本文關(guān)鍵詞： 內(nèi)容分發(fā)網(wǎng)絡(luò) 內(nèi)容流行度 副本替換 副本部署 云CDN　出處：《中國(guó)科學(xué)技術(shù)大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：在三網(wǎng)融合的大環(huán)境下,服務(wù)和業(yè)務(wù)的種類越來越豐富,其中視頻內(nèi)容的播放服務(wù)是三網(wǎng)融合的重要服務(wù)之一。隨著整個(gè)視頻服務(wù)行業(yè)的飛速發(fā)展,視頻內(nèi)容出現(xiàn)了爆炸式的增長(zhǎng)。 視頻內(nèi)容通過內(nèi)容分發(fā)網(wǎng)絡(luò),分發(fā)給電視、PC、手機(jī)、PAD等內(nèi)容終端。視頻內(nèi)容的快速增長(zhǎng)及終端用戶數(shù)量迅猛增加,與此同時(shí)基礎(chǔ)網(wǎng)絡(luò)傳輸帶寬發(fā)展相對(duì)緩慢,為了保證視頻服務(wù)質(zhì)量,目前主要通過由內(nèi)容分發(fā)和緩存系統(tǒng)組成的內(nèi)容分發(fā)網(wǎng)絡(luò)CDN (Content Delivery Network),完成視頻內(nèi)容的分發(fā)和緩存。大容量、高并發(fā)的視頻服務(wù),對(duì)視頻分發(fā)、緩存技術(shù)提出了更高要求。視頻內(nèi)容流行度的準(zhǔn)確預(yù)測(cè)、有效的緩存替換算法以及副本的預(yù)部署算法對(duì)CDN的性能有重要影響。 本文圍繞三網(wǎng)融合環(huán)境下的視頻內(nèi)容的分發(fā)與緩存關(guān)鍵技術(shù)展開研究工作,重點(diǎn)研究了CDN中視頻流行度的預(yù)測(cè),分布式緩存系統(tǒng)中移動(dòng)視頻內(nèi)容緩存的替換,以及基于云存儲(chǔ)的CDN中視頻內(nèi)容副本的預(yù)部署等策略。 本文的主要工作與成果如下： 1)針對(duì)基于“推”為主的CDN系統(tǒng)中,視頻內(nèi)容的流行度需要人為估計(jì)的問題,本文提出了一種利用貝葉斯網(wǎng)絡(luò)模型對(duì)視頻內(nèi)容的流行度進(jìn)行預(yù)測(cè)的方法。在保證用戶體驗(yàn)不變的前提下,減少后期系統(tǒng)對(duì)視頻內(nèi)容副本的調(diào)整,有效減少系統(tǒng)負(fù)擔(dān),使CDN邊緣節(jié)點(diǎn)的存儲(chǔ)資源能夠得到更合理的使用。 該方法利用貝葉斯網(wǎng)絡(luò)通過對(duì)視頻點(diǎn)播數(shù)據(jù)進(jìn)行知識(shí)挖掘,實(shí)現(xiàn)對(duì)需要分發(fā)到CDN邊緣緩存節(jié)點(diǎn)的視頻內(nèi)容流行度的有效預(yù)測(cè)。實(shí)驗(yàn)結(jié)果表明,該方法可對(duì)視頻內(nèi)容流行度較為準(zhǔn)確的預(yù)測(cè),電影流行度準(zhǔn)確率超過80%。 2)針對(duì)分布式協(xié)同緩存系統(tǒng)中的內(nèi)容替換問題,本文提出了一種基于全局信息的內(nèi)容替換算法。通過理論推導(dǎo)證明了協(xié)同緩存優(yōu)于獨(dú)立緩存。 協(xié)同緩存系統(tǒng)中現(xiàn)有的局部貪心算法假定文件長(zhǎng)度一致,僅考慮局部的內(nèi)容信息�？紤]這兩個(gè)不足,本文通過分析移動(dòng)環(huán)境下視頻的長(zhǎng)度分布與流行度分布情況,建立了視頻長(zhǎng)度與流行度的分布模型。然后根據(jù)分布模型并結(jié)合緩存系統(tǒng)的自身特點(diǎn),針對(duì)面向移動(dòng)視頻服務(wù)的分布式緩存系統(tǒng),本文提出了一種基于全局效用值的緩存替換算法,簡(jiǎn)稱VBG算法(Value-Based Global). VBG算法基于全局信息,同時(shí)考慮文件長(zhǎng)度。實(shí)驗(yàn)結(jié)果表明,協(xié)同緩存系統(tǒng)在三種不同緩存預(yù)部署策略下,使用VBG算法的系統(tǒng)傳輸代價(jià)均低于局部貪心算法,在三組實(shí)驗(yàn)中,局部貪心算法的傳輸開銷最高約是VBG算法的2.5倍。 3)面向基于云存儲(chǔ)的CDN視頻內(nèi)容預(yù)部署需求,本文提出了兩種離線的副本部署算法：GUCP算法(Greedy User Core Preallocation)和PBP算法(Popularity Based Placement). GUCP算法利用用戶請(qǐng)求信息,將副本有針對(duì)性的部署到云存儲(chǔ)節(jié)點(diǎn)上,將用戶快速的重定向到擁有所需副本內(nèi)容的節(jié)點(diǎn),從而解決原有GS (Greedy Site)算法可能引起的用戶負(fù)載不均問題。實(shí)驗(yàn)結(jié)果表明,GUCP算法在負(fù)載均衡方面的性能優(yōu)于GS算法很多。當(dāng)用戶數(shù)量達(dá)1000時(shí),GUCP算法的負(fù)載狀況值僅為GS算法的1/8。PBP算法則利用內(nèi)容的流行度計(jì)算出副本在系統(tǒng)中所要部署的數(shù)量,將用戶請(qǐng)求重定向到指定云存儲(chǔ)節(jié)點(diǎn),若該節(jié)點(diǎn)無法提供副本服務(wù),則從其它節(jié)點(diǎn)復(fù)制副本。對(duì)比其它節(jié)點(diǎn)復(fù)制副本的平均調(diào)整時(shí)延和調(diào)整費(fèi)用兩種性能指標(biāo),PBP算法比隨機(jī)預(yù)部署算法有較大優(yōu)勢(shì)。隨著用戶數(shù)量的增加,PBP算法的調(diào)整費(fèi)用近似趨于隨機(jī)算法的1/2,平均調(diào)整時(shí)延近似趨于隨機(jī)算法的3/4。 本文的研究工作及成果,依托于國(guó)家863重大項(xiàng)目“新一代高可信網(wǎng)絡(luò)”課題中“新一代業(yè)務(wù)運(yùn)行管控協(xié)同支撐環(huán)境的開發(fā)”課題以及華為“分布式分層Cache技術(shù)合作”基金項(xiàng)目,已經(jīng)應(yīng)用于本文所依托的兩個(gè)課題(已通過驗(yàn)收)。
