Long-term traffic forecasting in optical networks using Machine Learning


  • Krzysztof Walkowiak Wroclaw University of Science and Technology http://orcid.org/0000-0003-1686-3110
  • Daniel Szostak Wroclaw University of Science and Technology
  • Adam Włodarczyk Wroclaw University of Science and Technology
  • Andrzej Kasprzak Wroclaw University of Science and Technology


Knowledge about future traffic in backbone optical networks may greatly improve a range of tasks that Communications Service Providers (CSPs) have to face. This work proposes a procedure for long-term traffic forecasting in optical networks. We formulate a long-term traffic forecasting problem as an ordinal classification task. Due to the optical networks’ (and other network technologies’) characteristics, traffic forecasting has been realized by predicting future traffic levels rather than the exact traffic volume. We examine different machine learning (ML) algorithms and compare them with time series algorithms methods. To evaluate the developed ML models, we use a quality metric, which considers the network resource usage. Datasets used during research are based on real traffic patterns presented by Internet Exchange Point in Seattle. Our study shows that ML algorithms employed for long-term traffic forecasting problem obtain high values of quality metrics. Additionally, the final choice of the ML algorithm for the forecasting task should depend on CSPs expectations.


A.Y. Grebeshkov, “Cognitive optical networks: architectures and techniques,” Optical Technologies for Telecommunications 2016, International Society for Optics and Photonics, vol. 10342, 2017.

CISCO, “Cisco Annual Internet Report (2018–2023),” CISCO, 2020.

Nokia. “Deepfield Network Intelligence Report Networks in 2020,” Nokia, 2020.

B. Mukherjee, I. Tomkos, M. Tornatore, P. Winzer and Y. Zhao, Springer Handbook of Optical Networks. Springer International Publishing, 2020.

K. Walkowiak, Modeling and Optimization of Cloud-Ready and Content-Oriented Networks. Springer International Publishing, 2016, vol. 56, no. Decision and Control.

V.W.S Chan, “Cognitive optical networks,” in Proceedings of the IEEE International Conference on Communications (ICC), 2018, pp. 1-6.

V.W.S Chan and E. Jang, “Cognitive all-optical fiber network architecture,” in Proceedings of the International Conference on Transparent Optical Networks (ICTON), 2017, pp. 1-4.

A. Knapińska, et al., “On Advantages of Traffic Prediction and Grooming for Provisioning of Time-Varying Traffic in Multilayer Networks.", International Conference on Optical Network Design and Modeling, IEEE, 2023.

T. Panayiotou, M. Michalopoulou and G. Ellinas, “Survey on machine learning for traffic-driven service provisioning in optical networks”, IEEE Communications Surveys & Tutorials, 2023.

H. T. Mouftah and P. Ho, Optical Networks Architecture and Survivability. Springer Science & Business Media, 2003.

M. Jinno, H. Takara, B. Kozicki, Y. Tsukishima, Y. Sone and S. Matsuoka, “Spectrum-efficient and scalable elastic optical path network: architecture, benefits, and enabling technologies,” IEEE Communications Magazine, vol. 47, no. 11, pp. 66-73, 2009.

L. Zong, G. N. Liu, A. Lord, Y. R. Zhou and T. Ma, “40/100/400 Gb/s mixed line rate transmission performance in flexgrid optical networks,” in Proceedings of the Optical Fiber Communication Conference (OFC), 2013, pp. OTu2A-2.

Ciena, https://www.ciena.com/insights/data-sheets/800g-wavelogic-5-extreme-motr-module.html.

D. Szostak, A. Włodarczyk and K. Walkowiak, “Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction,” Electronics, vol. 10, no.13, 2021.

G. Rzym, P. Boryło and P.A. Chołda, “A Time-Efficient Shrinkage Algorithm for Fourier-Based Prediction Enabling Proactive Optimization in Software Defined Networks,” International Journal of Communication Systems, vol. 33, no. 12, 2019.

P. Cortez, M. Rio, M. Rocha and P. Sousa, “Multi-scale Internet traffic forecasting using neural networks and time series methods,” Expert Systems, vol. 29, no. 2, pp. 143-155, 2012.

