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

【摘要】：近年來IPTV用戶的數(shù)量在快速增長,IPTV網(wǎng)絡的系統(tǒng)結構日趨復雜,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維人員的經(jīng)驗及部署網(wǎng)絡探針,但是在大用戶量的情況下難以處理迅速定位IPTV故障。IPTV的服務質量是影響用戶滿意度的重要因素,因此運營商迫切的需要一種能夠快速定位IPTV故障的新技術、新方法,從而提高服務質量,提高用戶滿意度。本論文分析并設計實現(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)和報障數(shù)據(jù)進行了清洗、分析。由于KPI數(shù)據(jù)中包含較多互相關聯(lián)的指標,我們在做了一系列統(tǒng)計分析的基礎上,采用相關性算法與聚類算法相結合的方式篩選出了用戶KPI數(shù)據(jù)中的代表性指標。在代表性指標篩選完成之后,基于機器學習理論,選取合適的模型建立了質差用戶篩選模型,得到IPTV用戶中的觀看體驗較差的用戶。具體而言,在該部分中,針對所處理的數(shù)據(jù)特點,對AdaBoost算法進行了多方面的改進,包括樣本初始權重的賦值方式以及樣本權重的更新方式,以提高算法預測的準確率。再根據(jù)質差用戶以及用戶-設備關聯(lián)表建立故障設備篩選模型,實現(xiàn)IPTV故障定位。最后通過仿真實驗驗證了本論文所提出的設計方案,通過與其他模型的對比,驗證了改進的AdaBoost算法在提高IPTV系統(tǒng)故障定位準確率方法的效果。此外,本論文對大數(shù)據(jù)環(huán)境下IPTV故障定位系統(tǒng)進行了實現(xiàn),為了使系統(tǒng)具有可靠性、可擴展性、處理速度快等特點,我們在構建系統(tǒng)時以Hadoop平臺為基礎,實現(xiàn)了數(shù)據(jù)的分布式存儲、分布式計算,并且采用了Ambari對系統(tǒng)進行管理,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.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN949.292

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本文編號：1607903