A Study on Traffic Prediction for the Backbone of Korea’s Research and Science Network Using Machine Learning
DOI:
https://doi.org/10.13052/jwe1540-9589.2152Keywords:
Traffic Prediction, Machine Learning, SVR, LSTM, GRU, KREONETAbstract
To fix network congestion resulting from the increase in high volume traffic in data-intensive science and the increase in internet traffic due to COVID-19, 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.
Downloads
References
Huifang Feng, Yantai Shu, ‘Study on Network Traffic Prediction Techniques’, International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1041–1044, 2005
Vinayakumar R, Soman KP, et al., ‘Applying Deep Learning Approaches for Network Traffic Prediction’, ICACCI, 2353–2358, 2017
Paulo Cortez, Miguel Rio, et al., ‘Internet Traffic Forecasting using Neural Networks’, International Joint Conference on Neural Networks, pp. 2635–2642, 2006
Nandini Krishnaswamy, Mariam Kiran, ‘Data-driven Learning to Predict WAN Network Traffic’, SNTA, pp. 11–18, 2020
Harris Drucker, Chirs J.C. Buges., et al., ‘Support Vector Regression Machines’, Advances in neural information processing systems 9, pp. 155–161, 1996
Chi-Jie Lu a, Tian-Shyug Lee, et al., ‘Financial time series forecasting using independent component analysis and support vector regression’, Decision Support Systems, pp. 115–125, 2009
Sepp Hochreiter, Jürgen Schmidhuber, ‘Long Short-term Memory’, Neural Computation, pp. 1735–1780, 1997
Dejun Chen, Congcong Xiong, et al., ‘Improved LSTM Based on Attention Mechanism for Short-term Traffic Flow Prediction’, 10th International Conference on Information Science and Technology, pp. 71–76, 2020
W. Esmail, T. Stockmanns, et al., ‘Machine Learning for Track Finding at PANDA’, Proceedings of the CTD/WIT, pp, 2019
Kyunghyun Cho, et al., ‘Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation’, EMNLP 2014, pp. 1724–1734, 2014
Rui Fu, Zuo Zhang, and Li Li, ‘Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction’, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328, 2016
Rob J. Hyndman, et al., ‘Another look at measures of forecast accuracy’, International Journal of Forecasting 22, pp. 697–688, 2006
The KREONET official website http://www.kreonet.net/kreonet/index.jsp
William Stallings, ‘SNMP and SNMPv2: The Infrastructure for Network Management’, IEEE Communications Magazine, pp. 37–43, 1998
Tobias Oetiker, ‘MRTG – The Multi Router Traffic Grapher’, 1998
D. Arthur, S. Vassilvitskii, ‘How Slow is the k-means Method?’ Proceedings of the 2006 Symposium on Computational Geometry, 2006
Yong-hwan Kim, Ki-Hyeon Kim, Dongkyun Kim, ‘Design and Implementation of Virtually Dedicated Network Service in SD-WAN Based Advanced Research & Educational (R&E) Network’, The Journal of Korean Institute of Communications and Information Sciences, pp. 2050–2064, 2017
