發(fā)布時(shí)間：2018-02-14 02:25

  本文關(guān)鍵詞： 云計(jì)算 負(fù)載均衡 負(fù)載預(yù)測 任務(wù)調(diào)度　出處：《北京郵電大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)的普及與發(fā)展,數(shù)據(jù)量不斷增大,我們已經(jīng)進(jìn)入了數(shù)據(jù)量急劇膨脹的時(shí)代。云計(jì)算技術(shù)的出現(xiàn)大大緩解了數(shù)據(jù)的壓力。云計(jì)算的一系列優(yōu)勢,如海量計(jì)算能力、廉價(jià)、按需使用等,為云計(jì)算帶來了廣闊的發(fā)展空間。但與此同時(shí)產(chǎn)生的大量的虛擬資源卻變得難以管理和控制,用戶對虛擬資源選擇的不確定性非常容易造成資源節(jié)點(diǎn)負(fù)載失衡。針對這個(gè)問題,本文提出了一個(gè)具有負(fù)載均衡和動(dòng)態(tài)擴(kuò)展特性的資源調(diào)度框架。 資源調(diào)度框架由4個(gè)組件組成：歷史數(shù)據(jù)倉庫、負(fù)載均衡器、擴(kuò)展決策器和資源分配管理器。各個(gè)組件互相協(xié)作,最終在云環(huán)境下實(shí)現(xiàn)負(fù)載均衡、動(dòng)態(tài)擴(kuò)展的功能。其中,負(fù)載均衡器使用基于能力匹配的負(fù)載均衡策略,通過統(tǒng)計(jì)學(xué)趨勢預(yù)測算法進(jìn)行業(yè)務(wù)量預(yù)測,并以任務(wù)請求與虛擬機(jī)能力相匹配為原則將請求分配到合適的節(jié)點(diǎn),從而實(shí)現(xiàn)負(fù)載均衡的特性。 本文重點(diǎn)介紹了基于能力匹配的負(fù)載均衡策略的實(shí)現(xiàn)。該策略的實(shí)現(xiàn)需要負(fù)載預(yù)測、負(fù)載監(jiān)控和任務(wù)調(diào)度三個(gè)組件。負(fù)載預(yù)測組件采用統(tǒng)計(jì)學(xué)中的趨勢預(yù)測算法,根據(jù)歷史數(shù)據(jù)對未來負(fù)載量進(jìn)行預(yù)測,能夠得到相對準(zhǔn)確的預(yù)測結(jié)果；負(fù)載監(jiān)控組件使用開源munin組件和collect組件實(shí)現(xiàn)對物理機(jī)和虛擬機(jī)資源的監(jiān)控；對于任務(wù)調(diào)度組件,本文設(shè)計(jì)了能力匹配算法,將任務(wù)請求所需的計(jì)算資源與虛擬機(jī)的計(jì)算能力進(jìn)行匹配,從而將任務(wù)請求分發(fā)到合適的虛擬機(jī)進(jìn)行處理,充分利用計(jì)算資源,同時(shí)保證服務(wù)質(zhì)量。 最后,本文對該業(yè)務(wù)云平臺資源調(diào)度框架進(jìn)行了實(shí)驗(yàn)和測試,并將基于能力匹配的負(fù)載均衡策略與傳統(tǒng)的負(fù)載均衡策略進(jìn)行對比。實(shí)驗(yàn)結(jié)果表明,該框架能夠在動(dòng)態(tài)擴(kuò)展的基礎(chǔ)上實(shí)現(xiàn)負(fù)載均衡,基于能力匹配的負(fù)載均衡策略能夠比傳統(tǒng)的負(fù)載均衡策略更好地適應(yīng)負(fù)載的動(dòng)態(tài)變化,更合理地利用云中資源。
[Abstract]:With the popularization and development of the Internet, the amount of data is increasing, and we have entered the era of rapid expansion of data. The emergence of cloud computing technology has greatly alleviated the pressure of data, cloud computing has a series of advantages, such as the capacity of mass computing. Cheap, on-demand and so on, bring the cloud computing a broad space for development. But at the same time, a large number of virtual resources have become difficult to manage and control. The uncertainty of users' choice of virtual resources is very easy to cause resource node load imbalance. In order to solve this problem, a resource scheduling framework with load balancing and dynamic expansion is proposed in this paper. The resource scheduling framework consists of four components: historical data warehouse, load balancer, extended decision maker and resource allocation manager. The load balancer uses load balancing strategy based on capacity matching to predict traffic through statistical trend prediction algorithm and assigns the request to the appropriate node based on the matching of task request and virtual machine capability. Thus, the characteristic of load balancing is realized. This paper focuses on the implementation of load balancing strategy based on capability matching, which requires three components: load forecasting, load monitoring and task scheduling. According to the historical data to predict the future load, can get a relatively accurate prediction results; load monitoring components using open source munin components and collect components to monitor the physical machine and virtual machine resources; for task scheduling components, In this paper, a capacity matching algorithm is designed to match the computing resources required by the task request and the computing power of the virtual machine, so that the task request can be distributed to the appropriate virtual machine for processing, making full use of the computing resources and ensuring the quality of service at the same time. Finally, this paper tests and tests the resource scheduling framework of the service cloud platform, and compares the load balancing strategy based on capacity matching with the traditional load balancing strategy. The experimental results show that, This framework can realize load balancing on the basis of dynamic expansion. The load balancing strategy based on capacity matching can adapt to the dynamic change of load better than the traditional load balancing strategy and make more rational use of resources in the cloud.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 馬秀芳;李紅巖;;計(jì)算機(jī)虛擬化技術(shù)淺析[J];電腦知識與技術(shù);2010年33期

2 姜毅;王偉軍;曹麗;劉凱;陳桂強(qiáng);;基于開源軟件的私有云計(jì)算平臺構(gòu)建[J];電信科學(xué);2013年01期

3 安暉;;我國云計(jì)算產(chǎn)業(yè)實(shí)際狀況與或然性趨勢[J];重慶社會科學(xué);2012年05期

4 華夏渝;鄭駿;胡文心;;基于云計(jì)算環(huán)境的蟻群優(yōu)化計(jì)算資源分配算法[J];華東師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年01期

5 胡志剛;歐陽晟;閻朝坤;;云環(huán)境下面向能耗降低的資源負(fù)載均衡方法[J];計(jì)算機(jī)工程;2012年05期

6 劉鵬立;;負(fù)載均衡技術(shù)的分析與應(yīng)用[J];山西建筑;2007年04期

7 許艷軍;姜進(jìn)磊;王博;楊廣文;;幾種虛擬機(jī)鏡像格式及其性能測評[J];計(jì)算機(jī)應(yīng)用;2013年S1期

8 修長虹;趙云飛;宋繼俠;;基于Linux PC集群負(fù)載均衡的研究與實(shí)現(xiàn)[J];沈陽師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2006年02期

9 吳韶鴻;;通信和互聯(lián)網(wǎng)領(lǐng)域創(chuàng)新態(tài)勢可喜企業(yè)核心技術(shù)研發(fā)能力有待提升[J];世界電信;2013年Z1期

10 ChrisPreimesberger;范平;;開源云平臺5大優(yōu)劣勢分析[J];通訊世界;2012年08期

，

本文編號：1509661