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

  更多相關文章： Xen虛擬機 負載模型 主成分分析 馬爾科夫 神經(jīng)網(wǎng)絡

【摘要】：Xen是一種被廣泛應用的虛擬化軟件平臺,具備出色的隔離特性。隔離特性是通過引入VMM層實現(xiàn)的,Xen是VMM的一種具體的實現(xiàn)載體。由于本文的研究涉及到修改VMM的源代碼,而Xen開放源代碼的特性恰好為本文的衰退分析研究提供了實證基礎,因此本文采用Xen作為虛擬化系統(tǒng)的研究載體。 在分析了國內(nèi)外軟件衰退的研究現(xiàn)狀的基礎上,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量的方式。前者的主要思想是：借助馬爾科夫過程,Petri網(wǎng)等數(shù)學工具刻畫系統(tǒng)運行時狀態(tài)變遷的模型,并應用數(shù)學方法求解最優(yōu)自愈時間間隔,適用于具有靜態(tài)衰退剖面的場景。而后者的主要思想是：持續(xù)的監(jiān)測系統(tǒng)運行時的表征性能參數(shù),分析系統(tǒng)當前所處的性能狀態(tài),并綜合考慮目前的實時負載等因素確定最優(yōu)自愈時刻。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景。 基于上述背景分析,我們首先針對Xen虛擬化系統(tǒng),進行系統(tǒng)監(jiān)測,設計并實現(xiàn)了一種系統(tǒng)監(jiān)測工具,負責從VMM層采集運行時的VMM和VM資源使用狀態(tài)信息,以及主要系統(tǒng)功能部件的活動信息；在采集數(shù)據(jù)的基礎上,研究衰退分析方法,并設計了衰退分析系統(tǒng),提出的衰退分析方法考慮了不同的負載特征對于衰退預測和識別準確性的影響,建立了負載模型用于區(qū)分不同負載模式,應用主成分分析方法對于資源使用信息進行深入分析,識別導致衰退的關鍵參數(shù),進一步地,研究了改進的馬爾科夫和人工神經(jīng)網(wǎng)絡相結(jié)合的衰退預測方法識別和預測軟件衰退；結(jié)合負載模型和衰退預測方法,提出了一種自適應的衰退分析方法,并進行了系統(tǒng)驗證。
[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.

【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP302

【參考文獻】

相關期刊論文 前8條

1 孟海寧;齊勇;侯迪;;基于非馬爾可夫隨機Petri網(wǎng)的軟件再生建模與分析[J];計算機學報;2007年12期

2 范新媛,徐國治,應忍冬,蔣樂天;基于檢查點和Rejuvenation的軟件運行模型分析[J];計算機仿真;2004年01期

3 管雪風;;虛擬化技術——淺議VMWARE虛擬機的應用[J];信息技術教育;2007年12期

4 徐建;張琨;劉鳳玉;;基于Linux的計算系統(tǒng)性能監(jiān)控[J];南京理工大學學報(自然科學版);2007年05期

5 范新媛,徐國治,應忍冬;基于檢查點和Rejuvenation的軟件可靠性建模分析[J];系統(tǒng)仿真學報;2003年11期

6 游靜;徐建;趙學龍;劉鳳玉;;嵌套的基于時間的軟件抗衰策略模型與分析[J];系統(tǒng)仿真學報;2006年04期

7 徐建;張琨;游靜;劉鳳玉;;基于分形的軟件衰退預測[J];系統(tǒng)仿真學報;2007年03期

8 趙天海;齊勇;沈鈞毅;侯迪;鄭曉梅;;應用服務器多態(tài)老化模型和最優(yōu)再生策略研究[J];系統(tǒng)仿真學報;2007年08期

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本文編號：1366464