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  本文選題：云計算　切入點：虛擬機部署　出處：《天津工業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：在云數(shù)據(jù)中心虛擬機的部署策略已成為一個重要的研究課題,也是當前研究領域的一個熱點和難點問題。在云數(shù)據(jù)中心設有大量擁有的計算資源量是各不相同的物理服務器,如果不對這些服務器和攜帶任務的虛擬機進行分析直接將虛擬機隨機部署到服務器上會導致云計算系統(tǒng)的負載平衡性能差、虛擬機執(zhí)行效率低,而且會大量地浪費物理資源。本文首次提出了一種基于貝葉斯分類并結合首次自適應的思想進行虛擬機部署的策略。該策略算法通過對虛擬機需求以及物理服務器的剩余資源進行分析,利用貝葉斯公式及標準化的閾值找出能達到長期負載平衡適合部署虛擬機的物理服務器,將虛擬機的需求量進行降序排序后以首次適應算法部署到物理服務器上不僅降低了虛擬機部署的失敗次數(shù),也減少啟動了不必要開啟的物理服務器從而降低了整個系統(tǒng)的能耗。仿真實驗結果表明,新的任務部署策略不僅更好地實現(xiàn)了長期負載平衡,縮短了任務的完成時間,而且在降低能耗和任務部署失敗率方面都有明顯的效果。本文首次提出了利用樸素貝葉斯公式將虛擬機進行進一步地分類工作后,以先來先服務的方式進行虛擬機的部署。首先通過對虛擬機需求以及物理服務器的剩余資源進行分析,利用貝葉斯公式篩選出具有高效性能的服務器集群,再將這些服務器集群作為統(tǒng)計樸素貝葉斯所需概率的資源池,當后續(xù)再對虛擬機進行分配時,則根據(jù)樸素貝葉斯的分類策略就可以快速的找出可部署虛擬機的服務器集,將虛擬機以先來先服務的算法部署到分類后的服務器上,既可以達到負載平衡的效果也能使得服務器集里的服務器負載均衡,同時也提高了整個系統(tǒng)的執(zhí)行效率。仿真實驗結果表明,該任務部署策略不僅很好地實現(xiàn)了系統(tǒng)的長期負載平衡,縮短了任務的完成時間,而且在降低任務.部署失敗率方面也有明顯的效果。
[Abstract]:The deployment strategy of virtual machine in cloud data center has become an important research topic, and it is also a hot and difficult problem in current research field. If you don't analyze these servers and the virtual machines that carry tasks, deploying the virtual machines directly to the server at random will lead to poor load balancing performance of cloud computing systems, and the execution efficiency of virtual machines will be low. This paper presents a strategy of virtual machine deployment based on Bayesian classification and first adaptive thinking. The strategy algorithm is based on the requirements of virtual machines and physical services. To analyze the remaining resources of the. Using Bayesian formulas and standardized thresholds to find physical servers that can achieve long-term load balancing suitable for deploying virtual machines, Ordering the requirements of virtual machines in descending order and deploying the first adaptive algorithm to the physical server not only reduces the number of virtual machine deployment failures, The simulation results show that the new task deployment strategy not only realizes the long-term load balance better, but also shortens the completion time of the task. Moreover, it has obvious effect in reducing energy consumption and mission deployment failure rate. In this paper, we first put forward using naive Bayes formula to classify virtual machines further. First of all, by analyzing the requirement of virtual machine and the remaining resources of physical server, the server cluster with high efficiency is selected by using Bayesian formula. Then these server clusters are used as resource pools to calculate the probability of naive Bayes. When the virtual machines are allocated later, the server set of deployable virtual machines can be quickly found according to the classification strategy of naive Bayes. Deploying the virtual machine as a first-come, first-served algorithm to the classified servers can not only achieve the effect of load balancing, but also make the server load balance in the server set. At the same time, the efficiency of the whole system is improved. The simulation results show that the task deployment strategy not only realizes the long-term load balance of the system, but also shortens the completion time of the task. It also has a significant effect on reducing mission-deployment failure rates.
【學位授予單位】：天津工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP302

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本文編號：1628863