Abstract
Knowledge-Defined networking (KDN) is a concept that relies on Software-Defined networking (SDN) and Machine Learning (ML) in order to operate and optimize data networks. Thanks to SDN, a centralized path calculation can be deployed, thus enhancing the network utilization as well as Quality of Services (QoS). QoS-aware routing problem is a high complexity problem, especially when there are multiple flows coexisting in the same network. Deep Reinforcement Learning (DRL) is an emerging technique that is able to cope with such complex problem. Recent studies confirm the ability of DRL in solving complex routing problems; however, its performance in the network with QoS-sensitive flows has not been addressed. In this paper, we exploit a DRL agent with convolutional neural networks in the context of KDN in order to enhance the performance of QoS-aware routing. The obtained results demonstrate that the proposed approach is able to improve the performance of routing configurations significantly even in complex networks.
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References
Akyildiz, I.F., Lee, A., Wang, P., Luo, M., Chou, W.: A roadmap for traffic engineering in SDN-OpenFlow networks. Comput. Netw. 71, 1–30 (2014). https://doi.org/10.1016/j.comnet.2014.06.002, http://www.sciencedirect.com/science/article/pii/S1389128614002254
Layeghy, S., Pakzad, F., Portmann, M.: SCOR: software-defined constrained optimal routing platform for SDN. CoRR abs/1607.03243 (2016). http://arxiv.org/abs/1607.03243
Clark, D.D., Partridge, C., Ramming, J.C., Wroclawski, J.T.: A knowledge plane for the internet. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 3–10. ACM (2003)
Wang, M., Cui, Y., Wang, X., Xiao, S., Jiang, J.: Machine learning for networking: workflow, advances and opportunities. IEEE Netw. 32(2), 92–99 (2018). https://doi.org/10.1109/MNET.2017.1700200
Valadarsky, A., Schapira, M., Shahaf, D., Tamar, A.: A machine learning approach to routing. CoRR abs/1708.03074 (2017). http://arxiv.org/abs/1708.03074
Boyan, J.A., Littman, M.L.: Packet routing in dynamically changing networks: a reinforcement learning approach. In: Advances in Neural Information Processing Systems, pp. 671–678 (1994)
Lin, S., Akyildiz, I.F., Wang, P., Luo, M.: QoS-aware adaptive routing in multi-layer hierarchical software defined networks: a reinforcement learning approach. In: 2016 IEEE International Conference on Services Computing (SCC), pp. 25–33, June 2016. https://doi.org/10.1109/SCC.2016.12
Li, Y.: Deep reinforcement learning: an overview. arXiv preprint arXiv:1701.07274 (2017)
Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.: A brief survey of deep reinforcement learning. CoRR abs/1708.05866 (2017). http://arxiv.org/abs/1708.05866
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529 (2015)
Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)
Stampa, G., Arias, M., Sanchez-Charles, D., Muntés-Mulero, V., Cabellos, A.: A deep-reinforcement learning approach for software-defined networking routing optimization. CoRR abs/1709.07080 (2017). http://arxiv.org/abs/1709.07080
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., Riedmiller, M.: Deterministic policy gradient algorithms. In: Proceedings of the 31st International Conference on International Conference on Machine Learning, ICML 2014, vol. 32, pp. I-387–I-395. JMLR.org (2014). http://dl.acm.org/citation.cfm?id=3044805.3044850
Sutton, R.S., Barto, A.G., et al.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
The internet topology zoo. http://www.topology-zoo.org/dataset.html
Varga, A.: Discrete event simulation system. In: Proceedings of the European Simulation Multiconference (2011)
Roughan, M.: Simplifying the synthesis of internet traffic matrices. SIGCOMM Comput. Commun. Rev. 35(5), 93–96 (2005). https://doi.org/10.1145/1096536.1096551
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
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Pham, T.A.Q., Hadjadj-Aoul, Y., Outtagarts, A. (2019). Deep Reinforcement Learning Based QoS-Aware Routing in Knowledge-Defined Networking. In: Duong, T., Vo, NS., Phan, V. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. Qshine 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 272. Springer, Cham. https://doi.org/10.1007/978-3-030-14413-5_2
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