Routing based on Reinforcement Learning for Software-Defined Networking
Abstract
Traditional routing protocols employ limited information to make routing decisions, leading to slow adaptation to traffic variability and restricted support to applications quality of service requirements. This paper introduces the work developed in the MSc. thesis entitled "Routing based on Reinforcement Learning for Software-Defined Networking", which defines routing approaches based on (deep) reinforcement learning. The results show that our solutions surpass routing algorithms based on Dijkstra as well as they are practical and feasible solutions for routing in Software-Defined Networking.
References
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.
Published
2021-08-16
How to Cite
VELASCO, Daniela Maria Casas; FONSECA, Nelson Luis Saldanha da; RENDÓN, Oscar Maurício Caicedo.
Routing based on Reinforcement Learning for Software-Defined Networking. In: DISSERTATION DIGEST - BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (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.
