本文關(guān)鍵詞：Xen虛擬機(jī)環(huán)境下的軟件衰退研究　出處：《南京理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： Xen虛擬機(jī) 負(fù)載模型 主成分分析 馬爾科夫 神經(jīng)網(wǎng)絡(luò)

【摘要】：Xen是一種被廣泛應(yīng)用的虛擬化軟件平臺(tái),具備出色的隔離特性。隔離特性是通過(guò)引入VMM層實(shí)現(xiàn)的,Xen是VMM的一種具體的實(shí)現(xiàn)載體。由于本文的研究涉及到修改VMM的源代碼,而Xen開(kāi)放源代碼的特性恰好為本文的衰退分析研究提供了實(shí)證基礎(chǔ),因此本文采用Xen作為虛擬化系統(tǒng)的研究載體。 在分析了國(guó)內(nèi)外軟件衰退的研究現(xiàn)狀的基礎(chǔ)上,論文指出現(xiàn)有的兩大研究方向分別是基于理論模型和基于測(cè)量的方式。前者的主要思想是：借助馬爾科夫過(guò)程,Petri網(wǎng)等數(shù)學(xué)工具刻畫(huà)系統(tǒng)運(yùn)行時(shí)狀態(tài)變遷的模型,并應(yīng)用數(shù)學(xué)方法求解最優(yōu)自愈時(shí)間間隔,適用于具有靜態(tài)衰退剖面的場(chǎng)景。而后者的主要思想是：持續(xù)的監(jiān)測(cè)系統(tǒng)運(yùn)行時(shí)的表征性能參數(shù),分析系統(tǒng)當(dāng)前所處的性能狀態(tài),并綜合考慮目前的實(shí)時(shí)負(fù)載等因素確定最優(yōu)自愈時(shí)刻。通常利用數(shù)據(jù)挖掘和人工智能方法建模分析,適用于具有可變衰退剖面的場(chǎng)景。 基于上述背景分析,我們首先針對(duì)Xen虛擬化系統(tǒng),進(jìn)行系統(tǒng)監(jiān)測(cè),設(shè)計(jì)并實(shí)現(xiàn)了一種系統(tǒng)監(jiān)測(cè)工具,負(fù)責(zé)從VMM層采集運(yùn)行時(shí)的VMM和VM資源使用狀態(tài)信息,以及主要系統(tǒng)功能部件的活動(dòng)信息；在采集數(shù)據(jù)的基礎(chǔ)上,研究衰退分析方法,并設(shè)計(jì)了衰退分析系統(tǒng),提出的衰退分析方法考慮了不同的負(fù)載特征對(duì)于衰退預(yù)測(cè)和識(shí)別準(zhǔn)確性的影響,建立了負(fù)載模型用于區(qū)分不同負(fù)載模式,應(yīng)用主成分分析方法對(duì)于資源使用信息進(jìn)行深入分析,識(shí)別導(dǎo)致衰退的關(guān)鍵參數(shù),進(jìn)一步地,研究了改進(jìn)的馬爾科夫和人工神經(jīng)網(wǎng)絡(luò)相結(jié)合的衰退預(yù)測(cè)方法識(shí)別和預(yù)測(cè)軟件衰退；結(jié)合負(fù)載模型和衰退預(yù)測(cè)方法,提出了一種自適應(yīng)的衰退分析方法,并進(jìn)行了系統(tǒng)驗(yàn)證。
[Abstract]:Xen is a widely used virtual software platform, has good isolation characteristics. The isolation characteristic is through the introduction of VMM layer, Xen VMM is the carrier of a concrete. Because of this research involves modifying the source code of VMM, and the characteristics of Xen open source code just provides demonstration based on the regression analysis, this paper uses Xen as the carrier of the virtual system.
Based on the analysis of the research status of domestic and foreign software recession on the paper pointed out that the two existing research directions are based on the theoretical model and measurement methods based on the former. The main idea is: with the help of Markoff, Petri and other mathematical tools to depict system runtime state transition model, and the application of mathematical methods for solving the optimal the healing time interval, suitable for static scenes. The main idea of the recession section and the latter is: characterization of performance parameter monitoring system of continuous time, the performance analysis system, and considering the real-time load current and other factors to determine the optimal self-healing moment. Usually use data mining and artificial intelligence method modeling and analysis for, with a variable profile recession scenario.
Based on the analysis of the above background, we firstly Xen virtual system, monitoring system, design and implement a system monitoring tool, responsible for the use of state information from the VMM layer collects the runtime VMM and VM resources, the main functional parts and system information; in collecting data on the basis of the analysis of recession, and design the recession analysis system, the recession analysis method considering different load characteristics for the impact of the recession prediction accuracy and recognition, a load model for different load model, using principal component analysis method to analyze the resource usage information, resulting in the identification of key parameters, decline further, study combined with the improved Markoff and artificial neural network prediction method to identify and predict the recession software recession; a combination of load model and recession prediction method, extraction An adaptive regression analysis method is presented and the system verification is carried out.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP302

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