Abstract
The rapid development of computer hardware and software provides a suitable platform for machine learning, in which deep learning has become a breakthrough in machine learning technology in various fields in many disciplines. Some recent research efforts have focused on routing control based on deep learning. Therefore, this paper studies the problem of intelligent routing, and aims to propose an intelligent control strategy based on deep learning with the help of Software-Defined Network (SDN) and other new network technologies. The characteristics of SDN network that can easily obtain the network topology have laid the foundation for selecting different routing paths according to the different QoS levels of the flow. Nowadays, the routing modules in commonly used SDN controllers use the shortest path algorithm which is simple to implement and works effectively. However, the best path calculated by controllers may suffer from huge traffic load and result in congestion, and the controllers cannot learn from the previous experiences to intelligently switch to other paths. This paper present intelligent routing control strategy based on Deep Q-Learning (DQN) in SDN, which uses the Openflow to collect information from the network, and aggregates them to the SDN controller, and then uses DQN to generate the specific routing for forwarders.
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References
Benzekki, K., Fergougui, A.E., Elalaoui, A.E.: Software-Defined Networking (SDN): a survey. Secur. Commun. Netw. 9(18), 5803–5833 (2016). https://doi.org/10.1002/sec.1737
Bosshart, P., et al.: Forwarding metamorphosis: fast programmable match-action processing in hardware for SDN. In: Chiu, D.M., Wang, J., Barford, P., Seshan, S. (eds.) ACM SIGCOMM 2013 Conference, SIGCOMM 2013, Hong Kong, China, 12–16 August 2013, pp. 99–110. ACM (2013). https://doi.org/10.1145/2486001.2486011
Chica, J.C.C., Imbachi, J.C., Botero, J.F.: Security in SDN: a comprehensive survey. J. Netw. Comput. Appl. 159, 102595 (2020). https://doi.org/10.1016/j.jnca.2020.102595
Fu, Q., Sun, E., Kang, M., Li, M., Zhang, Y.: Deep q-learning for routing schemes in SDN-based data center networks. IEEE Access 8, 103491–103499 (2020). https://doi.org/10.1109/ACCESS.2020.2995511
Guo, X., Lin, H., Li, Z., Peng, M.: Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IOT. IEEE Internet Things J. 7(7), 6242–6251 (2020). https://doi.org/10.1109/JIOT.2019.2960033
Henni, D., Ghomari, A., Aoul, Y.H.: A consistent QoS routing strategy for video streaming services in SDN networks. Int. J. Commun. Syst. 33(10) (2020). https://doi.org/10.1002/dac.4177
Liu, Z., Zhu, J., Zhang, J., Liu, Q.: Routing algorithm design of satellite network architecture based on SDN and ICN. Int. J. Satellite Commun. Netw. 38(1), 1–15 (2020). https://doi.org/10.1002/sat.1304
Lu, Y.-H., Leu, F.-Y.: Dynamic routing and bandwidth provision based on reinforcement learning in SDN networks. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) AINA 2020. AISC, vol. 1151, pp. 1–11. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44041-1_1
Mao, B., et al.: Routing or computing? the paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Trans. Comput. 66(11), 1946–1960 (2017). https://doi.org/10.1109/TC.2017.2709742
Tang, F., et al.: On removing routing protocol from future wireless networks: a real-time deep learning approach for intelligent traffic control. IEEE Wirel. Commun. 25(1), 154–160 (2018). https://doi.org/10.1109/MWC.2017.1700244
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Jia, J., Fu, Y., Zhang, G., Liang, X., Xu, P. (2021). The Intelligent Routing Control Strategy Based on Deep Learning. In: Li, B., Li, C., Yang, M., Yan, Z., Zheng, J. (eds) IoT as a Service. IoTaaS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-67514-1_46
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