Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning
The software defined network separates the control plane from the data plane to achieve flexible traffic scheduling, which can use network resources more efficiently.However, with the increase of the number of flow entries, load rate, the number of connected hosts, and other factors, the forwarding...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-08-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022148/ |
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| author | Zongxuan SHA Ru HUO Chuang SUN Shuo WANG Tao HUANG |
| author_facet | Zongxuan SHA Ru HUO Chuang SUN Shuo WANG Tao HUANG |
| author_sort | Zongxuan SHA |
| collection | DOAJ |
| description | The software defined network separates the control plane from the data plane to achieve flexible traffic scheduling, which can use network resources more efficiently.However, with the increase of the number of flow entries, load rate, the number of connected hosts, and other factors, the forwarding efficiency of the SDN switch will be reduced, which will affect the end-to-end transmission delay.To solve the above problems, the forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning was proposed.First, the switch state was integrated into the perception model, and the mapping relationship between switch state information and forwarding efficiency was established based on neural network.Then, combined with network state and traffic information, traffic scheduling policy was generated through deep reinforcement learning.Finally, the expert samples generated by the shortest path and load balance algorithms could guide the model training, which enabled the model to learn knowledge from expert samples to improve performance and accelerated the training process.The experimental results show that the proposed algorithm not only reduces the average end-to-end transmission delay by 15.31%, but also ensures the overall load balance of the network, compared with other algorithms. |
| format | Article |
| id | doaj-art-b5a762460ff1450fab53c253c934d38b |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b5a762460ff1450fab53c253c934d38b2025-01-14T06:28:55ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-08-0143304059392126Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learningZongxuan SHARu HUOChuang SUNShuo WANGTao HUANGThe software defined network separates the control plane from the data plane to achieve flexible traffic scheduling, which can use network resources more efficiently.However, with the increase of the number of flow entries, load rate, the number of connected hosts, and other factors, the forwarding efficiency of the SDN switch will be reduced, which will affect the end-to-end transmission delay.To solve the above problems, the forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning was proposed.First, the switch state was integrated into the perception model, and the mapping relationship between switch state information and forwarding efficiency was established based on neural network.Then, combined with network state and traffic information, traffic scheduling policy was generated through deep reinforcement learning.Finally, the expert samples generated by the shortest path and load balance algorithms could guide the model training, which enabled the model to learn knowledge from expert samples to improve performance and accelerated the training process.The experimental results show that the proposed algorithm not only reduces the average end-to-end transmission delay by 15.31%, but also ensures the overall load balance of the network, compared with other algorithms.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022148/software defined networkdeep reinforcement learningtraffic schedulingforwarding efficiency awareload balance |
| spellingShingle | Zongxuan SHA Ru HUO Chuang SUN Shuo WANG Tao HUANG Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning Tongxin xuebao software defined network deep reinforcement learning traffic scheduling forwarding efficiency aware load balance |
| title | Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning |
| title_full | Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning |
| title_fullStr | Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning |
| title_full_unstemmed | Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning |
| title_short | Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning |
| title_sort | forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning |
| topic | software defined network deep reinforcement learning traffic scheduling forwarding efficiency aware load balance |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022148/ |
| work_keys_str_mv | AT zongxuansha forwardingefficiencyawaretrafficschedulingalgorithmbasedondeepreinforcementlearning AT ruhuo forwardingefficiencyawaretrafficschedulingalgorithmbasedondeepreinforcementlearning AT chuangsun forwardingefficiencyawaretrafficschedulingalgorithmbasedondeepreinforcementlearning AT shuowang forwardingefficiencyawaretrafficschedulingalgorithmbasedondeepreinforcementlearning AT taohuang forwardingefficiencyawaretrafficschedulingalgorithmbasedondeepreinforcementlearning |