【摘要】：視頻直播及點(diǎn)播、視頻會(huì)議、軟件下載、游戲更新等實(shí)時(shí)和非實(shí)時(shí)文件分發(fā)服務(wù)已經(jīng)成為互聯(lián)網(wǎng)流量的主要來(lái)源,隨著高清視頻等大容量、高清晰度視頻內(nèi)容的日益豐富,用戶對(duì)實(shí)時(shí)和非實(shí)時(shí)文件分發(fā)服務(wù)的體驗(yàn)質(zhì)量要求越來(lái)越高。如何優(yōu)化文件分發(fā)系統(tǒng)使其更好地滿足用戶體驗(yàn)質(zhì)量要求,是吸引學(xué)術(shù)界和工業(yè)界共同興趣的熱點(diǎn)問(wèn)題。本文基于大規(guī)模實(shí)際運(yùn)營(yíng)的文件分發(fā)系統(tǒng)(PPTV,騰訊旋風(fēng)下載平臺(tái)),對(duì)無(wú)線信道下視頻播放、用戶需求、緩存配置以及云帶寬資源部署等四個(gè)方面進(jìn)行測(cè)量分析和理論研究,發(fā)現(xiàn)現(xiàn)有的資源分發(fā)、配置策略在云端帶寬分配方面未充分考慮云帶寬對(duì)Swarm (即擁有或需要相同資源的用戶群體)的影響,用戶的個(gè)人需求預(yù)測(cè)尚未得到有效解決,Flash Crowd發(fā)生時(shí)云端資源消耗過(guò)高,無(wú)線信道中用戶視頻播放卡頓現(xiàn)象顯著。為此本文分別從用戶端優(yōu)化設(shè)計(jì)、用戶個(gè)人需求預(yù)測(cè)、緩存配置優(yōu)化以及云端帶寬分配這幾個(gè)角度探索改善用戶體驗(yàn)、節(jié)約系統(tǒng)資源的有效方法,并通過(guò)理論分析及實(shí)驗(yàn)仿真驗(yàn)證這些方案的有效性。本文主要工作和創(chuàng)新點(diǎn)如下：1)用戶端體驗(yàn)優(yōu)化：實(shí)際測(cè)量表明,現(xiàn)有的自適應(yīng)動(dòng)態(tài)碼率實(shí)時(shí)視頻方案,在無(wú)線信道條件下卡頓現(xiàn)象顯著。目前的研究利用歷史知識(shí)和當(dāng)前信道條件調(diào)整視頻碼率切換策略,導(dǎo)致碼率頻繁切換。本文利用無(wú)線信道模型推斷信道未來(lái)的變化,設(shè)計(jì)了基于非確定狀態(tài)決策模型的碼率切換算法,能夠避免頻繁的碼率切換,使用戶獲得最優(yōu)的視頻播放體驗(yàn),并給出了接近最優(yōu)算法性能的啟發(fā)式方案,最后通過(guò)仿真實(shí)驗(yàn)證實(shí)了算法的有效性。2)用戶需求和總體流行度預(yù)測(cè)：基于推薦的方法可以精確預(yù)測(cè)用戶未來(lái)需要的資源,但預(yù)測(cè)結(jié)果沒(méi)有時(shí)效性。在電視連續(xù)劇發(fā)布場(chǎng)景下,依賴劇集間的相關(guān)性以及用戶的看劇模式并使用機(jī)器學(xué)習(xí)方法,預(yù)測(cè)用戶未來(lái)一天需要觀看的劇集,該方法具有時(shí)效性,能夠用于資源部署方案設(shè)計(jì)。之后針對(duì)不同用戶類型分別設(shè)計(jì)了相應(yīng)的總體需求量預(yù)測(cè)方法,結(jié)果相比ARIMA算法精度提高12%。3)緩存、用戶資源利用：熱門新文件發(fā)布時(shí),大量用戶請(qǐng)求文件使云端負(fù)荷過(guò)高。在文件發(fā)布前采用預(yù)分發(fā)策略提前部署文件給用戶,就可以在文件發(fā)布后有效利用P2P來(lái)緩解云端壓力。傳統(tǒng)預(yù)分發(fā)方案只是依據(jù)用戶歷史在線行為以及客戶端性能挑選用戶,幫助文件擴(kuò)散,而不考慮用戶是否需要云端提供的文件。在電視連續(xù)劇發(fā)布場(chǎng)景中,大量用戶看一兩集之后可能棄劇,將文件部署給不需要該文件的用戶會(huì)浪費(fèi)寶貴的云端資源。本文基于用戶需求預(yù)測(cè),協(xié)同調(diào)度緩存資源和用戶資源,設(shè)計(jì)了最小化云端負(fù)載的前攝式緩存算法,在本文的仿真實(shí)驗(yàn)中發(fā)現(xiàn)它能節(jié)約40%的云端流量消耗。4)用戶群體(Swarm)間的云端帶寬分配：云與P2P協(xié)作的系統(tǒng)內(nèi),P2P貢獻(xiàn)能力不穩(wěn)定且不同Swarm的P2P貢獻(xiàn)能力不盡相同,使用云端帶寬作為補(bǔ)充可以保障用戶的體驗(yàn)�，F(xiàn)有的帶寬分配算法主要集中于直播場(chǎng)景或P2P帶寬的分配,不涉及云端帶寬資源分配對(duì)Swarm內(nèi)用戶下載生命周期以及P2P共享能力的影響。在文章中,基于流模型探討了上述兩個(gè)問(wèn)題,得到了云端帶寬與用戶下載速率的關(guān)系,并在云端帶寬資源受限的前提下提出了Swarm間的帶寬分配算法,優(yōu)化系統(tǒng)內(nèi)用戶的體驗(yàn)。
[Abstract]:Real-time and non-real-time file distribution services such as video live broadcast and on-demand, video conference, software download and game update have become the main source of internet traffic, and with the high-capacity and high-definition video content of high-definition video, The user experience quality requirements for real-time and non-real-time file distribution services are becoming more and more high. How to optimize the document distribution system makes it better to meet the user's experience quality requirements, and is a hot issue to attract the common interest of the academia and industry. Based on the large-scale practical operation of the file distribution system (PTV, Tencent's cyclone download platform), this paper carries out the measurement and analysis and the theoretical research on the video playing, the user's demand, the cache configuration and the cloud bandwidth resource deployment in the wireless channel, and finds out the existing resources distribution, The configuration policy does not take full account of the influence of the cloud bandwidth on the Swarm (that is, the user group with or needs the same resource) in the cloud bandwidth allocation, and the personal demand forecast of the user has not been effectively solved, and the cloud resource consumption is high in the event of the occurrence of the Flash Crown. the phenomenon of the user video playing card in the wireless channel is significant. In this paper, the effective methods of improving the user experience and saving system resources are explored from the aspects of the client-side