Machine Learning and Data Networks: Perspectives, Feasibility, and Opportunities

  • Raúl Lozada-Yánez
  • Fernando Molina-GranjaEmail author
  • Pablo Lozada-Yánez
  • Jonny Guaiña-Yungan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1160)


Currently, Machine Learning has become a research trend around the world and its application is being studied in most fields of human work where it is possible to take advantage of its potential. Current computer networks and distributed computing systems are key infrastructures that have allowed the development of efficient computing resources for Machine Learning. The benefits of Machine Learning mean that the data network itself can also use this promising technology. The aim of the study is to provide a comprehensive research guide on networking (networking) assisted by machine learning to help motivate researchers to develop new innovative algorithms, standards, and frameworks. This article focuses on the application of Machine Learning for Networks, a methodology that can stimulate the development of new network applications. The article presents the basic workflow for the application of Machine Learning technology in the field of networks. Then, there is also a selective inspection of recent representative advances with explanations of its benefits and its design principles. These advances are divided into several network design objectives and detailed information on how they perform at each step of the Machine Learning Network workflow is presented. Finally, the new opportunities presented by the application of Machine Learning in the design of networks and collaborative construction of this new interdisciplinary field are pointed out.


Machine learning Learning model Traffic prediction Traffic classification Congestion control 


  1. Alipourfard, O., Yu, M.: CherryPick: adaptively unearthing the best cloud configurations for big data analytics, 15 (2017)Google Scholar
  2. Chen, J.X.: The evolution of computing: AlphaGo. Comput. Sci. Eng. 18(4), 4–7 (2016). Scholar
  3. Chen, Z., Wen, J., Geng, Y.: Predicting future traffic using Hidden Markov Models. In: 2016 IEEE 24th International Conference on Network Protocols (ICNP), pp. 1–6 (2016).
  4. Clark, D.D., Partridge, C., Ramming, J.C., Wroclawski, J.T.: A knowledge plane for the internet. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 3–10 (2003).
  5. Cunha, Í., Marchetta, P., Calder, M., Chiu, Y.-C., Schlinker, B., Machado, B.V.A., Katz-Bassett, E.: Sibyl: a practical internet route Oracle. In: Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation, pp. 325–344. Recuperado de (2016).
  6. Dong, M., Li, Q., Zarchy, D., Godfrey, P.B., Schapira, M.: PCC: Re-architecting congestion control for consistent high performance, p. 15 (2015)Google Scholar
  7. IETF - Internet Engineering Task Force: IETF97-NMLRG-20161117-1330. Recuperado de (2016).
  8. Jiang, J., Sekar, V., Milner, H., Shepherd, D., Stoica, I., Zhang, H.: CFA: a practical prediction system for video QoE optimization, 15 (2016)Google Scholar
  9. Jiang, J., Sun, S., Sekar, V., Zhang, H.: Pytheas: enabling data-driven quality of experience optimization using group-based exploration-exploitation, 15 (2017)Google Scholar
  10. Kato, N., Fadlullah, Z.M., Mao, B., Tang, F., Akashi, O., Inoue, T., Mizutani, K.: The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wirel. Commun. 24(3), 146–153 (2017). Scholar
  11. Mao, B., Fadlullah, Z.M., Tang, F., Kato, N., Akashi, O., Inoue, T., Mizutani, K.: Routing or computing? The paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Trans. Comput. 66(11), 1946–1960 (2017). Scholar
  12. Mao, B., Fadlullah, Z.M., Tang, F., Kato, N., Akashi, O., Inoue, T., Mizutani, K.: State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surv. Tutorials 19(4), 2432–2455 (2017)CrossRefGoogle Scholar
  13. Mao, H., Alizadeh, M., Menache, I., Kandula, S.: Resource management with deep reinforcement learning, pp. 50–56 (2016).
  14. Mestres, A., Rodriguez-Natal, A., Carner, J., Barlet-Ros, P., Alarcón, E., Solé, M., Cabellos, A.: Knowledge-defined networking. SIGCOMM Comput. Commun. Rev. 47(3), 2–10 (2017). Scholar
  15. Poupart, P., Chen, Z., Jaini, P., Fung, F., Susanto, H., Geng, Y., Jin, H.: Online flow size prediction for improved network routing. In: 2016 IEEE 24th International Conference on Network Protocols (ICNP), pp. 1–6 (2016).
  16. Sun, Y., Yin, X., Jiang, J., Sekar, V., Lin, F., Wang, N., Sinopoli, B.: CS2P: improving video bitrate selection and adaptation with data-driven throughput prediction. In: Proceedings of the 2016 ACM SIGCOMM Conference, pp. 272–285. ACM (2016)Google Scholar
  17. Winstein, K., Balakrishnan, H.: TCP ex machina: computer-generated congestion control. In: Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM, pp. 123–134 (2013).
  18. Zhang, J., Chen, X., Xiang, Y., Zhou, W., Wu, J.: Robust network traffic classification. IEEE/ACM Trans. Netw. 23(4), 1257–1270 (2015). Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Raúl Lozada-Yánez
    • 1
  • Fernando Molina-Granja
    • 2
    Email author
  • Pablo Lozada-Yánez
    • 1
  • Jonny Guaiña-Yungan
    • 1
  1. 1.Facultad de Informática y ElectrónicaEscuela Superior Politécnica de ChimborazoRiobambaEcuador
  2. 2.Facultad de IngenieríaUniversidad Nacional de ChimborazoRiobambaEcuador

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