A Study on Traffic Prediction for the Backbone of Korea's Research and Science Network Using Machine Learning

ABSTRACT

To fix network congestion resulting from the increase in high volume traffic in data-intensive science and the increase in internet traffic due to COVID19, there has been a necessity of traffic engineering through traffic prediction. For this, there have been various attempts from a statistical method such as ARIMA to machine learning including LSTM and GRU. This study aimed to collect and learn KREOENT backbone and subscribers' traffic volume through diverse machine learning techniques (e.g., SVR, LSTM, GRU, etc.) and predict maximum traffic on the following day.