DINAMIK MARSHRUTLASH PROTOKOLINI SUN’IY INTELLEKT YORDAMIDA OPTIMALLASHTIRISH ISTIQBOLLARI

Авторы

  • Kamborov K.N. Toshkent amaliy fanlar universiteti kompuyter injiniringi kafedrasi dotsenti Автор
  • Muxammadiyev M.M. Toshkent amaliy fanlar universiteti kompuyter injiniringi kafedrasi asisenti Автор

DOI:

https://doi.org/10.65164/g4qfpf75

Ключевые слова:

marshrutlash protokollari, sun‘iy intellekt, chuqur o‘rganish, mashinali o‘rganish, SDN, OSPF, BGP, neyron tarmoqlar, tarmoq optimizatsiyasi.

Аннотация

Ushbu maqolada kompyuter tarmoqlarida marshrutlash protokollarini sun‘iy intellekt usullari yordamida boshqarish masalalari ko‘rib chiqilgan. Zamonaviy tarmoqlarda an‘anaviy marshrutlash protokollarining (OSPF, BGP, RIP) cheklanishlarini bartaraf etish maqsadida mashinaviy o‘rganish, chuqur o‘rganish va mustahkamlovchi o‘rganish usullarini qo‘llash imkoniyatlari tahlil qilingan. Maqolada DQN algoritmining marshrutlash masalalariga tatbiq etilishi, shuningdek SDN (Software-Defined Networking) bilan integratsiya muammolari yoritilgan.

Библиографические ссылки

[1]. Moynihan, T., & Russell, A. OSPF: Anatomy of an Internet Routing Protocol. Addison-Wesley, 2015.

[2]. Sutton, R. S., & Barto, A. G. Reinforcement Learning: An Introduction. MIT Press, 2018.

[3]. Geng, Y., et al. “AI-driven network routing optimization: A survey”. IEEE Communications Surveys & Tutorials, 2020.

[4]. Mamatov, E., & Jo‘rayev, A. Kompyuter tarmoqlari va marshrutlash asoslari. Toshkent, 2020.

[5]. Власов, В. Алгоритмы маршрутизации в компьютерных сетях. Санкт-Петербург, 2017.

1619

[6]. Lu, T., Li, W., & Chen, J. (2020). Deep Learning for Network Traffic Prediction and Control: A Survey. ACM Computing Surveys (CSUR), 53(1), 1-38.

[7] McMahan H.B. et al. “Communication-efficient learning of deep networks from decentralized data”. Proceedings of AISTATS 2017, vol. 54, pp. 1273-1282.

[8] Konecny J. et al. “Federated learning: Strategies for improving communication efficiency”. arxiv preprint arxiv:1610.05492, 2016.

[9] Pan S.J., Yang Q. “A survey on transfer learning”. IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010.

[10] Farhi E. et al. “A quantum approximate optimization algorithm”. arxiv preprint arxiv:1411.4028, 2014.

[11] McKeown N. et al. “OpenFlow: Enabling innovation in campus networks”. ACM SIGCOMM Computer Communication Review, vol. 38, no. 2, pp. 69-74, 2008.

[12] Stampa G. et al. “A deep-reinforcement learning approach for software-defined networking routing optimization”. arxiv preprint arxiv:1709.07080, 2017.

[13] Mestres A. et al. “Knowledge-defined networking”. ACM SIGCOMM Computer Communication Review, vol. 47, no. 3, pp. 2-10, 2017.

[14]. Lu, T., Li, W., & Chen, J. (2020). Deep Learning for Network Traffic Prediction and Control: A Survey. ACM Computing Surveys (CSUR), 53(1), 1-38.

Опубликован

2026-05-15