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  本文關鍵詞： 云計算 負載均衡 負載預測 任務調度　出處：《北京郵電大學》2014年碩士論文　論文類型：學位論文

【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)進入了數(shù)據(jù)量急劇膨脹的時代。云計算技術的出現(xiàn)大大緩解了數(shù)據(jù)的壓力。云計算的一系列優(yōu)勢,如海量計算能力、廉價、按需使用等,為云計算帶來了廣闊的發(fā)展空間。但與此同時產(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)點負載失衡。針對這個問題,本文提出了一個具有負載均衡和動態(tài)擴展特性的資源調度框架。 資源調度框架由4個組件組成：歷史數(shù)據(jù)倉庫、負載均衡器、擴展決策器和資源分配管理器。各個組件互相協(xié)作,最終在云環(huán)境下實現(xiàn)負載均衡、動態(tài)擴展的功能。其中,負載均衡器使用基于能力匹配的負載均衡策略,通過統(tǒng)計學趨勢預測算法進行業(yè)務量預測,并以任務請求與虛擬機能力相匹配為原則將請求分配到合適的節(jié)點,從而實現(xiàn)負載均衡的特性。 本文重點介紹了基于能力匹配的負載均衡策略的實現(xiàn)。該策略的實現(xiàn)需要負載預測、負載監(jiān)控和任務調度三個組件。負載預測組件采用統(tǒng)計學中的趨勢預測算法,根據(jù)歷史數(shù)據(jù)對未來負載量進行預測,能夠得到相對準確的預測結果；負載監(jiān)控組件使用開源munin組件和collect組件實現(xiàn)對物理機和虛擬機資源的監(jiān)控；對于任務調度組件,本文設計了能力匹配算法,將任務請求所需的計算資源與虛擬機的計算能力進行匹配,從而將任務請求分發(fā)到合適的虛擬機進行處理,充分利用計算資源,同時保證服務質量。 最后,本文對該業(yè)務云平臺資源調度框架進行了實驗和測試,并將基于能力匹配的負載均衡策略與傳統(tǒng)的負載均衡策略進行對比。實驗結果表明,該框架能夠在動態(tài)擴展的基礎上實現(xiàn)負載均衡,基于能力匹配的負載均衡策略能夠比傳統(tǒng)的負載均衡策略更好地適應負載的動態(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.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09

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