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Adaptive FH optimization in MEC-assisted 5G environments

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Abstract

To address the limitations of current radio access networks (RANs), centralized RANs adopting the concept of flexible splits of the BBU functions between radio units (RUs) and the central unit have been proposed. This concept can be implemented combining both the Mobile Edge Computing model and relatively large-scale centralized Data Centers. This architecture requires high-bandwidth/low-latency optical transport networks interconnecting RUs and compute resources adopting SDN control. This paper proposes a novel mathematical model based on Evolutionary Game Theory that allows to dynamically identify the optimal split option with the objective to unilaterally minimize the infrastructure operational costs in terms of power consumption. Optimal placement of the SDN controllers is determined by a heuristic algorithm in such a way that guarantees the stability of the whole system. Finally, multi-agent learning methods were investigated in order to expand the model to more sophisticated scenarios where many RUs with limited information are interacting.

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References

  1. Tzanakaki, et al.: Wireless-optical network convergence: enabling the 5G architecture to support operational and end-user services. IEEE Commun. Mag. 55(10), 184–192 (2017). https://doi.org/10.1109/mcom.2017.1600643

    Article  Google Scholar 

  2. 5G and Verticals ‹ 5G-PPP, 5g-ppp.eu, 2019. https://5g-ppp.eu/verticals/. Accessed 30 Oct 2019

  3. 5G Network Slicing for Vertical Industries, Huawei.com, 2019. https://www.huawei.com/minisite/5g/img/5g-network-slicing-for-vertical-industries-en.pdf. Accessed 30 Oct 2019

  4. Kamel, M., Hamouda, W., Youssef, A.: Ultra-dense networks: a survey. IEEE Commun. Surv. Tutor. 18(4), 2522–2545 (2016). https://doi.org/10.1109/comst.2016.2571730

    Article  Google Scholar 

  5. View on 5G Architecture, 5g-ppp.eu, 2019. https://5g-ppp.eu/wp-content/uploads/2018/01/5G-PPP-5G-Architecture-White-Paper-Jan-2018-v2.0.pdf. 30 Oct 2019

  6. Liu, H., Eldarrat, F., Alqahtani, H., Reznik, A., de Foy, X., Zhang, Y.: Mobile edge cloud system: architectures, challenges, and approaches. IEEE Syst. J. 12(3), 2495–2508 (2018). https://doi.org/10.1109/jsyst.2017.2654119

    Article  Google Scholar 

  7. Cloud RAN and MEC: A Perfect Pairing, Etsi.org, 2019. https://www.etsi.org/images/files/ETSIWhitePapers/etsi_wp23_MEC_and_CRAN_ed1_FINAL.pdf. Accessed 30 Oct 2019

  8. What is enhanced Mobile Broadband (eMBB), 5g.co.uk, 2019. https://5g.co.uk/guides/what-is-enhanced-mobile-broadband-embb/.Accessed 30 Oct 2019

  9. eCPRI Specification V1.1, Cpri.info, 2019. https://www.cpri.info/downloads/eCPRI_v_1_1_2018_01_10.pdf. Accessed 30 Oct 2019

  10. Software-Defined Networking (SDN) Definition—Open Networking Foundation, Open Networking Foundation, 2019. https://www.opennetworking.org/sdn-definition/ . Accessed 30 Oct 2019

  11. Heller, B., Sherwood, R., McKeown, N.: The controller placement problem. ACM SIGCOMM Comput. Commun. Rev. 42(4), 473 (2012). https://doi.org/10.1145/2377677.2377767

    Article  Google Scholar 

  12. Hock, D., et al.: Pareto-Optimal resilient controller placement in SDN-based Core networks. In: IEEE Proceedings of the 2013 25th International Teletraffic Congress (ITC), 10–12 Sep 2013, Shanghai, China. https://doi.org/10.1109/ITC.2013.6662939

  13. Noormohammadpour, M., Raghavendra, C.S.: "Datacenter traffic control: understanding techniques and tradeoffs. IEEE Commun. Surv. Tutor. 20(2), 1492–1525 (2018)

    Article  Google Scholar 

  14. Weibull, J.: Evolutionary Game Theory. MIT Press, Cambridge (2004)

    MATH  Google Scholar 

  15. Yi, T., Zuwang, W.: Effect of time delay and evolutionarily stable strategy. J.Theor. Biol. 187(1), 111–116 (1997). https://doi.org/10.1006/jtbi.1997.0427

    Article  Google Scholar 

  16. Obando, G., Poveda, J., Quijano, N.: Replicator dynamics under perturbations and time delays. Math. Control Signals Syst. (2016). https://doi.org/10.1007/s00498-016-0170-9

