Internet Traffic Prediction Using Recurrent Neural Networks
DOI:
https://doi.org/10.4108/eetinis.v9i4.1415Keywords:
Internet traffic prediction, recurrent neural networks, network planningAbstract
Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are in general unpredictable and must adapt to unforeseen circumstances. In small to medium-size networks, the administrator can anticipate the fluctuations in traffic without the need of using forecasting tools, but in the scenario of large-scale networks where hundreds of new users can be added in a matter of weeks, more efficient forecasting tools are required to avoid congestion and over provisioning. Network and hardware resources are however limited; and hence resource allocation is critical for the NTP with scalable solutions. To this end, in this paper, we propose an efficient NTP by optimizing recurrent neural networks (RNNs) to analyse the traffic patterns that occur inside flow time series, and predict future samples based on the history of the traffic that was used for training. The predicted traffic with the proposed RNNs is compared with the real values that are stored in the database in terms of mean squared error, mean absolute error and categorical cross entropy. Furthermore, the real traffic samples for NTP training are compared with those from other techniques such as auto-regressive moving average (ARIMA) and AdaBoost regressor to validate the effectiveness of the proposed method. It is shown that the proposed RNN achieves a better performance than both the ARIMA and AdaBoost regressor when more samples are employed.
Downloads
References
N. Ramakrishnan and T. Soni, ”Network Traffic Prediction Using Recurrent Neural Networks,” 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, 2018, pp. 187-193, doi: 10.1109/ICMLA.2018.00035. DOI: https://doi.org/10.1109/ICMLA.2018.00035
W. Wang et al., ”A network traffic flow prediction with deep learning approach for large-scale metropolitan area network,” NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, 2018, pp. 1-9, doi: 10.1109/NOMS.2018.8406252. DOI: https://doi.org/10.1109/NOMS.2018.8406252
S. Troia, R. Alvizu, Y. Zhou, G. Maier and A. Pattavina, ”Deep Learning-Based Traffic Prediction for Network Optimization,” 2018 20th International Conference on Transparent Optical Networks (ICTON), Bucharest, 2018, pp. 1-4, doi: 10.1109/ICTON.2018.8473978. DOI: https://doi.org/10.1109/ICTON.2018.8473978
T. Ding, A. AlEroud and G. Karabatis, ”Multi-granular aggregation of network flows for security analysis,” 2015 IEEE International Conference on Intelligence and Security Informatics (ISI), Baltimore, MD, 2015, pp. 173-175, doi: 10.1109/ISI.2015.7165965. DOI: https://doi.org/10.1109/ISI.2015.7165965
G. Vormayr, T. Zseby and J. Fabini, ”Botnet Communication Patterns,” in IEEE Communications Surveys Tutorials, vol. 19, no. 4, pp. 2768-2796, Fourthquarter 2017, doi: 10.1109/COMST.2017.2749442. DOI: https://doi.org/10.1109/COMST.2017.2749442
F. Shen, W. Zhang and P. Chang, ”An Engineering Approach to Prediction of Network Traffic Based on Time- Series Model,” 2009 International Joint Conference on Artificial Intelligence, Hainan Island, 2009, pp. 432-435, doi: 10.1109/JCAI.2009.104. DOI: https://doi.org/10.1109/JCAI.2009.104
D. H. Hagos, P. E. Engelstad, A. Yazidi and Ø. Kure, ”Recurrent Neural Network-Based Prediction of TCP Transmission States from Passive Measurements,” 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, 2018, pp. 1-10, doi: 10.1109/NCA.2018.8548064. DOI: https://doi.org/10.1109/NCA.2018.8548064
H.Zare Moayedi and M. A. Masnadi-Shirazi Arima model:”Arima model for network traffic prediction and anomaly detection”. In 2008 International Symposium on Information Technology, Kuala Lumpur, 2008, pp. 1-6, doi: 10.1109/IT-SIM.2008.4631947 DOI: https://doi.org/10.1109/ITSIM.2008.4631947
Y. Yu, J. Wang, M. Song and J. Song, ”Network Traffic Prediction and Result Analysis Based on Seasonal ARIMA and Correlation Coefficient,” 2010 International Conference on Intelligent System Design and Engineering Application, Changsha, 2010, pp. 980-983, doi: 10.1109/ISDEA.2010.335. DOI: https://doi.org/10.1109/ISDEA.2010.335
T. H. H. Aldhyani and M. R. Joshi, ”Integration of time series models with soft clustering to enhance network traffic forecasting,” 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, 2016, pp. 212-214, doi: 10.1109/ICRCICN.2016.7813658. DOI: https://doi.org/10.1109/ICRCICN.2016.7813658
Y. Song, M. Liu, S. Tang and X. Mao, ”Time series matrix factorization prediction of internet traffic matrices,” 37th Annual IEEE Conference on Local Computer Networks, Clearwater, FL, 2012, pp. 284-287, doi: 10.1109/LCN.2012.6423629. DOI: https://doi.org/10.1109/LCN.2012.6423629
B. Yu, G. Graciani, A. Nascimento and J. Hu, ”Cost-adaptive Neural Networks for Peak Volume Prediction with EMM Filtering,” 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 4208-4213, doi: 10.1109/BigData47090.2019.9006188. DOI: https://doi.org/10.1109/BigData47090.2019.9006188
Zhitang Chen, Jiayao Wen and Yanhui Geng, ”Predicting future traffic using Hidden Markov Models,” 2016 IEEE 24th International Conference on Network Protocols (ICNP), Singapore, 2016, pp. 1-6, doi: 10.1109/ICNP.2016.7785328. DOI: https://doi.org/10.1109/ICNP.2016.7785328
Joao Paulo Coelho, Tatiana M. Pinho, Jose Boaventura-Cunha, “Hidden Markov Models, Theory and Implementation using MATLAB”, 2019 by Taylor Francis Group, LLC, CRC Press, Version Date: 20190401, International Standard Book Number-13: 978-0-367-20349-8 (Hardback).
