Abstract
6G/IMT-2030 is designed to provide users with innovative speeds of terabit per second, which are proposed to be achieved using a number of advanced technologies, such as Mobile Edge Computing (MEC), Internet of Things (IoT), millimeter wave (mmWave), new radio and software defined networking. It is necessary to solve several important aspects in order to satisfy Quality of Service (QoS), first of all, to ensure network coverage density even in sparsely populated areas. In this paper we proposed software defined network based mobile edge computing dynamic algorithm for improving network performance. In addition, this algorithm can help the service provided to adapt with a required load on the radio links. Furthermore, local content caching and Local Internet Breakout (LIB) can be utilized to reduce the transport network requirements. Finally, the proposed algorithm is analyzed using some use cases and we developed testbed to emulate operator infrastructure.
The publication has been prepared with the support of the “RUDN University Program 5-100” (recipient K. Samouylov). For the research, infrastructure of the 5G Lab RUDN (Russia) was used.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Muthanna, A., et al.: Secure and reliable IoT networks using fog computing with software-defined networking and blockchain. J. Sens. Actuator Netw. 8(1), 15 (2019)
Ateya, A.A., Muthanna, A., Vybornova, A., Darya, P., Koucheryavy, A.: Energy - aware offloading algorithm for multi-level cloud based 5G system. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds.) NEW2AN/ruSMART -2018. LNCS, vol. 11118, pp. 355–370. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01168-0_33
Khayyat, M., Alshahrani, A., Alharbi, S., Elgendy, I., Paramonov, A., Koucheryavy, A.: Multilevel service-provisioning-based autonomous vehicle applications. Sustainability 12(6), 2497 (2020)
Jaiswal, R.K., Jaidhar, C.: Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter. Wireless Netw. 23(7), 2021–2036 (2017). https://doi.org/10.1007/s11276-016-1265-4
Ateya, A.A., et al.: Model mediation to overcome light limitations-toward a secure tactile internet system. J. Sens. Actuator Netw. 8(1), 6 (2019)
Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Energy saving technology of 5g base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020)
Nørgaard, B., Guerra, A.: Engineering 2030: conceptualization of industry 4.0 and its implications for engineering education. In: 7th International Research Symposium on PBL, p. 34 (2018)
Aijaz, A., Simsek, M., Dohler, M., Fettweis, G.: Shaping 5G for the tactile internet. In: Xiang, W., Zheng, K., Shen, X.S. (eds.) 5G Mobile Communications, pp. 677–691. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-34208-5_25
Daraseliya, A.V., Sopin, E.S., Samuylov, A.K., Shorgin, S.Y.: Comparative analysis of the mechanisms for energy efficiency improving in cloud computing systems. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds.) NEW2AN/ruSMART -2018. LNCS, vol. 11118, pp. 268–276. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01168-0_25
Parvez, I., Rahmati, A., Guvenc, I., Sarwat, A.I., Dai, H.: A survey on low latency towards 5G: RAN, core network and caching solutions. IEEE Commun. Surv. Tutor. 20(4), 3098–3130 (2018)
Alvarez, F., et al.: An edge-to-cloud virtualized multimedia service platform for 5G networks. IEEE Trans. Broadcast. 65(2), 369–380 (2019)
Carlin, A., Hammoudeh, M., Aldabbas, O.: Defence for distributed denial of service attacks in cloud computing. Procedia Comput. Sci. 73 (2015). https://doi.org/10.1016/j.procs.2015.12.037
Ericsson mobility report: on the pulse of the networked society. http://www.abc.es/gestordocumental/uploads/internacional/EMR-June-2016-D5201.pdf
Cisco, C.V.N.I.: Global mobile data traffic forecast update, 2016–2021, white paper, pp. 0018–9545 (2017)
Sopin, E.S., Daraseliya, A.V., Correia, L.M.: Performance analysis of the offloading scheme in a fog computing system, pp. 1–5 (2018)
Palola, M., et al.: Live field trial of licensed shared access (LSA) concept using LTE network in 2.3 GHz band. In: 2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN), pp. 38–47. IEEE (2014)
Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.-C., Hanzo, L.: Machine learning paradigms for next-generation wireless networks. IEEE Wirel. Commun. 24(2), 98–105 (2016)
Daraseliya, A., Sopin, E., Rykov, V.: On optimization of energy consumption in cloud computing system, October 2018
Kato, N., et al.: The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wirel. Commun. 24(3), 146–153 (2016)
Le, L.-V., Lin, B.-S., Do, S.: Applying big data, machine learning, and SDN/NFV for 5G early-stage traffic classification and network QoS control. Trans. Netw. Commun. 6(2), 36 (2018)
Le, L.-V., Sinh, D., Tung, L.-P., Lin, B.-S.P.: A practical model for traffic forecasting based on big data, machine-learning, and network KPIs. In: 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1–4. IEEE (2018)
Kumar, P.M., Manogaran, G., Sundarasekar, R., Chilamkurti, N., Varatharajan, R., et al.: Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput. Netw. 144, 154–162 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Khakimov, A., Muthanna, A., Elgendy, I.A., Samouylov, K. (2020). Dynamic Algorithm for Building Future Networks Based on Intelligent Core Network. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds) Distributed Computer and Communication Networks. DCCN 2020. Lecture Notes in Computer Science(), vol 12563. Springer, Cham. https://doi.org/10.1007/978-3-030-66471-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-66471-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66470-1
Online ISBN: 978-3-030-66471-8
eBook Packages: Computer ScienceComputer Science (R0)