optimization design, the user's personal demand forecast, the cache configuration optimization and the cloud bandwidth allocation, and the effectiveness of these schemes is verified through the theoretical analysis and the experimental simulation. The main work and innovation point of this paper are as follows: 1) Client experience optimization: The actual measurement shows that the existing self-adaptive dynamic code rate real-time video scheme is significant under the condition of wireless channel. Current research uses historical knowledge and current channel condition to adjust video rate switching strategy, resulting in frequent switching of code rate. In this paper, the wireless channel model is used to infer the future change of the channel, the rate switching algorithm based on the non-deterministic state decision model is designed, the frequent code rate switching can be avoided, the optimal video playing experience can be obtained by the user, and a heuristic scheme is provided which is close to the optimal algorithm performance, Finally, the validity of the algorithm is confirmed by the simulation experiment. 2) The user's demand and the overall popularity prediction: Based on the recommended method, the resource of the user's future needs can be accurately predicted, but the prediction result is not time-effective. in that case of a television series issue scenario, the dependency on the series and the user's watch play mode are depend on and the machine learning method is used to predict the show that the user needs to watch in the next day, and the method has the timeliness and can be used for resource deployment design. The corresponding overall demand forecasting method is designed for different user types. The result is that the accuracy of ARIMA algorithm is improved by 12%. 3) The cache and user's resource utilization: When the hot new file is released, the large number of user request files make the cloud load too high. The pre-distribution policy is used to pre-deploy the file to the user before the file is released, so that the cloud pressure can be effectively relieved by using the P2P after the file is released. The traditional pre-distribution scheme only selects the users and the help files according to the online behavior of the user and the performance of the client, and does not consider whether the user needs the files provided by the cloud. in a television series issue scenario, a large numb of users may abandon that play after a two-set, deploy the file to users that do not need the file to waste valuable cloud resources. This paper designs a proactive caching algorithm to minimize cloud load based on user demand forecast, collaborative scheduling of cache resources and user resources. In this paper, it is found that it can save 40% of cloud flow consumption. 4) The distribution of cloud bandwidth among user groups: In the system of cloud and P2P collaboration, the P2P contribution capability is unstable and the P2P contribution capability of the different Swarm is different, and the cloud bandwidth is used as the supplement to guarantee the user's experience. The existing bandwidth allocation algorithm is mainly focused on the distribution of the live broadcast scene or the P2P bandwidth, and does not relate to the influence of the cloud bandwidth resource allocation on the user download life cycle and the P2P sharing capability in the Swarm. In this paper, the above two problems are discussed based on the flow model, the relationship between the cloud bandwidth and the user's download rate is obtained, and the bandwidth allocation algorithm between the Swarm is put forward under the premise of the limited bandwidth resource of the cloud, and the user experience in the system is optimized.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN948.6

