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基于深度增強學習的軟件定義網絡路由優(yōu)化機制

蘭巨龍 于倡和 胡宇翔 李子勇

蘭巨龍, 于倡和, 胡宇翔, 李子勇. 基于深度增強學習的軟件定義網絡路由優(yōu)化機制[J]. 電子與信息學報, 2019, 41(11): 2669-2674. doi: 10.11999/JEIT180870
引用本文: 蘭巨龍, 于倡和, 胡宇翔, 李子勇. 基于深度增強學習的軟件定義網絡路由優(yōu)化機制[J]. 電子與信息學報, 2019, 41(11): 2669-2674. doi: 10.11999/JEIT180870
Julong LAN, Changhe YU, Yuxiang HU, Ziyong LI. A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2669-2674. doi: 10.11999/JEIT180870
Citation: Julong LAN, Changhe YU, Yuxiang HU, Ziyong LI. A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2669-2674. doi: 10.11999/JEIT180870

基于深度增強學習的軟件定義網絡路由優(yōu)化機制

doi: 10.11999/JEIT180870
基金項目: 國家自然科學基金群體創(chuàng)新項目(61521003),國家自然科學基金(61502530)
詳細信息
    作者簡介:

    蘭巨龍:男,1962年生,教授,博士生導師,主要研究方向為新型網絡體系結構與網絡安全

    于倡和:男,1993年生,碩士,研究方向為新型網絡體系結構與網絡安全

    通訊作者:

    于倡和 yu_changhe@hotmail.com

  • 中圖分類號: TP393

A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning

Funds: The National Natural Science Foundation of China for Innovative Research Groups (61521003), The National Natural Science Foundation of China (61502530)
  • 摘要: 為優(yōu)化軟件定義網絡(SDN)的路由選路,該文將深度增強學習原理引入到軟件定義網絡的選路過程,提出一種基于深度增強學習的路由優(yōu)化選路機制,用以削減網絡運行時延、提高吞吐量等網絡性能,實現連續(xù)時間上的黑盒優(yōu)化,減少網絡運維成本。此外,該文通過實驗對所提出的路由優(yōu)化機制進行評估,實驗結果表明,路由優(yōu)化機制具有良好的收斂性與有效性,較傳統(tǒng)路由協(xié)議可提供更優(yōu)的路由方案與實現更穩(wěn)定的性能。
  • 圖  1  加裝機器學習機制的SDN網絡架構

    圖  2  DDPG的訓練運行框架

    圖  3  DDPG優(yōu)化SDN路由選路的框架設計

    圖  4  不同流量強度下網絡的時延隨訓練步數的變化

    圖  5  DDPG智能體與隨機路由對比

    圖  6  DDPG與OSPF的網絡運行時延對比

  • 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
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出版歷程
  • 收稿日期:  2018-09-06
  • 修回日期:  2019-05-12
  • 網絡出版日期:  2019-05-27
  • 刊出日期:  2019-11-01

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