發(fā)布時間：2018-03-24 11:15

  本文選題：虛擬化　切入點：內(nèi)存資源　出處：《東北大學(xué)》2013年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的飛速發(fā)展,其信息服務(wù)的快速增長以及用戶對信息服務(wù)的逐漸依賴的趨勢已經(jīng)勢不可擋了。而互聯(lián)網(wǎng)服務(wù)提供商一直以來,面臨著以更低成本提供更好服務(wù)的挑戰(zhàn)。因此擁有按使用付費和高性價比的云計算模式受到了越來越多企業(yè)以及用戶的青睞。而借助虛擬化技術(shù),云計算能將大規(guī)模計算資源統(tǒng)一管理,提高了資源利用效率,簡化了管理和維護成本,并為用戶提供易獲取、易擴展的按需服務(wù)。然而虛擬化技術(shù)和云計算平臺的結(jié)合帶來了全新的使用模式和資源整合,基于虛擬化技術(shù)的資源按需分配與調(diào)度可以提高云平臺資源的利用率,并且提升云服務(wù)的服務(wù)質(zhì)量,甚至它降低了云用戶的總體擁有成本。但是,物理服務(wù)器的資源邊界限制了資源的全局優(yōu)化能力。而現(xiàn)有的研究是在云平臺對各個虛擬機之間的內(nèi)存資源進行調(diào)度算法的優(yōu)化,從而提高整體的內(nèi)存資源利用率�；蛘呤沁M行架構(gòu)方面的設(shè)計,從地址映射方面,頁面交換機制,空閑頁面回收方面來進行改進,從而達到資源利用的目的。這些方法雖然都可以很好地提高內(nèi)存資源利用率,但大部分的虛擬機的內(nèi)存資源無論是空閑還是緊張,都需要重新進行一次內(nèi)存的分配、調(diào)度,或者是映射。這在無形中就會增加虛擬機的負(fù)擔(dān),從而會相對地降低內(nèi)存資源利用率。本文在其已有的全局優(yōu)化框架上進行相應(yīng)的改進,增添了當(dāng)虛擬機內(nèi)部資源空閑時的最小內(nèi)存邊界值,當(dāng)虛擬機內(nèi)存資源利用率低的時候,需要比較最小內(nèi)存邊界值,再將多余的內(nèi)存資源映射到全局的空閑內(nèi)存池中。然后基于上述框架,將虛擬機的內(nèi)存資源再細(xì)分為利用率低和利用率高的情況進行研究,并分別給出了兩種調(diào)節(jié)算法,以及這兩種算法之間的相互關(guān)系。當(dāng)內(nèi)存利用率低的時候,虛擬機將先比較判斷最小邊界值,將部分空閑內(nèi)存放入的全局的空閑內(nèi)存池中。而當(dāng)虛擬機內(nèi)存資源利用率高時,將通過全局空閑內(nèi)存池進行全局調(diào)節(jié)。結(jié)果是既降低了每次與全局空閑內(nèi)存池交換的次數(shù),又降低了虛擬機之間的內(nèi)存交換次數(shù),平均內(nèi)存資源利用效率將大大提高。最后本文對研究的內(nèi)容進行了實驗分析。實驗結(jié)果表明,該框架以及算法能夠很好地優(yōu)化云平臺中內(nèi)存資源配置,提升整個平臺的資源利用率,使關(guān)鍵任務(wù)的執(zhí)行有顯著的加速。測試結(jié)果更加展示了本文方法的性能優(yōu)勢以及良好的可用性。
[Abstract]:With the rapid development of the Internet, the rapid growth of its information services and the gradual dependence of users on information services have become unstoppable. Faced with the challenge of providing better services at lower cost, cloud computing with a pay-per-use and cost-effective model is increasingly popular among businesses and users. Cloud computing can unify the management of large-scale computing resources, improve resource utilization efficiency, simplify management and maintenance costs, and provide easy access to users. However, the combination of virtualization technology and cloud computing platform brings new usage patterns and resource integration. Resource allocation and scheduling based on virtualization technology can improve the utilization of cloud platform resources. And to improve the quality of service of cloud services, even reducing the overall cost of ownership of cloud users. However, The resource boundary of the physical server limits the global optimization ability of the resource, and the existing research is to optimize the memory resources among the virtual machines in the cloud platform. In order to improve the overall utilization of memory resources. Or the design of architecture, from address mapping, page exchange mechanism, free page recovery to improve, In order to achieve the purpose of resource utilization, although these methods can improve the utilization of memory resources, most of the memory resources of virtual machine, whether idle or tight, need to be allocated and scheduled again. Or mapping. This will increase the burden of virtual machines, which will reduce the utilization of memory resources. In this paper, the existing global optimization framework is improved. Adds the minimum memory boundary value when the virtual machine internal resource is idle, and the minimum memory boundary value needs to be compared when the virtual machine memory resource utilization is low, Then the redundant memory resources are mapped to the global free memory pool. Then, based on the above framework, the memory resources of the virtual machine are subdivided into low utilization and high utilization, and two adjustment algorithms are given respectively. When the memory utilization is low, the virtual machine will compare and judge the minimum boundary value, and put part of the free memory into the global free memory pool. When the memory utilization of the virtual machine is high, the virtual machine will put part of the free memory into the global free memory pool. The global adjustment will be made through the global free memory pool. The result is that the number of exchanges with the global free memory pool is reduced each time, and the number of memory exchanges between virtual machines is also reduced. The average efficiency of memory resource utilization will be greatly improved. Finally, the experimental results show that the proposed framework and algorithm can optimize the allocation of memory resources in cloud platform. Improving the resource utilization of the whole platform can accelerate the execution of critical tasks significantly. The test results show the performance advantages and good usability of this method.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP393.09

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本文編號：1658054