本文選題：IPTV　切入點(diǎn)：大數(shù)據(jù)　出處：《南京郵電大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來IPTV用戶的數(shù)量在快速增長,IPTV網(wǎng)絡(luò)的系統(tǒng)結(jié)構(gòu)日趨復(fù)雜,故障發(fā)生的原因也日趨多樣化、綜合化。傳統(tǒng)的故障定位方法主要依靠運(yùn)維人員的經(jīng)驗(yàn)及部署網(wǎng)絡(luò)探針,但是在大用戶量的情況下難以處理迅速定位IPTV故障。IPTV的服務(wù)質(zhì)量是影響用戶滿意度的重要因素,因此運(yùn)營商迫切的需要一種能夠快速定位IPTV故障的新技術(shù)、新方法,從而提高服務(wù)質(zhì)量,提高用戶滿意度。本論文分析并設(shè)計(jì)實(shí)現(xiàn)了一種大數(shù)據(jù)環(huán)境下的IPTV故障定位系統(tǒng)。首先,本論文對IPTV用戶的KPI數(shù)據(jù)和報(bào)障數(shù)據(jù)進(jìn)行了清洗、分析。由于KPI數(shù)據(jù)中包含較多互相關(guān)聯(lián)的指標(biāo),我們在做了一系列統(tǒng)計(jì)分析的基礎(chǔ)上,采用相關(guān)性算法與聚類算法相結(jié)合的方式篩選出了用戶KPI數(shù)據(jù)中的代表性指標(biāo)。在代表性指標(biāo)篩選完成之后,基于機(jī)器學(xué)習(xí)理論,選取合適的模型建立了質(zhì)差用戶篩選模型,得到IPTV用戶中的觀看體驗(yàn)較差的用戶。具體而言,在該部分中,針對所處理的數(shù)據(jù)特點(diǎn),對AdaBoost算法進(jìn)行了多方面的改進(jìn),包括樣本初始權(quán)重的賦值方式以及樣本權(quán)重的更新方式,以提高算法預(yù)測的準(zhǔn)確率。再根據(jù)質(zhì)差用戶以及用戶-設(shè)備關(guān)聯(lián)表建立故障設(shè)備篩選模型,實(shí)現(xiàn)IPTV故障定位。最后通過仿真實(shí)驗(yàn)驗(yàn)證了本論文所提出的設(shè)計(jì)方案,通過與其他模型的對比,驗(yàn)證了改進(jìn)的AdaBoost算法在提高IPTV系統(tǒng)故障定位準(zhǔn)確率方法的效果。此外,本論文對大數(shù)據(jù)環(huán)境下IPTV故障定位系統(tǒng)進(jìn)行了實(shí)現(xiàn),為了使系統(tǒng)具有可靠性、可擴(kuò)展性、處理速度快等特點(diǎn),我們在構(gòu)建系統(tǒng)時(shí)以Hadoop平臺為基礎(chǔ),實(shí)現(xiàn)了數(shù)據(jù)的分布式存儲、分布式計(jì)算,并且采用了Ambari對系統(tǒng)進(jìn)行管理,采用Scribe收集系統(tǒng)日志統(tǒng)一處理,確保系統(tǒng)能夠穩(wěn)定、高效的運(yùn)行。
[Abstract]:In recent years, the number of IPTV users is increasing rapidly. The system structure of IPTV network is becoming more and more complex, and the causes of the faults are becoming more and more diversified and integrated. The traditional fault location methods mainly rely on the experience of operators and the deployment of network probes. However, in the case of large number of users, it is difficult to deal with the rapid location of IPTV fault. The quality of service (QoS) is an important factor affecting the customer satisfaction. Therefore, operators urgently need a new technology and method to quickly locate the IPTV fault. In order to improve the quality of service and improve customer satisfaction, this paper analyzes and designs a IPTV fault location system under big data environment. Firstly, the KPI data and obstacle data of IPTV users are cleaned in this paper. Analysis. Because the KPI data contain more interrelated indicators, we have done a series of statistical analysis, The representative indexes in user KPI data are screened by combining correlation algorithm with clustering algorithm. After the representative index selection is completed, based on the theory of machine learning, the quality difference user screening model is established based on the machine learning theory. In particular, in this part, according to the characteristics of the data processed, the AdaBoost algorithm is improved in many aspects. It includes the assignment method of sample initial weight and the updating method of sample weight to improve the accuracy of algorithm prediction. Then the fault equipment screening model is established according to the quality difference user and user-device association table. Finally, the design scheme proposed in this paper is verified by simulation experiments. Compared with other models, the improved AdaBoost algorithm is proved to be effective in improving the accuracy of fault location in IPTV system. In this paper, the IPTV fault location system under big data environment is implemented. In order to make the system reliable, extensible and fast, we build the system on the basis of Hadoop platform and realize the distributed storage of data. Distributed computing, and the use of Ambari to manage the system, Scribe collection system log unified processing to ensure that the system can run stably and efficiently.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN949.292

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