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

【摘要】：云計(jì)算技術(shù)是新型的IT模式,從網(wǎng)格計(jì)算、并行計(jì)算與分布式計(jì)算發(fā)展而來(lái)。云計(jì)算是指用戶(hù)可以按照自己的計(jì)算需求,通過(guò)網(wǎng)絡(luò)以一種較為容易擴(kuò)展的方式來(lái)獲取所需的計(jì)算資源(如計(jì)算、存儲(chǔ)和網(wǎng)絡(luò)等)的服務(wù)。云計(jì)算通過(guò)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(dòng)態(tài)可伸縮的服務(wù)形式將資源提供給用戶(hù)。隨著云計(jì)算用戶(hù)的持續(xù)增加,云數(shù)據(jù)中心的規(guī)模也在不斷擴(kuò)大,如何對(duì)物理資源進(jìn)行分配,選擇合適的虛擬機(jī)資源調(diào)度方式來(lái)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(shí)間,成為如今云計(jì)算要改善和解決的主要問(wèn)題。本文立足于OpenStack這個(gè)開(kāi)源云平臺(tái),結(jié)合云計(jì)算的虛擬機(jī)調(diào)度算法,對(duì)云數(shù)據(jù)中心的資源調(diào)度問(wèn)題進(jìn)行深入的研究。主要工作內(nèi)容如下:(1)對(duì)云計(jì)算的基本概念和相關(guān)特性進(jìn)行闡述,分析了云計(jì)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(tái),對(duì)OpenStack平臺(tái)的組織結(jié)構(gòu)和邏輯架構(gòu)進(jìn)行詳細(xì)的闡述,主要對(duì)核心組件進(jìn)行了分析。(2)提出了基于粒子群算法的虛擬機(jī)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(shí)延的問(wèn)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(guò)建立云環(huán)境內(nèi)部時(shí)延模型,利用改進(jìn)的粒子群算法來(lái)降低應(yīng)用的時(shí)延,提高運(yùn)行效率。(3)基于OpenStack平臺(tái)的Nova組件,設(shè)計(jì)虛擬機(jī)調(diào)度模塊,基于WFPSO算法,對(duì)虛擬機(jī)調(diào)度模塊進(jìn)行系統(tǒng)架構(gòu)與功能模塊的設(shè)計(jì),并將其集成在OpenStack平臺(tái)上,并實(shí)現(xiàn)該平臺(tái)的部署。(4)將實(shí)現(xiàn)虛擬機(jī)調(diào)度模塊的OpenStack平臺(tái)進(jìn)行功能性測(cè)試,測(cè)試該調(diào)度模塊的可用性與正確性。同時(shí)對(duì)調(diào)度模塊的WFPSO算法進(jìn)行性能測(cè)試,采用CloudSim仿真平臺(tái)對(duì)該算法進(jìn)行大規(guī)模集群測(cè)試,驗(yàn)證其可以較大幅度的提升云環(huán)境中應(yīng)用的運(yùn)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP393.09

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