本文選題：云計算 + 虛擬化技術(shù)　； 參考：《東北大學(xué)》2012年碩士論文

【摘要】：隨著分布式計算、并行計算、和網(wǎng)格計算的發(fā)展,云計算開始形成并不斷地完善。云計算是基于虛擬化技術(shù),將IT資源構(gòu)成一個動態(tài)的虛擬資源池,以服務(wù)的形式供外界使用。虛擬化技術(shù)能夠有效進(jìn)行服務(wù)整合以減少服務(wù)器數(shù)量從而降低功耗,增強(qiáng)服務(wù)器的可靠性,并能顯著簡化IT基礎(chǔ)設(shè)施、優(yōu)化資源以及降低風(fēng)險。然而虛擬化技術(shù)同時帶來的底層物理架構(gòu)的變化和虛擬機(jī)上用戶服務(wù)的持續(xù)變化給云計算環(huán)境中底層的物理資源的優(yōu)化分配方法帶來了前所未有的挑戰(zhàn)。由于虛擬機(jī)上的用戶服務(wù)的持續(xù)變化,虛擬機(jī)監(jiān)視器無法監(jiān)測到每臺虛擬機(jī)的各類資源的實際使用量,更無法預(yù)測到用戶服務(wù)的性能狀態(tài),如何合理有效地完成資源池中的資源調(diào)度分配以提高計算資源的利用率及滿足虛擬機(jī)上用戶服務(wù)的性能要求是云計算環(huán)境中資源分配研究的重要課題。 本文圍繞基于虛擬化的云計算進(jìn)行了廣泛的理論與技術(shù)研究,通過研究掌握了虛擬化的云計算技術(shù),闡述了云環(huán)境下虛擬機(jī)資源分配方法的分類和性能預(yù)測模型的分類,詳細(xì)論述了基于收益的云環(huán)境虛擬機(jī)資源分配的過程,針對虛擬機(jī)上的用戶服務(wù)的性能預(yù)測和基于收益的虛擬機(jī)資源分配兩個問題展開論述和研究,主要提出了基于隨機(jī)梯度回歸的服務(wù)性能預(yù)測算法和基于收益的云環(huán)境虛擬機(jī)資源動態(tài)分配方法。主要內(nèi)容如下。 首先,考慮云端資源提供者的收益和虛擬機(jī)上的用戶服務(wù)的性能問題,提出了基于收益的云環(huán)境虛擬機(jī)資源動態(tài)分配過程,給出基于收益的云環(huán)境虛擬機(jī)資源動態(tài)分配框架； 其次,針對虛擬機(jī)上的用戶服務(wù)的性能預(yù)測,利用數(shù)據(jù)預(yù)處理方法對服務(wù)性能指標(biāo)數(shù)據(jù)進(jìn)行預(yù)處理,采用基于隨機(jī)梯度回歸的服務(wù)性能預(yù)測模型預(yù)測服務(wù)的平均響應(yīng)時間變化情況,為是否進(jìn)行虛擬機(jī)資源分配提供依據(jù)； 再次,針對虛擬機(jī)上的用戶服務(wù)所需要的各類資源是不確定的,本文采用灰色預(yù)測的方法對虛擬機(jī)上的用戶服務(wù)對各類資源的預(yù)需求量進(jìn)行預(yù)測,為資源分配提供數(shù)據(jù)依據(jù)； 最后,根據(jù)用戶的預(yù)算和平均響應(yīng)時間的要求,給各個虛擬機(jī)上的用戶服務(wù)劃分服務(wù)級別,基于收益最大化的原則,提出基于收益的云環(huán)境虛擬機(jī)資源動態(tài)分配方法,根據(jù)云端資源提供者滿足每個虛擬機(jī)上的用戶服務(wù)的服務(wù)質(zhì)量要求,得到不同的收益(增加或減少),建立資源與云端資源提供者的收益的關(guān)系,動態(tài)的分配虛擬機(jī)資源,提高資源的利用率。 本文針對上述內(nèi)容中的用戶服務(wù)的性能預(yù)測算法-基于隨機(jī)梯度回歸的服務(wù)性能預(yù)測算法進(jìn)行了仿真實驗,進(jìn)行了對比實驗,結(jié)果顯示該算法的具有較高的精度,可以為虛擬機(jī)的資源分配提供可靠的依據(jù)。針對本文提出的基于收益的云環(huán)境虛擬機(jī)資源動態(tài)分配方法,通過對基礎(chǔ)云計算平臺Hadoop中的虛擬機(jī)資源動態(tài)分配的需求實例的研究,對虛擬機(jī)對各類資源的預(yù)需求量的預(yù)測驗證實例、邊際收益和邊際成本求解過程驗證實例、各個運行用戶服務(wù)的虛擬機(jī)的各類資源的分配驗證實例進(jìn)行定量的驗證和分析,得出的結(jié)果是本文提出的基于收益的云環(huán)境虛擬機(jī)資源動態(tài)分配方法是可行的和有效的。
[Abstract]:With the development of distributed computing, parallel computing and grid computing, cloud computing began to form and constantly improve. Cloud computing is based on virtualization technology, IT resources to form a virtual resource pool is a dynamic, in the form of services for external use. The virtualization technology can effectively reduce the number of servers and service integration to reduce power, enhance the reliability of server, and it can significantly simplify IT infrastructure, optimize resources and reduce risk. However, changes of virtualization technology and the underlying physical architecture and virtual machine users continued to change the underlying computational physics resources environment to the cloud distribution optimization method has brought hitherto unknown challenge due to the continued. The change on the virtual machine service, virtual machine monitor cannot monitor the actual usage of various resources of each virtual machine, but can not forecast to The performance status of user service, how to efficiently and efficiently allocate and allocate resources in resource pool to improve the utilization of computing resources and satisfy the performance requirements of user services on virtual machines is an important topic in resource allocation research in cloud computing.