T. Otoshi, Y. Ohsita, M. Murata, Y. Takahashi, K. Ishibashi and K. Shiomoto, K. “Traffic prediction for dynamic traffic engineering,” Computer Networks, vol. 85, pp. 36-50, 2015.

P. Cembaluk, J Aniszewski, A Knapińska and K Walkowiak, “Forecasting the network traffic with PROPHET,” in Proceedings of the PP-RAI, 2022.

A. Knapińska, K. Półtorak, D. Poręba, J. Miszczyk, M. Daniluk, and K. Walkowiak, „On Feature Selection in Short-Term Prediction of Backbone Optical Network Traffic,” in Proceedings of the 2022 International Conference on Optical Network Design and Modeling (ONDM), 2022. pp. 1-6.

D. Szostak, and K. Walkowiak, “Application of Machine Learning Algorithms for Traffic Forecasting in Dynamic Optical Networks with Service Function Chains,” Foundations of Computing and Decision Sciences, vol. 45, no. 3, pp. 217-232, 2020.

D. Szostak, and K. Walkowiak, “Influence of traffic type on traffic prediction quality in dynamic optical networks with service chains,” in Proceedings of the PP-RAI, 2019.

D. Szostak, and K. Walkowiak, „Machine learning methods for traffic prediction in dynamic optical networks with service chains,” in Proceedings of the 21st International Conference on Transparent Optical Networks (ICTON), 2019. p. 1-4.

R. Boutaba, M.A. Salahuddin, N. Limam, S. Ayoubi, N. Shahriar, F. Estrada-Solano and O.M. Caicedo, “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities,” Journal of Internet Services and Applications, vol. 9, no. 16, pp. 1-99, 2018.

J. Mata, I. de Miguel, R.J. Duran, N. Merayo, S.K. Singh, A. Jukan and M. Chamania, “Artificial intelligence (AI) methods in optical networks: A comprehensive survey,” Optical Switching and Networking, vol. 28, pp. 43-57, 2018.

D. Szostak, “Machine Learning Ensemble Methods for Optical Network Traffic Prediction,” in Proceedings of Computational Intelligence in Security for Information Systems Conference, 2021. p. 105-115.

D. Szostak, K. Walkowiak, and A. Włodarczyk, “Short-term traffic forecasting in optical network using linear discriminant analysis machine learning classifier,” in Proceedings of 22nd International Conference on Transparent Optical Networks (ICTON), 2020, pp. 1–4.

A. Włodarczyk, P. Lechowicz, D. Szostak, K. Walkowiak, „An algorithm for provisioning of time-varying traffic in translucent SDM elastic optical networks”, 22nd International Conference on Transparent Optical Networks (ICTON), 2020.

M. Zukerman, T.D. Neame and R.G. Addie, “Internet traffic modeling and future technology implications,” in Proceedings of the IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 1, 2003, pp. 587-596.

W. Kotlowski and R. Slowinski, “On nonparametric ordinal classification with monotonicity constraints,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, pp. 2576–2589, 2012.

S. Sridhar and A. Kalaivani, “A Survey on Methodologies for Handling Imbalance Problem in Multiclass Classification,” Advances in Smart System Technologies, 2021, pp. 775-790.

J. Tanha, Y. Abdi, N. Samadi, N. Razzaghi and M. Asadpour, “Boosting methods for multi-class imbalanced data classification: an experimental review,” Journal of Big Data, vol. 7, no. 1, pp. 1-47, 2020.

L. Gaudette and N. Japkowicz, “Evaluation methods for ordinal classification,” in Proceedings of 2nd Canadian Conference on Artificial Intelligence, Springer, 2009, pp. 207-210.

C.M. Bishop and M. Nasser, Pattern Recognition and Machine Learning. Springer, 2006, vol. 4, no. 4.

W. Waegeman, B. de Baets and L. Boullart, “ROC analysis in ordinal regression learning,” Pattern Recognition Letters, vol. 29, no. 1, pp. 1-8, 2008.

A. Luque, A. Carrasco, A. Martín and A. de Las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognition, vol. 91, pp. 216-231, 2019.

