本文選題：云計(jì)算　切入點(diǎn)：虛擬機(jī)　出處：《遼寧大學(xué)》2013年碩士論文

【摘要】：虛擬化技術(shù)采用隔離技術(shù)降低了云計(jì)算環(huán)境下集群的底層異構(gòu)性，通過虛擬資源設(shè)計(jì)提高了云計(jì)算環(huán)境下資源利用率，是云計(jì)算的核心技術(shù)之一，是云環(huán)境下目前的研究熱點(diǎn)。虛擬化中的動(dòng)態(tài)遷移技術(shù)能夠?qū)μ摂M機(jī)進(jìn)行無縫遷移，在保證業(yè)務(wù)運(yùn)行的同時(shí)將虛擬機(jī)從源物理主機(jī)遷移到目的物理主機(jī)，保證遷移的安全性和高效性。目前，云計(jì)算環(huán)境下的動(dòng)態(tài)遷移性能一般通過SaaS層的具體應(yīng)用的優(yōu)化來實(shí)現(xiàn)，，這樣雖然提高了應(yīng)用的跨平臺(tái)適應(yīng)性，但是卻顯著降低了動(dòng)態(tài)遷移性能。 本文針對(duì)Xen平臺(tái)環(huán)境，將實(shí)際應(yīng)用場(chǎng)景和Xen底層的遷移進(jìn)行結(jié)合，提出了基于Xen虛擬機(jī)的動(dòng)態(tài)遷移策略。具體工作如下： 首先，為了在云計(jì)算環(huán)境下進(jìn)行虛擬機(jī)遷移，Xen遷移時(shí)采用比較傳遞頁(yè)位圖和跳過頁(yè)位圖的方式來判斷內(nèi)存頁(yè)是否重傳。針對(duì)頁(yè)位圖比較帶來多次重傳增加網(wǎng)絡(luò)傳送開銷，提出基于AR模型的內(nèi)存優(yōu)化算法，根據(jù)記錄的內(nèi)存頁(yè)所有修改時(shí)間間隔來預(yù)測(cè)內(nèi)存頁(yè)的下次修改時(shí)間，當(dāng)下次修改時(shí)間大于某個(gè)閾值時(shí)進(jìn)行重傳。該內(nèi)存優(yōu)化算法縮短了虛擬機(jī)遷移的時(shí)間，減少了虛擬機(jī)遷移時(shí)的網(wǎng)絡(luò)開銷。 其次，在AR模型的基礎(chǔ)上，對(duì)縮短遷移時(shí)間的同時(shí)降低業(yè)務(wù)質(zhì)量的情況進(jìn)行分析處理。針對(duì)RATE_IS_SAVE模式下遷移時(shí)網(wǎng)絡(luò)帶寬最大化分配帶來業(yè)務(wù)預(yù)留帶寬不足，對(duì)原模式進(jìn)行自適應(yīng)優(yōu)化，提出基于指數(shù)平滑算法的網(wǎng)絡(luò)帶寬改進(jìn)策略，依據(jù)迭代過程中的臟頁(yè)產(chǎn)生速率合理預(yù)測(cè)業(yè)務(wù)預(yù)留帶寬，再將剩余帶寬分配給遷移，有效縮短了遷移時(shí)間，保證了遷移時(shí)的業(yè)務(wù)質(zhì)量。 最后，通過對(duì)基于AR模型的內(nèi)存優(yōu)化算法、基于指數(shù)平滑算法的網(wǎng)絡(luò)帶寬分配策略以及兩者結(jié)合在多個(gè)應(yīng)用類型下的實(shí)驗(yàn)數(shù)據(jù)收集，分析遷移時(shí)間、宕機(jī)時(shí)間和服務(wù)質(zhì)量等性能指標(biāo)。結(jié)果表明，本文提出的算法在縮短遷移時(shí)間的基礎(chǔ)上保證了遷移時(shí)的業(yè)務(wù)質(zhì)量。
[Abstract]:Virtualization technology, which is one of the core technologies of cloud computing, reduces the underlying heterogeneity of cluster in cloud computing environment by isolating technology, and improves the utilization ratio of resources in cloud computing environment through virtual resource design. Dynamic migration technology in virtualization can seamlessly migrate virtual machines and transfer virtual machines from source physical host to destination physical host while ensuring business operation. Ensure the security and efficiency of migration. At present, the dynamic migration performance in cloud computing environment is generally realized through the optimization of specific applications in the SaaS layer, although it improves the cross-platform adaptability of applications. However, dynamic migration performance is significantly reduced. In this paper, a dynamic migration strategy based on Xen virtual machine is proposed by combining the actual application scenario with the Xen underlying migration in the Xen platform environment. The specific work is as follows:. First of all, in order to judge whether the memory page is retransmitted by comparing the passing of page bitmap and skipping the page bitmap, the Xen migration of virtual machine in cloud computing environment brings multiple retransmissions to increase the network transmission overhead. A memory optimization algorithm based on AR model is proposed to predict the next modification time of the memory page according to all the modified time intervals of the recorded memory pages. The memory optimization algorithm shortens the migration time of virtual machine and reduces the network overhead of virtual machine migration. Secondly, on the basis of AR model, this paper analyzes and deals with the situation of shortening migration time and reducing service quality, aiming at the shortage of reserved bandwidth due to the maximum allocation of network bandwidth during migration in RATE_IS_SAVE mode. Based on the adaptive optimization of the original mode, a network bandwidth improvement strategy based on exponential smoothing algorithm is proposed. The reserved bandwidth is reasonably predicted according to the dirty page generation rate in the iterative process, and the remaining bandwidth is allocated to the migration. Effectively shorten the migration time, ensure the quality of the business migration. Finally, through the memory optimization algorithm based on AR model, the network bandwidth allocation strategy based on exponential smoothing algorithm and the experimental data collection under multiple application types, the migration time is analyzed. The results show that the proposed algorithm can guarantee the service quality of migration on the basis of shortening the migration time.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP302