    Article  MathSciNet  MATH  Google Scholar 

  17. Anastasopoulos, N., Asteriou, D.: Optimal dynamic auditing based on game theory. Oper Res Int J (2019). https://doi.org/10.1007/s12351-019-00491-3

    Article  MATH  Google Scholar 

  18. Anastasopoulos, N.P., Anastasopoulos, M.P.: The evolutionary dynamics of audit. Eur. J. Oper. Res. 216, 469–476 (2012)

    Article  MathSciNet  Google Scholar 

  19. Bloembergen, D., Tuyls, K., Hennes, D., Kaisers, M.: Evolutionary dynamics of multi-agent learning: a survey. J. Artif. Intell. Res. 53, 659–697 (2015). https://doi.org/10.1613/jair.4818

    Article  MathSciNet  MATH  Google Scholar 

  20. Wiering, M., Otterlo, M.: Reinforcement Learning. Springer, Berlin (2014)

    Google Scholar 

  21. Hernandez-Leal, P., Kaisers, M., Baarslag, T., Munoz de Cote, E.: A survey of learning in multiagent environments: dealing with non-stationarity.  arXiv vol. 170709183, 2017. Accessed 5 Nov. 2019

  22. Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 38(2), 156–172 (2008). https://doi.org/10.1109/TSMCC.2007.913919

    Article  Google Scholar 

  23. Börgers, T., Sarin, R.: Learning through reinforcement and replicator dynamics. J. Econ. Theory 77(1), 1–14 (1997). https://doi.org/10.1006/jeth.1997.2319

    Article  MathSciNet  MATH  Google Scholar 

  24. Tuyls, K., Hoen, P.J.T., Vanschoenwinkel, B.: An evolutionary dynamical analysis of multi-agent learning in iterated games. J. Auton. Agents Multi Agent Syst. 12(1), 115–153 (2006)

    Article  Google Scholar 

  25. Panait, L., Tuyls, K., Luke, S.: Theoretical advantages of lenient learners: an evolutionary game theoretic perspective. J. Mach. Learn. Res. 9, 423–457 (2008)

    MathSciNet  MATH  Google Scholar 

  26. Klos, T., Ahee, G.J.V., Tuyls, K.: Evolutionary dynamics of regret minimization. Technical report, 2010

  27. Wübben, et al.: Benefits and impact of cloud computing on 5g signal processing. In: IEEE Signal Processing Magazine, pp. 35–44, November 2014

  28. Desset, C., et al.: Flexible power modeling of LTE base stations. In: IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, April, 2012

  29. Xia, Y., Tse, D.: Inference of link delay in communication networks. IEEE J. Sel. Areas Commun. 24(12), 2235–2248 (2006)

    Article  Google Scholar 

  30. Ben Khalifa, N., et.al.: Random time delays in evolutionary game dynamics. In: Proceedings of IEEE CDC, Osaka, Japan, pp. 3840–3845

  31. Bernard, S., et al.: Sufficient conditions for stability of linear differential equations with distributed delay. Discrete Contin. Dyn. Syst. B 1(2), 233–256 (2001). https://doi.org/10.3934/dcdsb.2001.1.233

    Article  MathSciNet  MATH  Google Scholar 

  32. Baliga, J., et al.: Energy consumption in optical IP networks. J. Lightwave Technol. 27, 2391–2403 (2009)

    Article  Google Scholar 

  33. Platform Overview. https://www.opendaylight.org.

  34. Mininet Overview. https://mininet.org/overview/

  35. https://grnet.gr/infrastructure/network-and-topology/

  36. Student's t-distribution. https://en.wikipedia.org/wiki/Student%27s_t-distribution

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Acknowledgements

This work has been financially supported partly by a State Scholarships Foundation (IKY) scholarship and funded by the Act "Strengthening Human Resource Efficiency through the Implementation of Doctoral Research" from the resources of the OP "Human Resources Development, Education and Lifelong Learning," 2014–2020, the EU Horizon 2020 project 5G-COMPLETE under Grant Agreement No. 871900 and the EU Horizon 2020 project 5G-PICTURE under Grant Agreement No. 762057.

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Correspondence to Viktoria-Maria Alevizaki.

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Alevizaki, VM., Anastasopoulos, M., Tzanakaki, A. et al. Adaptive FH optimization in MEC-assisted 5G environments. Photon Netw Commun 40, 209–220 (2020). https://doi.org/10.1007/s11107-020-00906-8

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  • DOI: https://doi.org/10.1007/s11107-020-00906-8

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