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Traffic Engineering Based on Deep Reinforcement Learning in Hybrid IP/SR Network 被引量:1
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作者 Bo Chen Penghao Sun +3 位作者 Peng Zhang Julong Lan Youjun Bu Juan Shen 《China Communications》 SCIE CSCD 2021年第10期204-213,共10页
Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.How... Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.However,upgrading current IP network to a full SR one can be costly and difficult.Hybrid IP/SR network will last for some time.Aiming at the low flexibility problem of static TE policies in the current SR networks,this paper proposes a Deep Reinforcement Learning(DRL)based TE scheme.The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution.As a result,the network congestion can be mitigated and the performance of the network is improved.Simulation results show that our proposed scheme can improve the throughput of the network by up to 9%than existing schemes. 展开更多
关键词 SDN deep reinforcement learning segment routing traffic engineering equal cost multiple paths
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