Optimization of border gateway routing protocol with Lagrange multiplier and gradient descent integration for network

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.326561

Keywords:

BGP, connection stability, routing, machine learning, Lagrange multiplier, gradient descent

Abstract

This study has a research object, namely data transmission lines. In this study, there are problems that must be solved related to the optimization of network transmission routes that are dynamic and adaptive to changes in real-time conditions, including latency factors, connection stability, and algorithm integration that can accommodate large-scale network needs efficiently in terms of transmission. The results obtained from this study are in the form of a model that can identify route management and optimize the border gateway protocol. The results of the study show that the application of this method can optimize the transmission path by considering network constraints and real-time condition dynamics. This study has an interpretation that the proposed model is proven to be effective in improving network performance, with increased efficiency, reduced constraints, and the ability to adapt to changes in network conditions. This is evidenced by the accuracy in the form of quantitative effectiveness by producing 95 % accuracy with the Reinforcement Learning model, able to significantly increase efficiency and accuracy compared to traditional methods in BGP routing optimization. The characteristics contained in this study include the ability to manage and identify transmission routes to improve network efficiency, reduce latency, increase throughput, minimize the number of hops in managing BGP transmission routes. There are limitations related to input data processing that require deeper annotation. This study contributes to BGP route optimization with machine learning algorithms that can be applied in complex and dynamic networks

