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  本文選題：云計(jì)算　切入點(diǎn)：虛擬機(jī)部署　出處：《天津工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在云數(shù)據(jù)中心虛擬機(jī)的部署策略已成為一個(gè)重要的研究課題,也是當(dāng)前研究領(lǐng)域的一個(gè)熱點(diǎn)和難點(diǎn)問題。在云數(shù)據(jù)中心設(shè)有大量擁有的計(jì)算資源量是各不相同的物理服務(wù)器,如果不對(duì)這些服務(wù)器和攜帶任務(wù)的虛擬機(jī)進(jìn)行分析直接將虛擬機(jī)隨機(jī)部署到服務(wù)器上會(huì)導(dǎo)致云計(jì)算系統(tǒng)的負(fù)載平衡性能差、虛擬機(jī)執(zhí)行效率低,而且會(huì)大量地浪費(fèi)物理資源。本文首次提出了一種基于貝葉斯分類并結(jié)合首次自適應(yīng)的思想進(jìn)行虛擬機(jī)部署的策略。該策略算法通過對(duì)虛擬機(jī)需求以及物理服務(wù)器的剩余資源進(jìn)行分析,利用貝葉斯公式及標(biāo)準(zhǔn)化的閾值找出能達(dá)到長(zhǎng)期負(fù)載平衡適合部署虛擬機(jī)的物理服務(wù)器,將虛擬機(jī)的需求量進(jìn)行降序排序后以首次適應(yīng)算法部署到物理服務(wù)器上不僅降低了虛擬機(jī)部署的失敗次數(shù),也減少啟動(dòng)了不必要開啟的物理服務(wù)器從而降低了整個(gè)系統(tǒng)的能耗。仿真實(shí)驗(yàn)結(jié)果表明,新的任務(wù)部署策略不僅更好地實(shí)現(xiàn)了長(zhǎng)期負(fù)載平衡,縮短了任務(wù)的完成時(shí)間,而且在降低能耗和任務(wù)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(jī)進(jìn)行進(jìn)一步地分類工作后,以先來先服務(wù)的方式進(jìn)行虛擬機(jī)的部署。首先通過對(duì)虛擬機(jī)需求以及物理服務(wù)器的剩余資源進(jìn)行分析,利用貝葉斯公式篩選出具有高效性能的服務(wù)器集群,再將這些服務(wù)器集群作為統(tǒng)計(jì)樸素貝葉斯所需概率的資源池,當(dāng)后續(xù)再對(duì)虛擬機(jī)進(jìn)行分配時(shí),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(jī)的服務(wù)器集,將虛擬機(jī)以先來先服務(wù)的算法部署到分類后的服務(wù)器上,既可以達(dá)到負(fù)載平衡的效果也能使得服務(wù)器集里的服務(wù)器負(fù)載均衡,同時(shí)也提高了整個(gè)系統(tǒng)的執(zhí)行效率。仿真實(shí)驗(yàn)結(jié)果表明,該任務(wù)部署策略不僅很好地實(shí)現(xiàn)了系統(tǒng)的長(zhǎng)期負(fù)載平衡,縮短了任務(wù)的完成時(shí)間,而且在降低任務(wù).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(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP302

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本文編號(hào)：1628863