本文選題：虛擬化 + 數(shù)據(jù)格式　； 參考：《西北師范大學》2013年碩士論文

【摘要】：為了提高數(shù)據(jù)中心所有物理服務器的資源利用率和能源使用率，可以通過動態(tài)整合虛擬機的方式來實現(xiàn)。虛擬機動態(tài)整合的主要實現(xiàn)方式有物理服務器之間虛擬機的實時遷移和動態(tài)地將空閑的物理服務器轉(zhuǎn)換到低能耗模式。 本文主要研究監(jiān)控數(shù)據(jù)的傳輸格式，資源利用預測以及物理服務器超載和低負載情況下的虛擬機重定位。首先，分析典型監(jiān)控系統(tǒng)的數(shù)據(jù)傳輸格式，結(jié)合監(jiān)控信息的特點，提出一種優(yōu)化的數(shù)據(jù)傳輸格式，該格式將監(jiān)控信息進行統(tǒng)一的管理。其次，通過分析灰色模型和馬爾科夫理論的特點，提出自反饋灰色馬爾科夫預測模型(Self-feedback Grey Markov model，SfGM)，該模型以每臺虛擬機的某一資源利用的N個連續(xù)的歷史數(shù)據(jù)作為輸入，通過分析這N個數(shù)據(jù)的內(nèi)在關系，預測虛擬機未來一段時間對該資源的利用情況。最后，動態(tài)的監(jiān)控數(shù)據(jù)中心的所有虛擬機和物理服務器的資源占用情況和運行狀態(tài)，當存在虛擬機或物理服務器滿足預先設定的整合條件時，調(diào)用SfGM來預測資源的使用情況，通過預測結(jié)果來判斷在當前時間點是否需要整合虛擬機。當需要執(zhí)行虛擬機整合時，利用本文提出的最重物理機最適合虛擬機優(yōu)先重定位(the Heaviest PM the mostSuitable VM First Relocation， HPSVFR)算法進行虛擬機的重定位。 仿真實驗表明，優(yōu)化的數(shù)據(jù)格式能夠有效減少傳輸?shù)男畔⒘�，�?jié)約網(wǎng)絡帶寬。SfGM的預測具有最高的準確性，最好情況下其預測值數(shù)學期望的相對誤差是GMM的46.8%，GM(1,1)的28.6%。HPSVFR算法與最先適應和最好適應算法相比較，重定位開銷最少，，僅為它們的70%左右。本文中的虛擬機整合架構能夠有效地判斷當前數(shù)據(jù)中心的資源使用情況，如果當前數(shù)據(jù)中心存在處于超載或者低負載狀態(tài)的物理服務器和虛擬機時，能夠有效的利用HPSVFR算法實現(xiàn)虛擬機的重定位。
[Abstract]:In order to improve the resource utilization and energy utilization of all physical servers in the data center, the virtual machine can be dynamically integrated.The main ways to realize the dynamic integration of virtual machines are the real-time migration of virtual machines between physical servers and the dynamic conversion of idle physical servers to low energy consumption mode.This paper focuses on the transmission format of monitoring data, resource utilization prediction and virtual machine repositioning under physical server overload and low load.First of all, the data transmission format of typical monitoring system is analyzed, and an optimized data transmission format is proposed according to the characteristics of monitoring information. This format unified the management of monitoring information.Secondly, by analyzing the characteristics of grey model and Markov theory, a self-feedback grey Markov prediction model, self-feedback Grey Markov model SfGMN, is proposed. The model takes N consecutive historical data from a resource of each virtual machine as input.By analyzing the internal relations of these N data, the utilization of this resource in the future is predicted.Finally, it dynamically monitors the resource occupation and running status of all virtual machines and physical servers in the data center. When there is a virtual machine or physical server that meets the pre-set integration conditions, SfGM is called to predict the use of resources.The prediction results are used to determine whether or not the virtual machine needs to be integrated at the current point in time.When it is necessary to perform virtual machine integration, the Heaviest PM the mostSuitable VM First repositioning (HPSVFR) algorithm proposed in this paper is used to relocate the virtual machine.The simulation results show that the optimized data format can effectively reduce the amount of information transmitted and save the network bandwidth. The prediction of SfGM has the highest accuracy.In the best case, the relative error of the mathematical expectation of its prediction value is 46.8% of GMM. Compared with the first adaptive algorithm and the best adaptive algorithm, the repositioning cost of the 28.6%.HPSVFR algorithm is the least, which is only about 70% of that of the first adaptive algorithm and the best adaptive algorithm.The virtual machine integration architecture in this paper can effectively judge the resource usage of the current data center, if the current data center has physical servers and virtual machines in an overloaded or low-load state,Can effectively use the HPSVFR algorithm to achieve virtual machine relocation.
【學位授予單位】：西北師范大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP302

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本文編號：1751428