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http://dx.doi.org/10.17661/jkiiect.2021.14.2.115

A Study on the Quality Monitoring and Prediction of OTT Traffic in ISP  

Nam, Chang-Sup (Department of Mechanical ICT Engineering, Hoseo University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.14, no.2, 2021 , pp. 115-121 More about this Journal
Abstract
This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.
Keywords
artificial intelligence; big data; internet quality monitoring; internet traffic; Internet traffic prediction; Machine learning; network traffic;
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