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 蔡一兵;王春峰;孫利民;李忠誠(chéng);;網(wǎng)絡(luò)移動(dòng)支持研究[J];計(jì)算機(jī)科學(xué);2005年07期

2 向小軍;高陽(yáng);商琳;楊育彬;;基于Hadoop平臺(tái)的海量文本分類的并行化[J];計(jì)算機(jī)科學(xué);2011年10期

3 劉伯成;陳慶奎;;云計(jì)算中的集群資源模糊聚類劃分模型[J];計(jì)算機(jī)科學(xué);2011年S1期

4 曹立強(qiáng);羅紅兵;張曉霞;;集群環(huán)境中影響NFS文件系統(tǒng)帶寬的測(cè)試與分析[J];計(jì)算機(jī)工程;2007年19期

5 溫研;王懷民;;基于本地虛擬化技術(shù)的隔離執(zhí)行模型研究[J];計(jì)算機(jī)學(xué)報(bào);2008年10期

6 張彬彬;汪小林;楊亮;賴榮鳳;王振林;羅英偉;李曉明;;修改客戶操作系統(tǒng)優(yōu)化KVM虛擬機(jī)的I/O性能[J];計(jì)算機(jī)學(xué)報(bào);2010年12期

7 李強(qiáng);郝沁汾;肖利民;李舟軍;;云計(jì)算中虛擬機(jī)放置的自適應(yīng)管理與多目標(biāo)優(yōu)化[J];計(jì)算機(jī)學(xué)報(bào);2011年12期

8 張偉哲;張宏莉;張迪;程濤;;云計(jì)算平臺(tái)中多虛擬機(jī)內(nèi)存協(xié)同優(yōu)化策略研究[J];計(jì)算機(jī)學(xué)報(bào);2011年12期

9 葉可江;吳朝暉;姜曉紅;何欽銘;;虛擬化云計(jì)算平臺(tái)的能耗管理[J];計(jì)算機(jī)學(xué)報(bào);2012年06期

10 蔡志平,殷建平,祝恩;在Windows中執(zhí)行Ring0特權(quán)級(jí)代碼的幾種方法[J];計(jì)算機(jī)應(yīng)用;2001年06期

相關(guān)碩士學(xué)位論文 前1條

1 李穎;時(shí)間序列指數(shù)平滑算法的改進(jìn)研究[D];遼寧工程技術(shù)大學(xué);2009年

本文編號(hào)：1685957