Routing based on Reinforcement Learning for Software-Defined Networking
Resumo
Os protocolos de roteamento tradicionais empregam informações limitadas para tomar decisões de roteamento, levando a uma adaptação lenta à variabilidade do tráfego e ao suporte restrito aos requisitos de qualidade de serviço das aplicações. Este artigo apresenta o trabalho desenvolvido na tese de mestrado intitulada "Roteamento baseado em Aprendizado por Reforço para Redes Definidas por Software", que define duas abordagens de roteamento baseadas em aprendizagem (profunda) por reforço. Os resultados mostram que nossas soluções superam o desempenho dos algoritmos de roteamento baseados em Dijkstra bem como são soluções práticas e viáveis para roteamento em Redes Definidas por Software.
Referências
Casas-Velasco, D. M., Caicedo, O. M., and Da Fonseca, N. L. S. (2021). Routing based on (deep) reinforcement learning for software-defined networking. In https://github.com/danielaCasasv/DRSIR-DRL-routing-approach-for-SDN.
Chaudhary, S. and Johari, R. (2020). Oruml: Optimized routing in wireless networks using machine learning. International Journal of Communication Systems.
Gopi, D., Cheng, S., and Huck, R. (2017). Comparative analysis of sdn and conventional networks using routing protocols. In CITS, pages 108–112. IEEE.
Liao, L. and Leung, V. C. (2016). Lldp based link latency monitoring in software defined networks. In CNSM, pages 330–335. IEEE.
Mao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, O., Inoue, T., and Mizutani, K. (2017). Routing or computing? the paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Transactions on Computers, 66(11):1946–1960.
Martín, I., Troia, S., Hernández, J. A., Rodríguez, A., Musumeci, F., Maier, G., Alvizu, R., and de Dios, ´O. G. (2019). Machine learning-based routing and wavelength assignment in software-defined optical networks. IEEE Transactions on Network and Service Management, 16(3):871–883.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015). Human-level control through deep reinforcement learning. nature, 518(7540):529–533.
Serhani, A., Naja, N., and Jamali, A. (2020). Aq-routing: mobility-, stability-aware adaptive routing protocol for data routing in manet–iot systems. Cluster Computing, 23(1):13–27.
Shirmarz, A. and Ghaffari, A. (2020). An adaptive greedy ow routing algorithm for performance improvement in software-defined network. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 33(1).
Sutton, R. S. and Barto, A. G. (2018). Reinforcement learning: An introductionsecond edition, volume 1. Cambridge, Massachusetts.
Wang, C., Zhang, L., Li, Z., and Jiang, C. (2018). Sdcor: Software defined cognitive routing for internet of vehicles. IEEE Internet of Things Journal, 5(5):3513–3520.
Yu, C., Lan, J., Guo, Z., and Hu, Y. (2018). Drom: Optimizing the routing in softwaredened networks with deep reinforcement learning. IEEE Access, 6:64533–64539.
Chaudhary, S. and Johari, R. (2020). Oruml: Optimized routing in wireless networks using machine learning. International Journal of Communication Systems.
Gopi, D., Cheng, S., and Huck, R. (2017). Comparative analysis of sdn and conventional networks using routing protocols. In CITS, pages 108–112. IEEE.
Liao, L. and Leung, V. C. (2016). Lldp based link latency monitoring in software defined networks. In CNSM, pages 330–335. IEEE.
Mao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, O., Inoue, T., and Mizutani, K. (2017). Routing or computing? the paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Transactions on Computers, 66(11):1946–1960.
Martín, I., Troia, S., Hernández, J. A., Rodríguez, A., Musumeci, F., Maier, G., Alvizu, R., and de Dios, ´O. G. (2019). Machine learning-based routing and wavelength assignment in software-defined optical networks. IEEE Transactions on Network and Service Management, 16(3):871–883.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015). Human-level control through deep reinforcement learning. nature, 518(7540):529–533.
Serhani, A., Naja, N., and Jamali, A. (2020). Aq-routing: mobility-, stability-aware adaptive routing protocol for data routing in manet–iot systems. Cluster Computing, 23(1):13–27.
Shirmarz, A. and Ghaffari, A. (2020). An adaptive greedy ow routing algorithm for performance improvement in software-defined network. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 33(1).
Sutton, R. S. and Barto, A. G. (2018). Reinforcement learning: An introductionsecond edition, volume 1. Cambridge, Massachusetts.
Wang, C., Zhang, L., Li, Z., and Jiang, C. (2018). Sdcor: Software defined cognitive routing for internet of vehicles. IEEE Internet of Things Journal, 5(5):3513–3520.
Yu, C., Lan, J., Guo, Z., and Hu, Y. (2018). Drom: Optimizing the routing in softwaredened networks with deep reinforcement learning. IEEE Access, 6:64533–64539.
Publicado
16/08/2021
Como Citar
VELASCO, Daniela Maria Casas; FONSECA, Nelson Luis Saldanha da; RENDÓN, Oscar Maurício Caicedo.
Routing based on Reinforcement Learning for Software-Defined Networking. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 185-192.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2021.17170.
