CFR-RL: Traffic Engineering With Reinforcement Learning in SDN

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of...

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Veröffentlicht in:	IEEE journal on selected areas in communications 2020-10, Vol.38 (10), p.2249-2259
Hauptverfasser:	Zhang, Junjie, Ye, Minghao, Guo, Zehua, Yen, Chen-Yu, Chao, H. Jonathan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Control systems Critical flow Engineering education Heuristic algorithms Heuristic methods Learning Linear programming load balancing network disturbance mitigation Optimization Quality of service Reinforcement learning Routing Software-defined networking Solution space Traffic control Traffic engineering Traffic flow
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