【摘要】：當(dāng)前隨著云計(jì)算技術(shù)的飛速發(fā)展和虛擬化技術(shù)的廣泛應(yīng)用，虛擬機(jī)內(nèi)存管理方面的研究越來越受到研究者的重視，相繼開發(fā)了許多高效的虛擬機(jī)內(nèi)存管理技術(shù)。但現(xiàn)有的虛擬機(jī)內(nèi)存管理技術(shù)大都集中在對自身內(nèi)存的調(diào)節(jié)方面，缺乏了全局狀態(tài)下對遠(yuǎn)程虛擬機(jī)閑置內(nèi)存的使用，這就降低了在云計(jì)算環(huán)境中整個虛擬機(jī)集群的內(nèi)存利用率，也使得虛擬機(jī)內(nèi)存管理技術(shù)的應(yīng)用范圍受到限制。本文便是針對當(dāng)前出現(xiàn)的這些問題展開研究，提出了基于云計(jì)算環(huán)境的虛擬機(jī)內(nèi)存管理模型DMBMM。 DMBMM主要實(shí)現(xiàn)了以下三個技術(shù)關(guān)鍵點(diǎn)：虛擬機(jī)內(nèi)存信息管理策略、虛擬機(jī)內(nèi)存平衡策略和基于網(wǎng)絡(luò)存儲的內(nèi)存切換方法。在虛擬機(jī)內(nèi)存信息管理方面，本文使用了雙層內(nèi)存信息管理結(jié)構(gòu)來管理和統(tǒng)計(jì)所有虛擬機(jī)的內(nèi)存利用信息。該方法首先由各個虛擬機(jī)集群中的內(nèi)存監(jiān)測模塊統(tǒng)計(jì)自身集群內(nèi)虛擬機(jī)的內(nèi)存利用信息，然后每個內(nèi)存監(jiān)測模塊再與內(nèi)存信息管理服務(wù)器交互，完成對所有虛擬機(jī)內(nèi)存信息的統(tǒng)計(jì)。在虛擬機(jī)內(nèi)存平衡策略方面，本文根據(jù)每個虛擬機(jī)集群所處的內(nèi)存狀態(tài)是緊缺狀態(tài)還是充裕狀態(tài)，分別執(zhí)行單機(jī)平衡策略和多機(jī)平衡策略。在基于網(wǎng)絡(luò)存儲的內(nèi)存切換方法方面，，本文在DMBMM中引入了網(wǎng)絡(luò)存儲技術(shù)，利用現(xiàn)有的網(wǎng)絡(luò)實(shí)現(xiàn)了內(nèi)存的遠(yuǎn)程使用，解決了現(xiàn)有的虛擬機(jī)內(nèi)存管理技術(shù)無法使用遠(yuǎn)程內(nèi)存的問題。 本文利用MyEclipse開發(fā)的兩個微基準(zhǔn)測試程序獲取實(shí)驗(yàn)數(shù)據(jù)。結(jié)合本實(shí)驗(yàn)軟硬件開發(fā)、測試的實(shí)際環(huán)境情況，驗(yàn)證了單機(jī)平衡策略和多機(jī)平衡策略的有效性。與常用的虛擬機(jī)內(nèi)存管理模型進(jìn)行對比分析，證明了該模型在有效利用遠(yuǎn)程內(nèi)存，提高內(nèi)存存取速度方面的優(yōu)勢，從實(shí)驗(yàn)的角度印證了本論文思路的可行性。
[Abstract]:With the rapid development of cloud computing technology and the wide application of virtualization technology, researchers pay more and more attention to the research of virtual machine memory management, and many efficient virtual machine memory management technologies have been developed one after another. However, most of the existing virtual machine memory management technologies focus on the adjustment of their own memory, and lack of the use of idle memory in the global state, which reduces the memory utilization of the entire virtual machine cluster in the cloud computing environment. It also limits the application of virtual machine memory management technology. In this paper, we present a virtual machine memory management model (DMBMM.) based on cloud computing. DMBMM mainly implements the following three key points: virtual machine memory information management strategy, virtual machine memory balance strategy and memory switching method based on network storage. In the aspect of virtual machine memory information management, this paper uses the double layer memory information management structure to manage and statistics the memory utilization information of all virtual machines. Firstly, the memory monitoring module in each virtual machine cluster counts the memory utilization information of the virtual machine in its own cluster, and then each memory monitoring module interacts with the memory information management server. Complete the statistics of all virtual machine memory information. In terms of virtual machine memory balance strategy, according to whether the memory state of each virtual machine cluster is scarce or abundant, the single machine balance strategy and the multiple machine balance strategy are implemented respectively. In the aspect of the memory switching method based on network storage, this paper introduces the network storage technology into DMBMM, realizes the remote use of memory by using the existing network, and solves the problem that the existing virtual machine memory management technology can not use remote memory. In this paper, two microbenchmark programs developed by MyEclipse are used to obtain experimental data. Combined with the software and hardware development of the experiment, the effectiveness of the single machine balancing strategy and the multi-machine balancing strategy is verified. Compared with the common virtual machine memory management model, it proves the advantage of the model in using remote memory effectively and improving the memory access speed. The feasibility of this paper is verified from the point of view of experiment.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP302;TP333.1

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