度。其次,云計算環(huán)境下的應用大多對服務質量敏感且對可用帶寬的要求較高,基于統(tǒng)計的測量算法雖然測量精度高,但無法根據(jù)實時的可用帶寬來調節(jié)自身的測量力度,在可用帶寬不足時有可能會發(fā)生探測流量干擾正常業(yè)務流的情況。因此,提出了自適應探測速率的可用帶寬測量算法,該算法能夠根據(jù)實時可用帶寬來調節(jié)探測流量,還能在可用帶寬低于閾值時發(fā)出預警。第三,研究了網絡監(jiān)控和預測技術。為了實時地監(jiān)控網絡中各段鏈路的可用帶寬,并且預測可用帶寬的變化趨勢,設計出了網絡帶寬監(jiān)控預測模型。該模型根據(jù)監(jiān)控需求對監(jiān)控功能進行了模塊的劃分,測量模塊借助帶寬測量算法對可用帶寬進行監(jiān)控,分析模塊通過數(shù)據(jù)處理對測量結果進行監(jiān)控,而反饋模塊則根據(jù)監(jiān)控結果和預測分析技術對將來一段時間內可用帶寬的變化趨勢進行了判斷。最后,為了實現(xiàn)網絡帶寬監(jiān)控預測模型,對云操作系統(tǒng)中的云資源監(jiān)控模型進行了擴展,將模型中的各個模塊重新封裝,增加了可用帶寬測量和預測的功能,提升了云平臺運行的可靠性。為了驗證該模型,在服務器中搭建實驗環(huán)境,模擬了多個實驗,分別驗證了模型的監(jiān)控準確性、自適應性和預測分析功能。實驗結果表明,該模型在可用帶寬監(jiān)控和可用帶寬預測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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.06

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本文編號：1953276