S.V. Kozlov1, E.A. Spirina2, E.A. Nemtsev3, A.A. Spirina4

1–4 Kazan National Research Technical University named after A.N. Tupolev – KAI (Kazan, Russia)

1 SVkozlov@kai.ru, 2 EASpirina@kai.ru, 3 Egor.nemtcev.04@mail.ru, 4 Nastya_Sp_05@mail.ru

To increase the heterogeneous networks throughput by reducing the of intra-system interference flow, it is important to use the collective dynamic routing method. The simplest way to implement the collective dynamic routing method is to use the software-defined networks technology, which allows solving this problem by developing only software for the controller. At the same time, of the equipment resources on which the controller is implemented will limit this implementation option applicability. Therefore, an urgent task is to assess the resource intensity of the collective dynamic routing method implementation in software-defined heterogeneous networks.

As a work result, the computational complexity of implementing the algorithms for the analysis and routing stages of the collective dynamic routing method based on software-defined network technologies, the permissible the data transfer algorithm execution time, the implementing collective dynamic routing method resource intensity and the limitations on the performance of the software-defined network controller computing platforms without using and using a robust estimating the channel data rate method for various subnet segments were assessed variants. The obtained resource intensity estimates of the collective dynamic routing method implementation in software-defined heterogeneous networks show that when using a robust estimating the channel data rate method, the network controller can be implemented on virtually any server.

Kozlov S.V., Spirina E.A., Nemtsev E.A., Spirina A.A. Resource intensity the collective dynamic routing method implementation in software-defined heterogeneous networks. Electromagnetic waves and electronic systems. 2025. V. 30. № 5. P. 93−103. DOI: https://doi.org/10.18127/j15604128-202505-07 (in Russian)

1. Patent for invention RUS2784656 dated 29.11.2022. A method of joint dynamic routing in a communication network with packet message transmission. Kozlov S.V., Spirina E.A. (in Russian)
2. Spirina E.A., Kozlov S.V., Ismagilov E.A. Modification of the collective dynamic routing method in heterogeneous networks. Electromagnetic waves and electronic systems. 2024. V. 29. № 4. P. 96−107. DOI 10.18127/j15604128-202404-08. (in Russian)
3. Seyedebrahimi M., Bouhafs F., Raschellà A., Mackay M., Shi Q. SDN-based channel assignment algorithm for interference management in dense Wi-Fi networks. European Conference on Networks and Communications (EuCNC). Athens, Greece. 2016. P. 128–132. DOI 10.1109/EuCNC.2016.7561018.
4. P1930.1/D1. IEEE Draft Recommended Practice for Software Defined Networking (SDN) based Middleware for Control and Management of Wireless Networks. 2022. 136 p.
5. Costa-Requena J. SDN integration in LTE mobile backhaul networks. The International Conference on Information Networking. Phuket, Thailand. 2014. P. 264–269. DOI 10.1109/ICOIN.2014.6799479.
6. Rajalakshmi S., Deepika N., Srivardhini C.S., Vignesh A.C., Vignesh D.V. SDN Controller for LTE Networks. International Journal of Computer Applications. 2016. V. 133. № 3. P. 31–36. DOI 10.5120/ijca2016907782.
7. Irshad M.N., Du L., Khoso I.A., Javed T.B., Aslam M.M. A Hybrid Solution of SDN Architecture for 5G Mobile Communication to Improve Data Rate Transmission. 28th Wireless and Optical Communications Conference (WOCC). Beijing, China. 2019. P. 1–5. DOI 10.1109/ WOCC.2019.8770664.
8. Vintenkova Yu.S., Kozlov S.V. Computational Complexity Reduction of Valid Multidimensional Routes Construction Procedure in Collective Dynamic Routing. Vestnik of Volga State University of Technology. Ser.: Radio Engineering and Infocommunication Systems. 2019. № 2(42). P. 22–30. DOI 10.25686/2306-2819.2019.2.22. (in Russian)
9. Shevelev Yu.P. Discrete mathematics. Part 2: Theory of finite automata. Combinatorics. Graph theory. Tomsk: Tomsk State University of Control Systems and Radio Electronics. 2003. 130 p. (in Russian)
10. Vintenkova Yu.S. Optimization of information distribution in broadband radio access networks in conditions of limited computing resources: diss. … Candidate of Technical Sciences. Kazan: Kazan National Research Technical University named after A.N. Tupolev – KAI. 2019. 128 p. (in Russian)
11. Spirina E.A., Petrova E.A. A Robust Method for Estimating the Channel Data Rate in Seamless IEEE 802.11ax Standard Networks. Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). Kaliningrad, Russia. 2021. P. 1–5. DOI 10.1109/SYNCHROINFO51390.2021.9488341.
12. Kozlov S.V. Data Rate Estimation Method for Wi-Fi Networks Operating under Intra-system Interference Influence. Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). Svetlogorsk, Russia. 2020. P. 1–5. DOI 10.1109/SYNCHROINFO49631.2020.9166057.
13. Vintenkova Yu.S., Kozlov S.V., Spirina E.A. The estimation of data transfer rates in the broadband radio access networks with collective dynamic routing. Systems of Signal Synchronization, Generating and Processing in Telecommunications (SINKHROINFO). 2017. P. 1–4. DOI 10.1109/SINKHROINFO.2017.7997510.
14. Spirina E.A. Applying the Collective Dynamic Routing Method in IEEE 802.11ax Standard WI-FI Networks. Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). Svetlogorsk, Russia. 2020. P. 1–6. DOI 10.1109/SYNCHROINFO49631.2020.9166048.
15. 3GPP-Release 13 Analytical View Version. [Electronic resource] – Access mode: https://www.3gpp.org/release-13, date of reference 15.04.2025.
16. TOP500 June 2025. [Electronic resource] – Access mode: https://top500.org/lists/top500/2025/06/, date of reference 15.04.2025.
17. CPU Benchmarks. [Electronic resource] – Access mode: https://www.cpubenchmark.net/, date of reference 15.04.2025.