本文選題：云計算 + 虛擬機放置��； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：云計算技術(shù)是新型的IT模式,從網(wǎng)格計算、并行計算與分布式計算發(fā)展而來。云計算是指用戶可以按照自己的計算需求,通過網(wǎng)絡(luò)以一種較為容易擴展的方式來獲取所需的計算資源(如計算、存儲和網(wǎng)絡(luò)等)的服務(wù)。云計算通過虛擬化技術(shù)將底層物理硬件虛擬化,形成巨大的虛擬資源池,然后以動態(tài)可伸縮的服務(wù)形式將資源提供給用戶。隨著云計算用戶的持續(xù)增加,云數(shù)據(jù)中心的規(guī)模也在不斷擴大,如何對物理資源進行分配,選擇合適的虛擬機資源調(diào)度方式來提高云數(shù)據(jù)中心性能、提高云應(yīng)用的性能和降低云應(yīng)用處理時間,成為如今云計算要改善和解決的主要問題。本文立足于OpenStack這個開源云平臺,結(jié)合云計算的虛擬機調(diào)度算法,對云數(shù)據(jù)中心的資源調(diào)度問題進行深入的研究。主要工作內(nèi)容如下:(1)對云計算的基本概念和相關(guān)特性進行闡述,分析了云計算的三種應(yīng)用框架。闡述了虛擬化的相關(guān)概念和主流的虛擬化技術(shù)。分析OpenStack云平臺,對OpenStack平臺的組織結(jié)構(gòu)和邏輯架構(gòu)進行詳細的闡述,主要對核心組件進行了分析。(2)提出了基于粒子群算法的虛擬機放置策略,該策略為解決應(yīng)用基于復(fù)雜網(wǎng)絡(luò)具有較高通信時延的問題,該策略考慮了CPU、內(nèi)存、帶寬和網(wǎng)絡(luò)通信量四種因素,通過建立云環(huán)境內(nèi)部時延模型,利用改進的粒子群算法來降低應(yīng)用的時延,提高運行效率。(3)基于OpenStack平臺的Nova組件,設(shè)計虛擬機調(diào)度模塊,基于WFPSO算法,對虛擬機調(diào)度模塊進行系統(tǒng)架構(gòu)與功能模塊的設(shè)計,并將其集成在OpenStack平臺上,并實現(xiàn)該平臺的部署。(4)將實現(xiàn)虛擬機調(diào)度模塊的OpenStack平臺進行功能性測試,測試該調(diào)度模塊的可用性與正確性。同時對調(diào)度模塊的WFPSO算法進行性能測試,采用CloudSim仿真平臺對該算法進行大規(guī)模集群測試,驗證其可以較大幅度的提升云環(huán)境中應(yīng)用的運行效率。
[Abstract]:Cloud computing technology is a new IT model developed from grid computing, parallel computing and distributed computing. Cloud computing refers to the services that users can obtain computing resources (such as computing, storage, network, etc.) through the network in a way that is easy to expand according to their own computing requirements. Cloud computing virtualizes the underlying physical hardware through virtualization to form a huge pool of virtual resources and then provides resources to users in the form of dynamically scalable services. With the continuous increase of cloud computing users, the scale of cloud data center is also expanding. How to allocate physical resources, select the appropriate virtual machine resource scheduling method to improve the performance of cloud data center, Improving the performance of cloud applications and reducing the processing time of cloud applications have become the main problems that cloud computing needs to improve and solve. Based on the open source cloud platform OpenStack and the virtual machine scheduling algorithm of cloud computing, the resource scheduling problem of cloud data center is deeply studied in this paper. The main work is as follows: (1) the basic concepts and related features of cloud computing are expounded, and three application frameworks of cloud computing are analyzed. This paper expounds the related concepts of virtualization and the mainstream virtualization technology. The OpenStack cloud platform is analyzed, the organization structure and logic structure of OpenStack platform are described in detail, and the kernel components are analyzed. (2) the strategy of virtual machine placement based on particle swarm optimization is put forward. In order to solve the problem of high communication delay based on complex network, this strategy considers four factors: CPU, memory, bandwidth and network traffic, and establishes the internal delay model of cloud environment. The improved particle swarm optimization algorithm is used to reduce the application delay and improve the running efficiency. The Nova component based on OpenStack platform is designed and the virtual machine scheduling module is designed. Based on the WFPSO algorithm, the system architecture and function module are designed for the virtual machine scheduling module. It is integrated on the OpenStack platform, and the deployment of the platform. 4) the virtual machine scheduling module will be implemented on the OpenStack platform for functional testing, testing the availability and correctness of the scheduling module. At the same time, the performance of the WFPSO algorithm of the scheduling module is tested, and the large scale cluster test of the algorithm is carried out on the CloudSim simulation platform, which verifies that it can greatly improve the efficiency of the application in the cloud environment.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.09

【參考文獻】

相關(guān)期刊論文 前10條

1 陳興蜀;楊露;羅永剛;葛龍;;國內(nèi)外云計算安全標(biāo)準(zhǔn)研究[J];信息安全研究;2016年05期

2 楊田貴;;云計算及其應(yīng)用綜述[J];軟件導(dǎo)刊;2016年03期

3 張玉清;王曉菲;劉雪峰;劉玲;;云計算環(huán)境安全綜述[J];軟件學(xué)報;2016年06期

4 董健康;王洪波;李陽陽;程時端;;IaaS環(huán)境下改進能源效率和網(wǎng)絡(luò)性能的虛擬機放置方法[J];通信學(xué)報;2014年01期

5 楊紹光;張云勇;陳清金;潘松柏;;基于OpenStack的云計算laaS管理平臺研究[J];互聯(lián)網(wǎng)天地;2013年03期

6 陳涵;孫克強;丁敬雯;;云計算產(chǎn)業(yè)的形成及發(fā)展對策[J];江蘇紡織;2012年03期

7 李強;郝沁汾;肖利民;李舟軍;;云計算中虛擬機放置的自適應(yīng)管理與多目標(biāo)優(yōu)化[J];計算機學(xué)報;2011年12期

8 龔強;;網(wǎng)格計算商業(yè)演化的云計算與應(yīng)用展望[J];信息技術(shù);2011年10期

9 劉萬軍;張孟華;郭文越;;基于MPSO算法的云計算資源調(diào)度策略[J];計算機工程;2011年11期

10 李喬;鄭嘯;;云計算研究現(xiàn)狀綜述[J];計算機科學(xué);2011年04期

，

本文編號：1898861