本文選題：云計(jì)算 + Docker　； 參考：《北京郵電大學(xué)》2017年碩士論文

【摘要】：隨著視頻監(jiān)控設(shè)備的大規(guī)模部署,傳統(tǒng)的視頻監(jiān)控系統(tǒng)已無法對海量的視頻監(jiān)控?cái)?shù)據(jù)進(jìn)行有效的計(jì)算分析,伴隨云計(jì)算的發(fā)展,視頻監(jiān)控系統(tǒng)與云計(jì)算融合所形成的視頻監(jiān)控云成了近年來的研究熱點(diǎn)。當(dāng)前主流的視頻監(jiān)控云平臺(tái)基本都是以虛擬機(jī)的形式為用戶提供服務(wù),資源分配粒度大且存在性能損耗。目前云平臺(tái)中對資源配置基本是在初始階段采取靜態(tài)分配方式,然后在運(yùn)行期再根據(jù)監(jiān)控預(yù)警或負(fù)載預(yù)測的方法進(jìn)行動(dòng)態(tài)水平伸縮。然而監(jiān)控預(yù)警的方式?jīng)]有考慮資源需求的實(shí)時(shí)性,是一種被動(dòng)伸縮方式,容易違反服務(wù)等級協(xié)議SLA;負(fù)載預(yù)測方式雖然具有預(yù)動(dòng)性,但目前仍沒有一種預(yù)測方法能夠?qū)σ曨l監(jiān)控云平臺(tái)中的服務(wù)負(fù)載做出準(zhǔn)確的預(yù)測。如何提高視頻監(jiān)控云平臺(tái)的資源利用率,實(shí)現(xiàn)一個(gè)高效的可以動(dòng)態(tài)彈性伸縮的視頻監(jiān)控云平臺(tái)是本文的研究重點(diǎn)。首先,本文通過對典型視頻監(jiān)控服務(wù)的負(fù)載特性進(jìn)行分析,提出了適用于視頻監(jiān)控云平臺(tái)的資源預(yù)測模型,該模型的構(gòu)建可分為兩個(gè)階段,在第一階段可以根據(jù)視頻服務(wù)的屬性特征來預(yù)測其初始資源需求量,第二階段基于資源需求時(shí)間序列相似性對工作負(fù)載進(jìn)行預(yù)測。其次,針對視頻監(jiān)控云平臺(tái)中無法對資源及時(shí)準(zhǔn)確的進(jìn)行動(dòng)態(tài)調(diào)整這一問題,本文對現(xiàn)有基于容器技術(shù)的視頻監(jiān)控云平臺(tái)進(jìn)行了優(yōu)化,在云平臺(tái)中添加了資源預(yù)測模塊并調(diào)整了資源配置策略,使之可以根據(jù)預(yù)測結(jié)果通過熱遷移及垂直伸縮的方式對容器的資源進(jìn)行重配置,從而提高資源利用率。最后,本文實(shí)現(xiàn)了基于Docker的視頻監(jiān)控云計(jì)算平臺(tái)的優(yōu)化工作,對所提出的資源預(yù)測模型及調(diào)整后的資源配置策略進(jìn)行了性能測試,實(shí)驗(yàn)結(jié)果表明本文所提出的資源預(yù)測模型有更高的準(zhǔn)確率,所調(diào)整資源配置策略可以有效提高視頻監(jiān)控云平臺(tái)的資源利用率。
[Abstract]:With the large-scale deployment of video surveillance equipment, the traditional video surveillance system has been unable to calculate and analyze the mass of video surveillance data effectively, with the development of cloud computing. Video surveillance cloud formed by the fusion of video surveillance system and cloud computing has become a research hotspot in recent years. At present the mainstream video surveillance cloud platform is basically in the form of virtual machine to provide services to users resource allocation granularity and performance loss. At present, resource allocation in the cloud platform is based on static allocation in the initial stage, and then dynamically scalable in the runtime according to the method of monitoring, warning or load forecasting. However, the method of monitoring and warning does not take into account the real-time requirements of resources, it is a passive expansion mode, and it is easy to violate the service level protocol slaa. However, there is still no prediction method to accurately predict the service load in video surveillance cloud platform. How to improve the resource utilization of video surveillance cloud platform and realize an efficient and dynamic elastic video monitoring cloud platform is the focus of this paper. Firstly, by analyzing the load characteristics of typical video surveillance services, this paper proposes a resource prediction model for video surveillance cloud platform, which can be divided into two stages. In the first stage, the initial resource demand can be predicted based on the attribute characteristics of the video service. In the second stage, the workload is predicted based on the similarity of the time series of resource requirements. Secondly, aiming at the problem that the resources can not be adjusted dynamically and accurately in the video surveillance cloud platform, this paper optimizes the existing video surveillance cloud platform based on container technology. The resource prediction module is added to the cloud platform and the resource allocation strategy is adjusted so that it can reconfigure the container resources by heat transfer and vertical expansion according to the prediction results so as to improve the resource utilization ratio. Finally, this paper realizes the optimization of the video surveillance cloud computing platform based on Docker, and tests the performance of the proposed resource prediction model and the adjusted resource allocation strategy. The experimental results show that the proposed resource prediction model has higher accuracy and the resource allocation strategy can effectively improve the resource utilization of video surveillance cloud platform.
【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】：2017
【分類號】：TP393.09

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本文編號：2104914