面向移动互联网的业务分析和资源优化系统实现

发布时间：2018-06-24 08:43

本文选题：移动互联网大数据 + 业务流量分析　；参考：《北京邮电大学》2014年硕士论文

【摘要】：随着移动互联网的高速发展,以智能手机和平板电脑为代表的移动终端更新换代的频率越来越高,数据业务的种类不断增加且其流量占比日渐增大,给电信运营商的服务水平提出了更为严峻的挑战。与此同时,数据业务的迅猛发展使得蜂窝网络中业务、资源和计费等的数据量日渐庞大,从而向移动互联中的数据存储、处理和分析提出了更高的要求,因此,运营商面对网络流量的迅速增长,亟需新的分析工具来充分挖掘大数据中的价值,解决将流量转变为效益的难题,最终实现蜂窝网络流量经营和智能化管道等发展战略。业务流量作为用户实际业务行为的载体,能够在一定程度上反映用户的行为特征和对业务的偏好规律,因此建立准确可靠地业务流量模型有助于运营商把握用户行为特征,并依据该特征制定相应的运营策略,从而提高电信运营商的服务质量。移动互联网中的业务种类繁多,数据业务种类繁多,为简化研究的复杂性,必须依据业务协议规范和流量特性对其进行合理有效的分类,最终建立准确的业务流量模型。以往关于移动互联网资源的研究大多都忽视业务类型对网络资源占用情况的影响,然而业务类型和资源之间却存在非常密切的内在联系。因此,本文从实测数据出发,充分研究了不同业务的流量特征以及用户行为特征,进一步建立了业务资源映射模型,并在此基础上开发了基于SQL Server的业务流量分析和资源优化平台,从而为蜂窝网络扩容和优化提供一定的理论指导和技术支撑。面对大数据对移动互联网的挑战,本文还引入了基于Hadoop的业务流量分析系统,通过HDFS分布式存储系统和MapReduce并行处理框架为移动互联网中的大数据分析提供了一套解决方案,从而充分挖掘业务流量大数据中包含的价值,提高网络运营效率和服务水平。
[Abstract]:With the rapid development of mobile Internet, the frequency of mobile terminals, represented by smart phones and tablets, is becoming higher and higher, and the types of data services are increasing and their traffic ratio is increasing day by day. To the service level of telecom operators put forward a more severe challenge. At the same time, with the rapid development of data services, the amount of data such as services, resources and billing in cellular networks is increasing, which leads to the storage of data in mobile interconnection. Therefore, facing the rapid growth of network traffic, operators urgently need new analytical tools to fully tap the value of big data and solve the problem of transforming traffic into benefits. The ultimate realization of cellular network traffic management and intelligent pipeline development strategy. As the carrier of the user's actual business behavior, the service flow can reflect the characteristics of the user's behavior and the law of the user's preference for the business to a certain extent. Therefore, establishing an accurate and reliable service flow model is helpful for operators to grasp the characteristics of user behavior and formulate corresponding operation strategies according to the characteristics, thus improving the service quality of telecom operators. In order to simplify the complexity of the research, it is necessary to classify the mobile Internet according to the rules of service protocol and the characteristics of traffic, in order to simplify the complexity of the research, it is necessary to classify it reasonably and effectively. Finally, an accurate business flow model is established. In the past researches on mobile Internet resources mostly ignored the influence of service types on the occupation of network resources. However there is a very close internal connection between service types and resources. Therefore, based on the measured data, the traffic characteristics and user behavior characteristics of different services are fully studied in this paper, and the service resource mapping model is further established. On this basis, a platform for traffic analysis and resource optimization based on SQL Server is developed, which provides some theoretical guidance and technical support for the expansion and optimization of cellular networks. In the face of the challenge of big data to the mobile Internet, this paper also introduces a traffic analysis system based on Hadoop. Through the HDFS-distributed storage system and MapReduce parallel processing framework, it provides a set of solutions for big data analysis in the mobile Internet, thus fully mining the value contained in the traffic big data. Improve network operation efficiency and service level.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.01;TN929.5

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