Prediction of Data Traffic in Telecom Networks based on Deep Neural Networks

Corresponding Author: Quang Hung Do Faculty of Information Technology, University of Transport Technology, Vietnam Email: hungdq@utt.edu.vn Abstract: Accurate prediction of data traffic in telecom network is a challenging task for a better network management. It advances dynamic resource allocation and power management. This study employs deep neural networks including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) techniques to one-hour-ahead forecast the volume of expected traffic and compares this approach to other methods including Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Group Method of Data Handling (GMDH). The deep neural network implementation in this study analyses, evaluates and generates predictions based on the data of telecommunications activity every one hour, continuously in one year, released by Viettel Telecom in Vietnam. The performance indexes, including RMSE, MAPE, MAE, R and Theil’s U are used to make comparison of the developed models. The obtained results show that both LSTM and GRU model outperformed the ANFIS, ANN and GMDH models. The research findings are expected to provide an assistance and forecasting tool for telecom network operators. The experimental results also indicate that the proposed model is efficient and suitable for real-world network traffic prediction.

[1]  Andreas Kassler,et al.  Predicting expected TCP throughput using genetic algorithm , 2016, Comput. Networks.

[2]  Dingde Jiang,et al.  A novel hybrid prediction algorithm to network traffic , 2015, annals of telecommunications - annales des télécommunications.

[3]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[4]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[5]  Hengchao Li,et al.  A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction , 2015 .

[6]  Salihu Aish Abdulkarim,et al.  A cooperative neural network approach for enhancing data traffic prediction , 2017, Turkish J. Electr. Eng. Comput. Sci..

[7]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[8]  Jun Liu,et al.  Internet Traffic Forecasting using Boosting LSTM Method , 2018 .

[9]  Gowrishankar,et al.  A Time Series Modeling and Prediction of Wireless Network Traffic , 2009, Int. J. Interact. Mob. Technol..

[10]  Giha Lee,et al.  Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting , 2019, Water.

[11]  Hui Tian,et al.  A Hybrid Network Traffic Prediction Model Based on Optimized Neural Network , 2017, 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT).

[12]  Young-Keun Park,et al.  Applications of neural networks in high-speed communication networks , 1995 .

[13]  Prashant Kaushik,et al.  Traffic Prediction in Telecom Systems Using Deep Learning , 2018, 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO).

[14]  Hani S. Mahmassani,et al.  Dynamic Network Traffic Assignment and Simulation Methodology for Advanced System Management Applications , 2001 .

[15]  Li Li,et al.  Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).