本文選題：云平臺 + 可維護(hù)性��； 參考：《浙江大學(xué)》2015年碩士論文

【摘要】：因云計(jì)算的興盛與普及,越來越多的企業(yè)選擇引入云計(jì)算技術(shù)改造原有的企業(yè)數(shù)據(jù)中心,一些企業(yè)則開始租賃大廠商的云服務(wù)。學(xué)術(shù)界和業(yè)界研究人員投入了相當(dāng)多的精力,專注于提高云平臺的安全性和資源利用率,但對于云平臺的可維護(hù)性,卻很少有人涉及。本文通過搭建DartCloud云平臺的實(shí)踐和思考,研究了云平臺的可維護(hù)性,主要包括：云平臺的快速部署及監(jiān)控系統(tǒng)的搭建、鏡像管理策略的定制和對業(yè)務(wù)擴(kuò)展性的支持。本文工作得到了863項(xiàng)目“互聯(lián)網(wǎng)語言翻譯系統(tǒng)研制”的資助和支持。本文工作和貢獻(xiàn)如下：●對云平臺的可維護(hù)性做了全面闡述,并在云平臺的快速部署及監(jiān)控系統(tǒng)的搭建、鏡像管理策略的定制和對業(yè)務(wù)擴(kuò)展性的支持這三方面做了深入的探討�！穹治隽薕penNebula和Xen的實(shí)現(xiàn)原理,給出了從物理裸機(jī)到一個(gè)云平臺的搭建指南,針對現(xiàn)有部署效率低下的問題,提出并實(shí)現(xiàn)了批量和自動(dòng)化部署策略,加速平臺搭建和部署,提高了云平臺擴(kuò)展性和部署效率�！駥�(shí)現(xiàn)了基于Ganglia的云資源監(jiān)控管理系統(tǒng),提供各虛擬機(jī)、各物理服務(wù)器的CPU利用率等指標(biāo),為性能調(diào)優(yōu)提供基本依據(jù)。●提出并實(shí)現(xiàn)了一個(gè)完整的鏡像管理解決方案,其中主要包括分級鏡像權(quán)限控制、鏡像版本管理和基于增量復(fù)制策略的鏡像自動(dòng)化管理三部分�！裢ㄟ^實(shí)現(xiàn)的DartCloud資源監(jiān)控系統(tǒng)采集實(shí)驗(yàn)數(shù)據(jù),深入分析了云平臺中scale-up和scale-out兩種資源擴(kuò)展方法在大數(shù)據(jù)并行計(jì)算應(yīng)用上的性能差異,為綜合提高資源利用率和減少成本的性能調(diào)優(yōu)管理提供了依據(jù)。
[Abstract]:Because of the prosperity and popularity of cloud computing, more and more enterprises choose to introduce cloud computing technology to transform the original enterprise data center, and some enterprises begin to lease cloud services from large manufacturers. Academic and industry researchers have devoted considerable attention to improving the security and resource utilization of cloud platforms, but few people have been involved in the maintainability of cloud platforms. Through the practice and thinking of constructing DartCloud cloud platform, this paper studies the maintainability of cloud platform, including the rapid deployment of cloud platform and the construction of monitoring system, the customization of mirror image management strategy and the support of business expansibility. This work is supported by 863 project, Internet language Translation system Development. The work and contributions of this paper are as follows: the maintainability of cloud platform is expounded comprehensively, and the rapid deployment of cloud platform and the construction of monitoring system are introduced. The customization of mirror management policy and the support for business extensibility are discussed in depth. The implementation principle of OpenNebula and Xen is analyzed, and the construction guide from physical bare machine to a cloud platform is given. Aiming at the problem of low deployment efficiency, a batch and automatic deployment strategy is proposed and implemented, which accelerates platform construction and deployment, improves the scalability and deployment efficiency of cloud platform, and implements cloud resource monitoring and management system based on ganglia. The CPU utilization ratio of each virtual machine and physical server is provided to provide the basic basis for performance tuning. A complete mirror management solution is proposed and implemented, which mainly includes hierarchical mirror permission control. Image version management and image automation management based on incremental replication strategy. Through the implementation of DartCloud resource monitoring system to collect experimental data, In this paper, the performance differences between scale-up and scale-out in big data parallel computing are analyzed, which provides a basis for improving resource utilization and reducing cost.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP393.09

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