[Abstract]:With the development of the whole video service industry , the video content has exploded with the rapid development of the whole video service industry . The content distribution network is distributed to the content terminals such as TV , PC , mobile phone , PAD , etc . The fast growth of video content and the number of end users are increasing rapidly . At the same time , the transmission bandwidth of the base network is relatively slow . In order to guarantee the video quality of service , the content distribution and caching are completed mainly through the content distribution network CDN ( Content Delivery Network ) composed of content distribution and caching system . In this paper , the key technologies of distribution and caching of video content in the three - network integration environment are studied . The prediction of video popularity in CDN , the replacement of mobile video content cache in distributed caching system and the pre - deployment of video content copies in CDN based on cloud storage are studied . The main work and results of this article are as follows : 1 ) In the CDN system based on " push " , the popularity of video content requires man - made estimation . This paper presents a method to predict the popularity of video content using Bayesian network model . Experiments show that the method can predict the popularity of video content accurately , and the accuracy rate of movie popularity is more than 80 % . 2 ) Aiming at the problem of content replacement in distributed cooperative caching system , a content replacement algorithm based on global information is presented in this paper . In this paper , the distribution model of video length and popularity is established by analyzing the distribution of the length and popularity of the video in the mobile environment . Then , according to the distribution model and the characteristics of the caching system , a cache replacement algorithm based on the global utility value is proposed , which is called Value - Based Global . Based on the global information , the VBG algorithm takes into account the file length . The experimental results show that the system transmission cost of the cooperative caching system is lower than that of the local greedy algorithm under three different cache pre - deployment strategies . In the three groups of experiments , the maximum transmission cost of the local greedy algorithm is about 2.5 times that of the VBG algorithm . 3 ) To meet the pre - deployment requirements of CDN video content based on cloud storage , two offline copy deployment algorithms : GUCP algorithm and Popularity Based Placement are proposed in this paper . The results show that the performance of GUCP algorithm is better than GS algorithm when the number of users is 1000 . The results show that the performance of GUCP algorithm is better than that of GS algorithm . When the number of users is 1000 , the load condition value of GUCP algorithm is only 1 / 8 of GS algorithm . When the number of users reaches 1000 , the PBP algorithm is more advantageous than random pre - deployment algorithm . The research work and results of this paper are based on the project of " Next Generation Business Operation Control and Cooperative Support Environment " in the " Next Generation High Trust Network " project of National 863 Major Projects and the project of Huawei " Distributed Tiered Cache Technical Cooperation " , which have been applied to the two topics supported by this paper ( accepted ) .

【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.02;TP333

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本文編號(hào)：1531503