J. Rodrigues, A. Nogueira and P. Salvador, ”Improving the Traffic Prediction Capability of Neural Networks Using Sliding Window and Multi-task Learning Mechanisms,” 2010 2nd International Conference on Evolving Internet, Valcencia,2010, pp. 1-8, doi: 10.1109/INTERNET.2010.11. DOI: https://doi.org/10.1109/INTERNET.2010.11
G. Feng, ”Network Traffic Prediction Based on Neural Network,” 2015 International Conference on Intelligent Transporta- tion, Big Data and Smart City, Halong Bay, 2015, pp. 527-530, doi: 10.1109/ICITBS.2015.136. DOI: https://doi.org/10.1109/ICITBS.2015.136
Q. Zhuo, Q. Li, H. Yan and Y. Qi, ”Long short-term memory neural network for network traffic prediction,” 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, 2017, pp. 1-6, doi: 10.1109/ISKE.2017.8258815. DOI: https://doi.org/10.1109/ISKE.2017.8258815
T. Ko, S. M. Raza, D. T. Binh, M. Kim and H. Choo, ”Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks,” 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), Taichung, Taiwan, 2020, pp. 1-4, doi: 10.1109/IMCOM48794.2020.9001712. DOI: https://doi.org/10.1109/IMCOM48794.2020.9001712
X. Wang, C. Zhang and S. Zhang, ”Modified Elman neural network and its application to network traffic prediction,” 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, Hangzhou, 2012, pp. 629-633, doi: 10.1109/CCIS.2012.6664250. DOI: https://doi.org/10.1109/CCIS.2012.6664250
J. Skupa and J. Safarik, ”Survey of traffic prediction methods for dynamic routing in overlay networks,” 2017 IEEE 14th International Scientific Conference on Informatics, Poprad, 2017, pp. 339-343, doi: 10.1109/INFORMATICS.2017.8327271. DOI: https://doi.org/10.1109/INFORMATICS.2017.8327271
Jason Brownlee, “Introduction to Time Series Forecasting with Python - How to Prepare Data and Develop Models to Predict the Future” https://machinelearningmastery.com/make-sample-forecasts-arima-python/, https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/
Jason Brownlee, “ XGBoost With Python”,” Gradient Boosted Trees with XGBoost and scikit-learn” Edition: v1.14 https://machinelearningmastery.com/adaboost-ensemble-in-python/
S. Wu and H. Nagahashi, ”Parameterized AdaBoost: Introducing a Parameter to Speed Up the Training of Real AdaBoost,” in IEEE Signal Processing Letters, vol. 21, no. 6, pp. 687-691, June 2014, doi: 10.1109/LSP.2014.2313570. DOI: https://doi.org/10.1109/LSP.2014.2313570
Simeon Kostadinov, “Recurrent Neural Networks with Python Quick Start Guide”, November 2018, Published by Packt Publishing Ltd., 35 Livery Street Birmingham
Jason Brownlee, “Long Short-Term Memory Networks with Python – Develop Sequence Prediction Models With Deep Learning”, Copyright 2017 Jason Brownlee. All Rights Reserved, Edition: v1.0
Dhruvil Shah, “Exploring the Next Word Predictor! – Different approaches for building the Next Word Predictor”, May 8 2020, https://towardsdatascience.com/exploring-the-next-word-predictor-5e22aeb85d8f
https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/43795
Jason Brownlee, “Master Machine Learning Algorithms, Discover How They Work and Implement Them From Scratch”
Diederik P. Kingma, Jimmy Lei Ba, ”ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION” Published as a conference paper at ICLR 2015 https://arxiv.org/pdf/1412.6980.pdf
Jason Brownlee, ”Better Deep Learning - Train Faster, Reduce Overfitting, and Make Better Predictions”, https://machinelearningmastery.com/better-deep-learning/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
