Research on deep reinforcement learning multi-path routing planning in SDN
Based on the research of current SDN traffic scheduling technology, combining the advantages of reinforcement learning in strategy optimization and the characteristics of SDN network resource centralized control, and using the neural network of depth learning to fit the Q-value table, this paper pro...
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| Veröffentlicht in: | Journal of physics. Conference series 2020-08, Vol.1617 (1), p.12043 |
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| creator | Wang, Zheng Lu, Zhengyong Li, Chuanhuang |
| description | Based on the research of current SDN traffic scheduling technology, combining the advantages of reinforcement learning in strategy optimization and the characteristics of SDN network resource centralized control, and using the neural network of depth learning to fit the Q-value table, this paper proposes an intelligent multi-path routing planning method based on depth reinforcement learning. Experiments show that the method can use the characteristics of SDN to route the network traffic in multi-path according to the current state information and traffic characteristics of the network; using the advantages of reinforcement learning, it can find multiple forwarding paths for different flows that conform to their traffic characteristics, and improve the utilization rate of the network link bandwidth; and using the neural network of deep learning to fit the Q value in the traditional reinforcement learning algorithm Table. |
| doi_str_mv | 10.1088/1742-6596/1617/1/012043 |
| format | Article |
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Experiments show that the method can use the characteristics of SDN to route the network traffic in multi-path according to the current state information and traffic characteristics of the network; using the advantages of reinforcement learning, it can find multiple forwarding paths for different flows that conform to their traffic characteristics, and improve the utilization rate of the network link bandwidth; and using the neural network of deep learning to fit the Q value in the traditional reinforcement learning algorithm Table.</description><identifier>ISSN: 1742-6588</identifier><identifier>EISSN: 1742-6596</identifier><identifier>DOI: 10.1088/1742-6596/1617/1/012043</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Algorithms ; Communications traffic ; Deep learning ; Machine learning ; Neural networks ; Optimization ; Physics ; Route planning ; Software-defined networking ; Traffic flow ; Traffic information ; Traffic planning</subject><ispartof>Journal of physics. 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| subjects | Algorithms Communications traffic Deep learning Machine learning Neural networks Optimization Physics Route planning Software-defined networking Traffic flow Traffic information Traffic planning |
| title | Research on deep reinforcement learning multi-path routing planning in SDN |
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