E. Amigó, J. Gonzalo, S. Mizzaro, J. Carrillo-de-Albornoz, "An effectiveness metric for ordinal classification: Formal properties and experimental results," arXiv preprint, 2020.

S. Baccianella, A. Esuli and F. Sebastiani, “Evaluation measures for ordinal regression,” in Proceedings of Ninth International Conference on Intelligent Systems Design and Applications, 2009, pp. 283-287.

P. Bellmann and F. Schwenker, "Ordinal Classification: Working Definition and Detection of Ordinal Structures," IEEE Access, vol. 8, pp. 164380-164391, 2020.

J.S. Cardoso, and R Sousa, "Measuring the performance of ordinal classification," International Journal of Pattern Recognition and Artificial Intelligence, vol. 25, no. 08, pp. 1173-1195, 2011.

M. Cruz-Ramírez, C. Hervás-Martínez, J. Sánchez-Monedero and P.A. Gutiérrez, “Metrics to guide a multi-objective evolutionary algorithm for ordinal classification,” Neurocomputing, vol. 135, pp. 21–31, 2014.

M. Lázaro and A.R. Figueiras-Vidal, “Neural network for ordinal classification of imbalanced data by minimizing a Bayesian cost”, Pattern Recognition, vol. 137, 2023.

W. Cao, V. Mirjalili and S. Raschka, “Rank consistent ordinal regression for neural networks with application to age estimation,” Pattern Recognition Letters, vol. 140, pp. 325-331, 2020.

J.C. Gámez, D. García, A. González and R. Pérez, “Ordinal classification based on the sequential covering strategy,” International Journal of Approximate Reasoning, vol. 76, pp. 96-110, 2016.

M. Tang, R. Pérez-Fernández and B. De Baets, “A comparative study of machine learning methods for ordinal classification with absolute and relative information,” Knowledge-Based Systems, vol. 230, 2021.

M. Tang, R. Pérez-Fernández and B. De Baets, “Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative information,” Information Fusion, vol. 65, pp. 72-83, 2021.

B. Vega-Márquez, I. A. Nepomuceno-Chamorro, C. Rubio-Escudero and J. C. Riquelme, “OCEAn: Ordinal classification with an ensemble approach,” Information Sciences, vol. 580, pp. 221-242, 2021.

N. Bhatia, “Survey of nearest neighbor techniques,” arXiv preprint, 2010.

T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967.

S. Dominguez-Almendros, N. Benitez-Parejo and A.R. Gonzalez-Ramirez, “Logistic regression models,” Allergologia et immunopathologia, vol. 39, no. 5, pp. 295-305, 2011.

D.C. Montgomery, E.A. Peck and G.G. Vining, Introduction to linear regression analysis. John Wiley & Sons, 2021.

H. Taud and J.F. Mas, “Multilayer perceptron (MLP),” Geomatic approaches for modeling land change scenarios, 2018, pp. 451-455.

F. Rosenblatt, “Perceptron simulation experiments,” in Proceedings of the IRE, vol. 48, no. 3, pp. 301–309, 1960.

T.G. Dietterich, “Ensemble methods in machine learning,” International Workshop on Multiple Classifier Systems, 2000, pp. 1-15.

L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons, 2014.

Y. Yang, H. Lv and N. Chen, “A survey on ensemble learning under the era of deep learning.” Artificial Intelligence Review, 2023, vol. 56(6), pp. 5545-5589.

Extra Trees Classifier, https://www.scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html

Extra Trees Regressor, https://www.scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.

G. Biau and E. Scornet, “A random forest guided tour,” Springer, vol. 25, no. 2, pp. 197-227, 2016.

E. Frank and M. Hall, “A Simple Approach to Ordinal Classification,” European Conference on Machine Learning, Springer, 2001, pp. 145-156.

C.D. Sutton, “Classification and regression trees, bagging, and boosting,” Handbook of statistics, vol. 24, pp. 303-329, 2005.

G.T. Smith, pmdarima: ARIMA estimators for Python, 2017, http://www.alkaline-ml.com/pmdarima.