Load balancing algorithm for service traffic in the virtualized infrastructure of next generation mobile networks


DOI: 10.34759/trd-2023-128-11

Аuthors

Buzhin I. G.*, Antonova V. M.**, Mironov Y. B.***, Gaifutdinov E. A.****

Moscow Technical University of Communications And Informatics, 8a, Aviamotornaya Str., Moscow, 111024, Russia

*e-mail: i.g.buzhin@mtuci.ru
**e-mail: xarti@mail.ru
***e-mail: i.b.mironov@mtuci.ru
****e-mail: e.a.gaifutdinov@mtuci.ru

Abstract

One of the key features that distinguishes future generation mobile networks (5G and 6G) from the previous generations is an increase in the number of services provided, a significant increase in data transmission speeds and a highly reliable control loop. The architecture of such networks is being built employing Software Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, as well as Network Slicing technology.

It is necessary to develop messages load balancing algorithm of the virtualized infrastructure between the control devices to ensure the high fault tolerance level of control, load balancing, network connexity of mobile network architecture of future generation mobile network by the SDN/NFV technology employing.

All SDN switches should be necessarily divided into the groups depending on their output loading so that the common loading from one group of switches does not exceed the SDN controller productive capacity for the load distribution (Packet-In messages received by the controller per second) from the virtualized infrastructure based on the SDN/NFV controllers.

This requires permanent load measuring on each of the controllers. The main challenge is the load-balancing task, i.e. the SDN switches allocation over controllers. The network topology can be plotted as a graph, where the nodes represent the SDN switches (virtualized infrastructure nodes) with the rated load about network states and the controllers with the rated data flow processing capacity, while the graph edges represent the data links.

Let us decompose the task into two subtasks; each of them herewith is being solved by a different algorithm. The first algorithm has to divide the switches into groups based on the information on the current loads and flows in the system, so that the groups can be assigned to the controllers employed in the system. The second algorithm should allocate the groups resulting from the first algorithm running to the employed controllers so that the minimum number of migrations would be required to convert the considered system. The algorithm for Packet-In messages allocating to controllers is new and has no equivalents for comparison.

The algorithm results in groups of virtualized infrastructure nodes with the total service traffic load not exceeding the maximum performance of the control loop nodes.

Keywords:

future generation mobile networks, load balancing, network layers, network connectivity, virtualised infrastructure, data networks

References

  1. Lin Y.B., Tseng C.C., Wang M.H. Effects of Transport Network Slicing on 5G Applications, Future Internet, 2021, vol. 13, no. 69. URL: https://doi.org/10.3390/fi13030069
  2. Tsai C.C., Lin F.J., Tanaka H. Evaluation of 5G Core Slicing on User Plane Function, Communications and Network, 2021, vol. 13, pp. 79-92. URL: https://doi.org/10.4236/cn.2021.133007.
  3. Camps-Aragó, S. Delaere, P. Ballon. 5G business models: Evolving mobile network operator roles in new ecosystems, Conference: Smart Cities & Information and Communication Technology (CTTE-FITCE), 2019. DOI:10.1109/CTTE-FITCE.2019.8894822
  4. Li, X. Wang, T. Zhang. System architecture and technological basics of 5G, Chap. 4 in 5G+ How 5G Change the Society, Springer, 2021.
  5. Wijethilaka, M. Liyanage. Survey on network slicing for Internet of things realization in 5G networks, IEEE Communications Surveys & Tutorials, 2021, vol. 23, no. 2, pp. 957–994. DOI:10.1109/COMST.2021.3067807
  6. Borcoci E. et al. An Overview of 5G Slicing Operational Business Models for Internet of Vehicles, Maritime IoT Applications and Connectivity Solutions, IEEE Access, 2021. DOI:10.1109/ACCESS.2021.3128496
  7. Xue H., Kim K.T., Youn H.Y. Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization, Sensors, 2019, vol. 19, pp. 311.
  8. DOI:10.3390/s19020311
  9. Cui X., Huang X., Ma Y., Meng Q. A load balancing routing mechanism based on SDWSN in smart city, Electronics, 2019, vol. 8, no. 273. DOI:10.3390/electronics8030273
  10. Semong T., Maupong T., Anokye S., Kehulakae K., Dimakatso S., Boipelo G., Sarefo S. Intelligent load balancing techniques in software defined networks: A survey, Electronics, 2020, vol. 9 (7), pp. 1091.
  11. Bakhtin A.A., Volkov A.S., Solodkov A.V., Baskakov A.E. Trudy MAI, 2021, no. 117. URL: https://trudymai.ru/eng/published.php?ID=122307. DOI: 10.34759/trd-2021-117-07
  12. Volkov A.S., Baskakov A.E. Trudy MAI, 2021, no. 118. URL: https://trudymai.ru/eng/published.php?ID=158240. DOI: 10.34759/trd-2021-118-07
  13. Principles and Practices for Securing Software-Defined Networks. ONF TR-511 Open Networking Foundation, 2015. URL: https://pdfslide.net/documents/principles-and-practices-for-securing-software-defined-networks.html
  14. Salman O. et al. Multi-level security for the 5G/IoT ubiquitous network, 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, 2017, pp. 188-193. DOI:10.1109/FMEC.2017.7946429.
  15. Buzhin I.G., Antonova V.M., Mironov Yu.B., Antonova V.A., Korchagina A.S, Kanishcheva M.G. Trudy MAI, 2021, no. 121. URL: https://trudymai.ru/eng/published.php?ID=162659. DOI: 10.34759/trd-2021-121-12.
  16. Masyukov I.I. Trudy MAI, 2021, no. 120. URL: https://trudymai.ru/eng/published.php?ID=161427. DOI: 10.34759/trd-2021-120-13.
  17. Antonova V.M., Zakhir B.M., Kuznetsov N.A. Informatsionnye protsessy, 2019, vol. 19, no. 2, pp. 159-169.
  18. Buranova M.A., Kartashevskii V.G., Mutkhanna A.S. Elektrosvyaz’, 2022, no. 4, pp. 2-7. DOI: 10.34832/ELSV.2022.29.4.001
  19. Perepelkin D.A., Nguen V.T. Vestnik Ryazanskogo gosudarstvennogo radiotekhnicheskogo universiteta, 2021, no. 77, pp. 43-57.
  20. Kalmykov N.S., Dokuchaev V.A. T-Comm-Telekommunikatsii i transport, 2021, vol. 15, no. 7, pp. 50-54.
  21. Volkov A.S., Baskakov A.E. T-Comm-Telekommunikatsii i transport, 2021, vol. 15, no. 9, pp. 17-23.

Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

Вход