Traffic-aware Routing with Software-defined Networks Using Reinforcement Learning and Fuzzy Logic


  • Shohreh Jaafari
  • Mohammad Nassiri
  • Reza Mohammadi



software-defined network, reinforcement learning, optimization of routing, delay, packet loss, bandwidth


In recent years, the idea of software-defined networks (SDNs) has been proposed for better network management. This architecture has succeeded in optimizing network management functions and increased the ability to synchronize network equipment. Currently, one of the major issues in this architecture is the routing of packets flowing in the network. The main aim in the routing of packets is to increase the quality of services. Enhancement of the quality and productivity of these networks will increase user satisfaction. To this end, the present study proposes a mechanism for selecting the best route from among several existing routes to direct a flow on such a network. The proposed method examines the network parameters including bandwidth, delay, and packet loss on each link of the route by using artificial intelligence algorithms and changes the parameters reducing network productivity by means of fuzzy logic. Our evaluations show that the proposed method can select routes with high productivity and increase the quality of services on the network. Receiving feedback and modifying the fuzzy membership functions related to each mentioned criterion can maintain the effect of these parameters on an acceptable level after which all transmissions tend towards the optimum. Given the use of reinforcement learning methods which underpin some of the routing methods in SDNs, the proposed idea may gradually contribute to the provision of optimized services on the network.


H.-N. Quach, S. Yoem, and K. Kim, “Survey on reinforcement learning based efficient routing in SDN,” Proceedings of the The 9th International Conference on Smart Media and Applications SMA’2020, September 2020, pp. 196–200.

M. Vafaei, A. Khademzadeh, and M. A. Pourmina, “A new QoS adaptive multi-path routing for video streaming in urban VANETs integrating ant colony optimization algorithm and fuzzy logic,” Wireless Personal Communications, vol. 118, pp. 2539-2572, 2021.

Q. Fu, E. Sun, K. Meng, M. Li, and Y. Zhang, “Deep Q-learning for routing schemes in SDN-based data center networks,” IEEE Access, vol. 8, pp. 103491-103499, 2020.

S. Torkzadeh, H. Soltanizadeh, and A. A. Orouji, “Energy-aware routing considering load balancing for SDN: a minimum graph-based ant colony optimization,” Cluster Computing, vol. 24, issue 3, pp. 2293-2312, 2021.

N. Feamster, J. Rexford, and E. Zegura, “The road to SDN: an intellectual history of programmable networks,” ACM SIGCOMM Computer Communication Review, vol. 44, no. 2, pp. 87-98, 2014.

B. A. A. Nunes, M. Mendonca, X.-N. Nguyen, K. Obraczka, and T. Turletti, “A survey of software-defined networking: Past, present, and future of programmable networks,” IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1617-1634, 2014.

S.-C. Lin, I. F. Akyildiz, P. Wang, and M. Luo, “QoS-aware adaptive routing in multi-layer hierarchical software defined networks: A reinforcement learning approach,” Proceedings of the 2016 IEEE International Conference on Services Computing (SCC), 2016, pp. 25-33.

I. F. Akyildiz, P. Wang, and S.-C. Lin, “SoftAir: A software defined networking architecture for 5G wireless systems,” Computer Networks, vol. 85, pp. 1-18, 2015.

A. Guo and C. Yuan, “Network intelligent control and traffic optimization based on SDN and artificial intelligence,” Electronics, vol. 10, no. 6, article 700, 2021.

G. Stampa, M. Arias, D. Sánchez-Charles, V. Muntés-Mulero, and A. Cabellos, “A deep-reinforcement learning approach for software-defined networking routing optimization,” arXiv preprint arXiv:1709.07080, 2017.

P. Jakma and D. Lamparter, “Introduction to the quagga routing suite,” IEEE Network, vol. 28, no. 2, pp. 42-48, 2014.

S. Sendra, A. Rego, J. Lloret, J. M. Jimenez, and O. Romero, “Including artificial intelligence in a routing protocol using software defined networks,” Proceedings of the 2017 IEEE International Conference on Communications Workshops (ICC Workshops), 2017, pp. 670-674.

H. Yang et al., “Time-aware software defined networking for OpenFlow-based datacenter optical networks,” Netw. Protoc. Algorithms, vol. 6, no. 4, pp. 77-91, 2014.

N. Wang, K. H. Ho, G. Pavlou, and M. Howarth, “An overview of routing optimization for internet traffic engineering,” IEEE Communications Surveys & Tutorials, vol. 10, no. 1, pp. 36-56, 2008.

M. K. Awad, M. H. H. Ahmed, A. F. Almutairi, and I. Ahmad, “Machine learning-based multipath routing for software defined networks,” Journal of Network and Systems Management, vol. 29, no. 2, pp. 1-30, 2021.

J. Xie et al., “A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 393-430, 2018.

L. El-Garoui, S. Pierre, and S. Chamberland, “A new SDN-based routing protocol for improving delay in smart city environments,” Smart Cities, vol. 3, no. 3, pp. 1004-1021, 2020.

S. Peng-hao, L. Ju-long, S. Juan, and H. Yu-xiang, “An intelligent routing technology based on deep reinforcement learning,” Acta Electonica Sinica, vol. 48, no. 11, p. 2170, 2020.

H. A. Al-Rawi, M. A. Ng, and K.-L. A. Yau, “Application of reinforcement learning to routing in distributed wireless networks: a review,” Artificial Intelligence Review, vol. 43, no. 3, pp. 381-416, 2015.

T. J. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, 2005.

H. El Alami and A. Najid, “SEFP: A new routing approach using fuzzy logic for clustered heterogeneous wireless sensor networks,” International Journal on Smart Sensing & Intelligent Systems, vol. 8, no. 4, pp. 2286-2306, 2015.

L. Zhao, Z. Bi, M. Lin, A. Hawbani, J. Shi, and Y. Guan, “An intelligent fuzzy-based routing scheme for software-defined vehicular networks,” Computer Networks, vol. 187, p. 107837, 2021.

R. Mohammadi, R. Javidan, and A. Jalili, “Fuzzy depth based routing protocol for underwater acoustic wireless sensor networks,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 7, no. 1, pp. 81-86, 2015.

S.-C. Lin, P. Wang, and M. Luo, “Jointly optimized QoS-aware virtualization and routing in software defined networks,” Computer Networks, vol. 96, pp. 69-78, 2016.

K. Kaur, J. Singh, and N. S. Ghumman, “Mininet as software defined networking testing platform,” Proceedings of the International Conference on Communication, Computing & Systems (ICCCS), 2014, pp. 139-42.

J. M. Jimenez, O. Romero, A. Rego, A. Dilendra, and J. Lloret, “Performance study of a software defined network emulator,” Proceedings of the Twelfth Advanced International Conference on Telecommunications (AICT 2016), 2016, pp. 17-22.




How to Cite

Jaafari, S., Nassiri, M., & Mohammadi, R. (2022). Traffic-aware Routing with Software-defined Networks Using Reinforcement Learning and Fuzzy Logic. International Journal of Computing, 21(3), 318-324.