The virtual cloud computing based on the theoretical and technical study in this dissertation, through research and master virtual cloud computing technology, expounds the classification of virtual machine resource allocation method under cloud environment classification and performance prediction model, discussed in detail the process of cloud environment virtual machine resource allocation based on income, according to the performance the prediction on the virtual machine user service and two problems of virtual machine resource revenue allocation based on discussion and research, mainly put forward the service performance of stochastic gradient regression prediction algorithm and dynamic resource allocation method based on cloud virtual machine environment based on revenue. The main contents are as follows.
First, considering the revenue of cloud resource providers and the performance of user services on virtual machines, we propose a revenue based dynamic allocation process of virtual machine resources in cloud environment, and give a revenue based dynamic allocation framework of virtual machine resources in cloud environment.
Secondly, according to the performance of virtual machine service users on the service performance index prediction, using data preprocessing method of data preprocessing, the average response time of change of service performance of stochastic gradient regression models for forecasting service based on the situation, whether virtual machine resource allocation;
Thirdly, all kinds of resources needed for user service on virtual machine are uncertain. In this paper, we use grey prediction method to predict the pre demand of user service and various resources on virtual machine, so as to provide data basis for resource allocation.
Finally, according to the user's budget and the average response time to the user service requirements, service level division of each virtual machine, based on the principle of profit maximization, the dynamic resource allocation method of cloud virtual machine environment based on revenue, according to the cloud resource providers meet each virtual machine users on the service quality of service requirements, get different income (increase or decrease), the relationship between the establishment of resources and cloud resource provider revenue, dynamic allocation of virtual machine resources, improve the utilization rate of resources.
Algorithm of service performance prediction algorithm based on stochastic gradient regression simulation experiment was carried out to predict the performance of the content of user services, conducted a comparative experiment results show that the algorithm has high accuracy, provide a reliable basis for the allocation of resources to the virtual machine. The dynamic resource allocation method in the cloud environment virtual machine based on revenue is put forward in this paper, through the research of virtual machine computing of dynamic resource allocation in Hadoop based on cloud platform needs examples, examples of the pre forecast demand for virtual machines of all types of resources, examples the marginal revenue and marginal cost calculation, examples and allocate resources of virtual machines running each user service the verification and quantitative analysis, the result is the proposed dynamic resource allocation method of cloud virtual machine environment based on revenue is Good and effective.

【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】：2012
【分類號】：TP302

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本文編號：1742601