A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning
-
摘要: 为优化软件定义网络(SDN)的路由选路，该文将深度增强学习原理引入到软件定义网络的选路过程，提出一种基于深度增强学习的路由优化选路机制，用以削减网络运行时延、提高吞吐量等网络性能，实现连续时间上的黑盒优化，减少网络运维成本。此外，该文通过实验对所提出的路由优化机制进行评估，实验结果表明，路由优化机制具有良好的收敛性与有效性，较传统路由协议可提供更优的路由方案与实现更稳定的性能。Abstract: In order to achieve routing optimization in the Software Defined Network (SDN) environment, deep reinforcement learning is imposed to the SDN routing process and a mechanism based on deep reinforcement learning is proposed to optimize routing. This mechanism can improve network performance such as delay, throughput, and realize black-box optimization in continuous time, which surely reduces network operation and maintenance costs. Besides, the proposed routing optimization mechanism is evaluated through a series of experiments. The experimental results show that the proposed SDN routing optimization mechanism has good convergence and effectiveness, and can provide better routing configurations and performance stability than traditional routing protocols.
-
BOUTABA R, SALAHUDDIN M A, LIMAM N, et al. A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities[J]. Journal of Internet Services and Applications, 2018, 9(1): 16. doi: 10.1186/s13174-018-0087-2 FADLULLAH Z M, TANG Fengxiao, MAO Bomin, et al. State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2432–2455. doi: 10.1109/COMST.2017.2707140 LI Wei, LI Guojun, and YU Xiufen. A fast traffic classification method based on SDN network[C]. The 4th International Conference on Electronics, Communications and Networks, Beijing, China, 2015: 223–229. WANG Fu, LIU Bo, ZHANG Lijia, et al. Dynamic routing and spectrum assignment based on multilayer virtual topology and ant colony optimization in elastic software-defined optical networks[J]. Optical Engineering, 2017, 56(7): 076111. doi: 10.1117/1.OE.56.7.076111 PARSAEI M R, MOHAMMADI R, and JAVIDAN R. A new adaptive traffic engineering method for telesurgery using ACO algorithm over Software Defined Networks[J]. European Research in Telemedicine, 2017, 6(3/4): 173–180. doi: 10.1016/j.eurtel.2017.10.003 WANG Junchao, DE LAAT C, and ZHAO Zhiming. QoS-aware virtual SDN network planning[C]. 2017 IFIP/IEEE Symposium on Integrated Network and Service Management, Lisbon, Portugal, 2017: 644–647. doi: 10.23919/INM.2017.7987350. LIN S C, AKYILDIZ I F, WANG Pu, et al. QoS-aware adaptive routing in multi-layer hierarchical software defined networks: a reinforcement learning approach[C]. 2016 IEEE International Conference on Services Computing, San Francisco, USA, 2016: 25–33. doi: 10.1109/SCC.2016.12. JIANG Jingyan, HU Liang, HAO Pingting, et al. Q-FDBA: Improving QoE fairness for video streaming[J]. Multimedia Tools and Applications, 2018, 77(9): 10787–10806. doi: 10.1007/s11042-017-4917-1 SUTTON R S and BARTO A G. Reinforcement Learning: An Introduction[M]. Cambridge, MA: The MIT Press, 1988. SENDRA S, REGO A, LLORET J, et al. Including artificial intelligence in a routing protocol using Software Defined Networks[C]. 2017 IEEE International Conference on Communications Workshops, Paris, France, 2017: 670–674. doi: 10.1109/ICCW.2017.7962735. MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529–533. doi: 10.1038/nature14236 LILLICRAP T P, HUNT J J, PRITZEL A, et al. Continuous control with deep reinforcement learning[P]. USA, Patent, WO2017019555, 2017. MESTRES A, RODRIGUEZ-NATAL A, CARNER J, et al. Knowledge-defined networking[J]. ACM SIGCOMM Computer Communication Review, 2017, 47(3): 2–10. doi: 10.1145/3138808.3138810 SILVER D, LEVER G, HEESS N, et al. Deterministic policy gradient algorithms[C]. International Conference on Machine Learning, Beijing, China, 2014: I-387–I-395. VARGA A and HORNIG R. An overview of the OMNeT++ simulation environment[C]. The 1st International Conference on Simulation Tools and Techniques for Communications, Networks and Systems & Workshops, Marseille, France, 2008: 60. ROUGHAN M. Simplifying the synthesis of internet traffic matrices[J]. ACM SIGCOMM Computer Communication Review, 2005, 35(5): 93–96. doi: 10.1145/1096536.1096551 PAN S J and YANG Qiang. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345–1359. doi: 10.1109/TKDE.2009.191 -
下载:
图(6)
计量
- 文章访问数: 5700
- HTML全文浏览量: 2196
- PDF下载量: 253
- 被引次数: 0
下载:
