本文關(guān)鍵詞：虛擬機(jī)動態(tài)資源分配及放置算法研究　出處：《復(fù)旦大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 云計算 虛擬機(jī) 動態(tài)分配 虛擬機(jī)放置 應(yīng)用感知 分組遺傳算法

【摘要】：“云計算”的概念自被提出之后,迅速成為科技領(lǐng)域最令人振奮的研究熱點,受到工業(yè)界和學(xué)術(shù)界廣泛關(guān)注。作為一項新興的戰(zhàn)略技術(shù),雖然還沒有一個關(guān)于它的統(tǒng)一定義,但學(xué)術(shù)界和工業(yè)界一般認(rèn)為云計算是以虛擬化技術(shù)為基礎(chǔ)、以按需付費為商業(yè)模式,具備彈性擴(kuò)展、動態(tài)分配和資源共享等特點的新型網(wǎng)絡(luò)化計算模式。 其中基礎(chǔ)設(shè)施即服務(wù)(Iaas)是云計算中最為基礎(chǔ)及支撐性的服務(wù),其存在的問題也是本論文集中關(guān)注的。IaaS通過虛擬化技術(shù)共享物理機(jī)資源池,用戶通過向運(yùn)營商租賃虛擬機(jī)來承載應(yīng)用系統(tǒng)。隨著運(yùn)營商的業(yè)務(wù)發(fā)展,必將導(dǎo)致云IDC中的基礎(chǔ)資源(如服務(wù)器,網(wǎng)絡(luò),存儲等)的大量聚集,物理資源分配調(diào)度技術(shù)的優(yōu)劣將直接影響到整體物理資源池的利用率,服務(wù)能力及SLA(Service-LevelAgreement,服務(wù)等級協(xié)議),因此成為IaaS云計算需要重點優(yōu)化和突破的關(guān)鍵技術(shù)問題。 對這些新問題的研究的缺乏將直接制約云計算資源的有效和集約的利用,降低云計算SLA的水平,造成云計算資源浪費及損失。傳統(tǒng)的彈性云服務(wù)雖然在一定程度上能實現(xiàn)根據(jù)應(yīng)用負(fù)載動態(tài)地對應(yīng)用的資源進(jìn)行增減,但通常是以虛擬機(jī)為單位的粗粒度的方法,造成資源利用率不足。另外,傳統(tǒng)的虛擬機(jī)放置問題研究主要集中于資源利用的提高,而沒有過多考慮多層應(yīng)用之間的關(guān)聯(lián)性。從而也會導(dǎo)致遷移一個過載的虛擬機(jī)之后給整個網(wǎng)絡(luò)帶來額外負(fù)載。 本論文深入研究了現(xiàn)有的虛擬機(jī)資源分配模型及虛擬機(jī)放置算法,提出了基于負(fù)載預(yù)測的動態(tài)資源分配和應(yīng)用感知的虛擬機(jī)放置算法�；谪�(fù)載預(yù)測的動態(tài)資源分配算法通過對每臺物理機(jī)中的虛擬機(jī)負(fù)載進(jìn)行監(jiān)測,并預(yù)估一段時間的資源使用情況,根據(jù)預(yù)測結(jié)果動態(tài)的在物理機(jī)層級為承載的虛擬機(jī)調(diào)整資源分配,因此能有效的、細(xì)粒度的使用物理機(jī)資源,減少全局遷移的發(fā)生。應(yīng)用感知的虛擬機(jī)放置算法采用分組遺傳算法,考慮不同虛擬機(jī)之間的耦合性,整合全局虛擬機(jī)的放置情況,減少物理機(jī)的使用,節(jié)約能耗,減少運(yùn)營成本。
[Abstract]:Since the concept of "cloud computing" was put forward, it has rapidly become the most exciting research hotspot in the field of science and technology, and has been widely concerned by industry and academia. It is a new strategic technology. Although there is no uniform definition of it, academia and industry generally believe that cloud computing is based on virtualization technology, with a business model based on demand, and flexible expansion. A new network computing model with the characteristics of dynamic allocation and resource sharing. Infrastructure as a service (Iaas) is the most basic and supporting services in cloud computing, and its problems are also concerned in this collection. IaaS sharing physical computer resource pool through virtualization technology. Users rent virtual machines from operators to host application systems. With the development of operators' services, the basic resources (such as servers, networks, storage, etc.) in the cloud IDC will be aggregated. Physical resource allocation and scheduling technology will directly affect the overall physical resource pool utilization, service capacity and SLA(Service-LevelAgreement. Service level agreements (SLAs) have become the key technical issues for IaaS cloud computing to be optimized and broken through. The lack of research on these new problems will directly restrict the efficient and intensive utilization of cloud computing resources and reduce the level of cloud computing SLA. The traditional flexible cloud services can dynamically increase or decrease the application resources according to the application load to a certain extent. However, it is usually coarse-grained method based on virtual machine, which results in insufficient utilization of resources. In addition, the traditional research on virtual machine placement mainly focuses on the improvement of resource utilization. It also leads to the migration of an overloaded virtual machine and brings additional load to the entire network without too much consideration of the correlation between the multi-tier applications. In this paper, the existing virtual machine resource allocation model and virtual machine placement algorithm are deeply studied. A dynamic resource allocation algorithm based on load prediction and an application-aware virtual machine placement algorithm are proposed. The dynamic resource allocation algorithm based on load prediction monitors the load of virtual machine in each physical machine. And predict the use of resources for a period of time, according to the results of the prediction dynamically in the physical machine level for the load of the virtual machine to adjust the allocation of resources, so it can be effective, fine-grained use of physical computer resources. In order to reduce the occurrence of global migration, the perceptual virtual machine placement algorithm uses grouping genetic algorithm, considering the coupling between different virtual machines, integrating the placement of global virtual machines, and reducing the use of physical machines. Save energy consumption, reduce operating costs.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP302

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 李進(jìn)超;陳靜怡;吳杰;梁瑾;;基于改進(jìn)分組遺傳算法的虛擬機(jī)放置研究[J];計算機(jī)工程與設(shè)計;2012年05期

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本文編號：1390572