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Proactive Network Slices Management Algorithm Based on Fuzzy Logic System and Support Vector Regression Model

  • Amal KammounEmail author
  • Nabil Tabbane
  • Gladys Diaz
  • Nadjib Achir
  • Abdulhalim Dandoush
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)

Abstract

Software Defined Networks (SDN), Network Function Virtualization (NFV) and Network Slicing are the key technologies for future network implementation. Their aggregation allows more flexibility for the networks by provisioning network slices according to specific use cases requirements. However, in order to ensure these requirements during all the slice execution time, a management module has to be implemented. In this paper, we present our considered architecture for the management of network slices. We detail especially the network controller components. Moreover, we propose a proactive dynamic approach which forecasts the future workload behavior of network slices. Based on the actual and predicted load state, the management algorithm, which is based on a fuzzy logic system (FLS), will determine the adequate management decision for the deployed slices. Based on real network traces, an evaluation of the efficiency of our algorithm is presented.

References

  1. 1.
    Omnes, N., Bouillon, M., Fromentoux, G., Grand, O.L.: A programmable and virtualized network it infrastructure for the internet of things: How can NFV SDN help for facing the upcoming challenges. In: 2015 18th International Conference on Intelligence in Next Generation Networks, pp. 64–69, February 2015Google Scholar
  2. 2.
    ETSI GS NFV-REL 001, Network Functions Virtualisation (NFV); Resiliency Requirements, V1.1.1 (2015)Google Scholar
  3. 3.
    Mell, P., Grance, T.: NIST special publication 800-145: The NIST definition of cloud computing (2011). https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf. Accessed 20 June 2019
  4. 4.
    Carella, G.A., Pauls, M., Grebe, L., Magedanz, T.: An extensible autoscaling engine (ae) for software-based network functions. In: 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), pp. 219–225, November 2016Google Scholar
  5. 5.
    Dotcenko, S., Vladyko, A., Letenko, I.: A fuzzy logic-based information security management for software-defined networks. In: 16th International Conference on Advanced Communication Technology, pp. 167–171, February 2014Google Scholar
  6. 6.
    Amiri, M., Mohammad-Khanli, L.: Survey on prediction models of applications for resources provisioning in cloud. J. Netw. Comput. Appl. 82, 93–113 (2017)CrossRefGoogle Scholar
  7. 7.
    Ahammad, T., Kumar Acharjee, U., Hasan, M.M.: Energy-effective service-oriented cloud resource allocation model based on workload prediction. In: 2018 21st International Conference of Computer and Information Technology (ICCIT), pp. 1–6, December 2018Google Scholar
  8. 8.
    Zhong, W., Zhuang, Y., Sun, J., Gu, J.: The cloud computing load forecasting algorithm based on wavelet support vector machine. In: Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2017, pp. 38:1–38:5. ACM, New York (2017)Google Scholar
  9. 9.
    Nadig, D., Ramamurthy, B., Bockelman, B., Swanson, D.: Large data transfer predictability and forecasting using application-aware SDN, pp. 1–6, December 2018Google Scholar
  10. 10.
    Dhib, E., Zangar, N., Tabbane, N., Boussetta, K.: Impact of seasonal ARIMA workload prediction model on QoE for massively multiplayers online gaming. In: 2016 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 737–741, September 2016Google Scholar
  11. 11.
    Tseng, F., Tsai, M., Tseng, C., Yang, Y., Liu, C., Chou, L.: A lightweight autoscaling mechanism for fog computing in industrial applications. IEEE Trans. Ind. Inform. 14, 4529–4537 (2018)CrossRefGoogle Scholar
  12. 12.
    Arabnejad, H., Pahl, C., Jamshidi, P., Estrada, G.: A comparison of reinforcement learning techniques for fuzzy cloud auto-scaling. In: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2017, pp. 64–73. IEEE Press, Piscataway (2017)Google Scholar
  13. 13.
    “Open baton”. http://openbaton.github.io/. Accessed 25 Mar 2018
  14. 14.
    Kammoun, A., Tabbane, N., Diaz, G., Dandoush, A., Achir, N.: End-to-end efficient heuristic algorithm for 5G network slicing. In: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pp. 386–392, May 2018Google Scholar
  15. 15.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  16. 16.
    “Alibaba cluster trace program”. https://github.com/alibaba/clusterdata. Accessed 31 July 2019
  17. 17.
    Lu, C., Ye, K., Xu, G., Xu, C., Bai, T.: Imbalance in the cloud: an analysis on alibaba cluster trace. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 2884–2892, December 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Amal Kammoun
    • 1
    • 2
    Email author
  • Nabil Tabbane
    • 1
  • Gladys Diaz
    • 2
  • Nadjib Achir
    • 2
  • Abdulhalim Dandoush
    • 3
  1. 1.MEDIATRON LaboratoryUniversity of Carthage, Sup’ComAryanahTunisia
  2. 2.L2TI LaboratoryUniversity of Paris 13ParisFrance
  3. 3.ESMEParisFrance

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