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 蔣芳;;什么是用戶體驗(yàn)[J];程序員;2010年07期

2 劉江;;用戶體驗(yàn),技術(shù)人員的必備常識(shí)[J];程序員;2010年07期

3 李程;;如何量化用戶體驗(yàn)[J];中國(guó)傳媒科技;2011年03期

4 蘇鋒;;誰(shuí)該為用戶體驗(yàn)負(fù)責(zé)?[J];微電腦世界;2011年05期

5 丁常彥;;一切為了用戶體驗(yàn)[J];中國(guó)計(jì)算機(jī)用戶;2008年04期

6 鄧勝利;;國(guó)外用戶體驗(yàn)研究進(jìn)展[J];圖書(shū)情報(bào)工作;2008年03期

7 顧駿;王艷秀;張志美;;圖書(shū)館數(shù)字化參考咨詢服務(wù)中的用戶體驗(yàn)[J];情報(bào)探索;2008年06期

8 馮華;;注重用戶體驗(yàn)[J];微電腦世界;2009年02期

9 周峰;;漫漫用戶體驗(yàn)路[J];程序員;2009年04期

10 譚浩;;用戶體驗(yàn)的藝術(shù)[J];程序員;2009年08期

相關(guān)會(huì)議論文 前10條

1 劉歡;陳潔;陳建香;劉暢;盧蓓蓉;;技術(shù)視角下的高校信息化應(yīng)用用戶體驗(yàn)研究[A];中國(guó)高等教育學(xué)會(huì)教育信息化分會(huì)第十二次學(xué)術(shù)年會(huì)論文集[C];2014年

2 陳軍亮;劉偉杰;劉正捷;;網(wǎng)上銀行用戶體驗(yàn)評(píng)價(jià)體系研究[A];第四屆和諧人機(jī)環(huán)境聯(lián)合學(xué)術(shù)會(huì)議論文集[C];2008年

3 尹志博;楊穎;;用戶體驗(yàn)的量化研究方法[A];第四屆和諧人機(jī)環(huán)境聯(lián)合學(xué)術(shù)會(huì)議論文集[C];2008年

4 韋安明;覃毅力;岳婷;肖輝;張福國(guó);王洪波;崔俊生;;互聯(lián)網(wǎng)大規(guī)模視頻服務(wù)用戶體驗(yàn)及行為規(guī)律研究探討[A];中國(guó)新聞技術(shù)工作者聯(lián)合會(huì)第六次會(huì)員代表大會(huì)、2014年學(xué)術(shù)年會(huì)暨第七屆《王選新聞科學(xué)技術(shù)獎(jiǎng)》和優(yōu)秀論文獎(jiǎng)?lì)C獎(jiǎng)大會(huì)論文集（三等獎(jiǎng)）[C];2014年

5 陶嶸;;用戶體驗(yàn)研究方法概述[A];第十屆全國(guó)心理學(xué)學(xué)術(shù)大會(huì)論文摘要集[C];2005年

6 王柱;周興社;王海鵬;倪紅波;武瑞娟;;一種普適環(huán)境下群體用戶體驗(yàn)的定量評(píng)價(jià)模型[A];第四屆和諧人機(jī)環(huán)境聯(lián)合學(xué)術(shù)會(huì)議論文集[C];2008年

7 陳鑫;;基于自適應(yīng)計(jì)算的互聯(lián)網(wǎng)用戶體驗(yàn)?zāi)Ｐ蚚A];2012全國(guó)無(wú)線及移動(dòng)通信學(xué)術(shù)大會(huì)論文集（下）[C];2012年

8 周娉;方興;;淺析信息社會(huì)下WEB2．0的用戶體驗(yàn)[A];節(jié)能環(huán)保 和諧發(fā)展——2007中國(guó)科協(xié)年會(huì)論文集（二）[C];2007年

9 張含冬;;精于心,簡(jiǎn)于形——淺析優(yōu)良設(shè)計(jì)與用戶體驗(yàn)[A];中國(guó)創(chuàng)意設(shè)計(jì)年鑒論文集2013[C];2014年

10 陳媛Z，

本文編號(hào)：2410522