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Personalized Service Degradation Policies on OTT Applications Based on the Consumption Behavior of Users

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10962)

Abstract

The proliferation of IP-based telecommunication networks has facilitated the decoupling of application and network layers. This kind of systems allows that Over the Top (OTT) providers deliver their content and applications directly to end users, but at the same time, the OTT applications have generated a growing impact on mobile data traffic and data revenues. In the mobile network’s scope, where the Telcos offer users data plans with limited consumption, service degradation is a measure implemented in a generalized way to apply limits to the amount of data that can be transferred by the users over a period. Currently, when a user exceeds his/her established consumption limit, the Telcos, to save resources and ensure the correct performance of the network, restrict the bandwidth according to user consumption. The vast majority of approaches have not considered the consumption behavior of users to propose a set of personalized service degradation policies that benefit the Telcos but take into consideration the users’ behavior. This paper proposes personalized service degradation policies, from the identification of different OTT services applying statistical analysis and deep packet inspection, and a classification of users, according to their consumption behavior and machine learning algorithms.

Keywords

  • OTT applications
  • Service degradation
  • Machine learning
  • Classification
  • Dataset
  • DPI

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References

  1. Wesley Clover: Over-The-Top (OTT) a dramatic makeover of global communications (2014)

    Google Scholar 

  2. Chetty, M., Banks, R., Brush, A.J., Donner, J., Grinter, R.: You’re capped: understanding the effects of bandwidth caps on broadband use in the home. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA, pp. 3021–3030 (2012)

    Google Scholar 

  3. Chetty, M., Kim, H., Sundaresan, S., Burnett, S., Feamster, N., Edwards, W.K.: uCap: An Internet Data Management Tool for the Home, pp. 3093–3102 (2015)

    Google Scholar 

  4. Ixia: Quality of Service (QoS) and Policy Management in Mobile Data Networks (2013)

    Google Scholar 

  5. ETSI TS 23.203: Policy and charging control architecture, ITU. http://www.itu.int/itu-t/workprog/wp_a5_out.aspx?isn=6084. Accessed 7 Dec 2017

  6. Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, Swinton, UK, pp. 68–77 (2008)

    Google Scholar 

  7. Crawley, E., Sandick, H., Nair, R., Rajagopalan, B.: A Framework for QoS-Based Routing in the Internet. https://tools.ietf.org/html/rfc2386. Accessed 29 Nov 2016

  8. Lakhtaria, K.I.: Enhancing QoS and QoE in IMS enabled next generation networks. In: First International Conference on Networks and Communications, NETCOM 2009, pp. 184–189 (2009)

    Google Scholar 

  9. Kritikos, K., et al.: A survey on service quality description. ACM Comput. Surv. 46(1), 1:1–1:58 (2013)

    CrossRef  Google Scholar 

  10. Quality of Service Regulation Manual. https://www.itu.int/pub/D-PREF-BB.QOS_REG01-2017. Accessed 2 Mar 2018

  11. Davies, E., Carlson, M.A., Weiss, W., Black, D., Blake, S., Wang, Z.: An Architecture for Differentiated Services. https://tools.ietf.org/html/rfc2475. Accessed 29 Nov 2016

  12. Gomes, J.V., Inácio, P.R.M., Pereira, M., Freire, M.M., Monteiro, P.P.: Detection and classification of peer-to-peer traffic: a survey. ACM Comput. Surv. 45(3), 30:1–30:40 (2013)

    CrossRef  Google Scholar 

  13. Agababov, V., et al.: Flywheel: Google’s Data Compression Proxy for the Mobile Web (2015)

    Google Scholar 

  14. Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. SIGCOMM Comput. Commun. Rev. 36(5), 5–16 (2006)

    CrossRef  Google Scholar 

  15. Yang, J., Qiao, Y., Zhang, X., He, H., Liu, F., Cheng, G.: Characterizing user behavior in mobile internet. IEEE Trans. Emerg. Top. Comput. 3(1), 95–106 (2015)

    CrossRef  Google Scholar 

  16. Bertin, E., Crespi, N., L’Hostis, M.: A few myths about telco and OTT models. In: 2011 15th International Conference on Intelligence in Next Generation Networks, pp. 6–10 (2011)

    Google Scholar 

  17. Qiao, X., Xue, S., Chen, J., Fensel, A.: A lightweight convergent personal mobile service delivery approach based on phone book. Int. J. Commun. Syst. 28(1), 49–70 (2015)

    CrossRef  Google Scholar 

  18. Mahola, U., Erasmus, L.: Emerging revenue model structure for mobile industry: the case for traditional and OTT service providers in Sub-Sahara. In: 2015 Portland International Conference on Management of Engineering and Technology (PICMET), pp. 1485–1494 (2015)

    Google Scholar 

  19. Kibilda, J., Malandrino, F., DaSilva, L.A.: Incentives for infrastructure deployment by over-the-top service providers in a mobile network: a cooperative game theory model. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6 (2016)

    Google Scholar 

  20. Dataset Unicauca - 2018 - Google Drive. https://drive.google.com/drive/folders/1FcnKUlSqRb4q5PkGfAGHz-g7bVKL8jmu?usp=sharing

  21. Flowmeter | Datasets | Research | Canadian Institute for Cybersecurity | UNB. http://www.unb.ca/cic/datasets/flowmeter.html. Accessed 30 Nov 2017

  22. ntopng: ntop, 4 August 2011

    Google Scholar 

  23. Ghnemat, R., Jaser, E.: Classification of mobile customers behavior and usage patterns using self-organizing neural networks. Int. J. Interact. Mob. Technol. IJIM 9(4), 4–11 (2015)

    CrossRef  Google Scholar 

  24. Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000)

    CrossRef  Google Scholar 

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Acknowledgements

The authors would like to thank Universidad Del Cauca for supporting this research and Colciencias for the PhD scholarship granted to MSc(C) Juan Sebastián Rojas.

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Correspondence to Juan Sebastián Rojas .

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Rojas, J.S., Gallón, Á.R., Corrales, J.C. (2018). Personalized Service Degradation Policies on OTT Applications Based on the Consumption Behavior of Users. In: , et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10962. Springer, Cham. https://doi.org/10.1007/978-3-319-95168-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-95168-3_37

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