Author Biographies

Ferry Fachrizal, Politeknik Negeri Medan

Master of Computer

Department of Computer Science

Okvi Nugroho, Universitas Muhammadiyah Sumatera Utara

Master of Computer

Department of Information Technology

Al-khowarizmi Al-khowarizmi, Universitas Muhammadiyah Sumatera Utara

Doctor of Computer Science

Department of Information Technology

References

  1. Shahid, K., Ahmad, S. N., Rizvi, S. T. H. (2024). Optimizing Network Performance: A Comparative Analysis of EIGRP, OSPF, and BGP in IPv6-Based Load-Sharing and Link-Failover Systems. Future Internet, 16 (9), 339. https://doi.org/10.3390/fi16090339
  2. Mastilak, L., Helebrandt, P., Galinski, M., Kotuliak, I. (2022). Secure Inter-Domain Routing Based on Blockchain: A Comprehensive Survey. Sensors, 22 (4), 1437. https://doi.org/10.3390/s22041437
  3. Scott, B. A., Johnstone, M. N., Szewczyk, P. (2024). A Survey of Advanced Border Gateway Protocol Attack Detection Techniques. Sensors, 24 (19), 6414. https://doi.org/10.3390/s24196414
  4. Djenna, A., Harous, S., Saidouni, D. E. (2021). Internet of Things Meet Internet of Threats: New Concern Cyber Security Issues of Critical Cyber Infrastructure. Applied Sciences, 11 (10), 4580. https://doi.org/10.3390/app11104580
  5. Romo-Chavero, M. A., Cantoral-Ceballos, J. A., Pérez-Díaz, J. A., Martinez-Cagnazzo, C. (2024). Median Absolute Deviation for BGP Anomaly Detection. Future Internet, 16 (5), 146. https://doi.org/10.3390/fi16050146
  6. Gupta, C., Johri, I., Srinivasan, K., Hu, Y.-C., Qaisar, S. M., Huang, K.-Y. (2022). A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks. Sensors, 22 (5), 2017. https://doi.org/10.3390/s22052017
  7. Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., Liljeberg, P. (2018). Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems, 78, 641–658. https://doi.org/10.1016/j.future.2017.02.014
  8. Wu, Y., Wu, Y., Guerrero, J. M., Vasquez, J. C. (2021). A comprehensive overview of framework for developing sustainable energy internet: From things-based energy network to services-based management system. Renewable and Sustainable Energy Reviews, 150, 111409. https://doi.org/10.1016/j.rser.2021.111409
  9. Zhao, X., Band, S. S., Elnaffar, S., Sookhak, M., Mosavi, A., Salwana, E. (2021). The Implementation of Border Gateway Protocol Using Software-Defined Networks: A Systematic Literature Review. IEEE Access, 9, 112596–112606. https://doi.org/10.1109/access.2021.3103241
  10. Weitz, K., Woos, D., Torlak, E., Ernst, M. D., Krishnamurthy, A., Tatlock, Z. (2016). Scalable verification of border gateway protocol configurations with an SMT solver. Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications, 765–780. https://doi.org/10.1145/2983990.2984012
  11. Sharma, S., Kang, D. H., Montes de Oca, J. R., Mudgal, A. (2021). Machine learning methods for commercial vehicle wait time prediction at a border crossing. Research in Transportation Economics, 89, 101034. https://doi.org/10.1016/j.retrec.2021.101034
  12. Koyuncu, H., Tomar, G. S., Sharma, D. (2020). A New Energy Efficient Multitier Deterministic Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks. Symmetry, 12 (5), 837. https://doi.org/10.3390/sym12050837
  13. Shah, P., Kasbe, T. (2021). A review on specification evaluation of broadcasting routing protocols in VANET. Computer Science Review, 41, 100418. https://doi.org/10.1016/j.cosrev.2021.100418
  14. Krisnawijaya, N. N. K., Paramartha, C. R. A. (2016). Penerapan jaringan multihoming pada jaringan komputer fakultas hukum. ILMU KOMPUTER, 9 (1), 23–31.
  15. Zhou, Q., Pezaros, D. (2020). A Prediction-Based Model for Consistent Adaptive Routing in Back-Bone Networks at Extreme Situations. Electronics, 9 (12), 2146. https://doi.org/10.3390/electronics9122146
  16. Dai, B., Cao, Y., Wu, Z., Dai, Z., Yao, R., Xu, Y. (2021). Routing optimization meets Machine Intelligence: A perspective for the future network. Neurocomputing, 459, 44–58. https://doi.org/10.1016/j.neucom.2021.06.093
  17. Song, Y., Liu, Z., Li, K., He, X., Zhu, W. (2024). Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning. Electronics, 13 (23), 4746. https://doi.org/10.3390/electronics13234746
  18. Dafhalla, A. K. Y., Elobaid, M. E., Tayfour Ahmed, A. E., Filali, A., SidAhmed, N. M. O., Attia, T. A. et al. (2025). Computer-Aided Efficient Routing and Reliable Protocol Optimization for Autonomous Vehicle Communication Networks. Computers, 14 (1), 13. https://doi.org/10.3390/computers14010013
  19. Cosovic, M., Obradovic, S., Junuz, E. (2018). Deep Learning for Detection of BGP Anomalies. Time Series Analysis and Forecasting, 95–113. https://doi.org/10.1007/978-3-319-96944-2_7
  20. Jabbar, W. A., Ismail, M., Nordin, R., Arif, S. (2016). Power-efficient routing schemes for MANETs: a survey and open issues. Wireless Networks, 23 (6), 1917–1952. https://doi.org/10.1007/s11276-016-1263-6
  21. Fronza, I., Sillitti, A., Succi, G., Terho, M., Vlasenko, J. (2013). Failure prediction based on log files using Random Indexing and Support Vector Machines. Journal of Systems and Software, 86 (1), 2–11. https://doi.org/10.1016/j.jss.2012.06.025
  22. Avgerinou, M., Bertoldi, P., Castellazzi, L. (2017). Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency. Energies, 10 (10), 1470. https://doi.org/10.3390/en10101470
Optimization of border gateway routing protocol with Lagrange multiplier and gradient descent integration for network

Downloads

Published

2025-04-29

How to Cite

Fachrizal, F., Nugroho, O., & Al-khowarizmi, A.- khowarizmi. (2025). Optimization of border gateway routing protocol with Lagrange multiplier and gradient descent integration for network. Eastern-European Journal of Enterprise Technologies, 2(9 (134), 6–13. https://doi.org/10.15587/1729-4061.2025.326561

Issue

Section

Information and controlling system