Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning
In order to solve the problem that the influencing factors are difficult to parameterize in the design and development of WDM/OTN backbone network routing planning tools, the author proposes an optimal scheduling model for WDM/OTN network transmission lines based on machine learning. Using the machi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2006930 |
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| _version_ | 1832561873714675712 |
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| author | Jianhua Zhao Jingquan Li Huicong Fan Wenxiao Li Jingna Zhang Xiaoyuan Dai |
| author_facet | Jianhua Zhao Jingquan Li Huicong Fan Wenxiao Li Jingna Zhang Xiaoyuan Dai |
| author_sort | Jianhua Zhao |
| collection | DOAJ |
| description | In order to solve the problem that the influencing factors are difficult to parameterize in the design and development of WDM/OTN backbone network routing planning tools, the author proposes an optimal scheduling model for WDM/OTN network transmission lines based on machine learning. Using the machine learning classification algorithm as a tool, the weight coefficients of each constraint factor are extracted from the historical design decisions, and the routing parameter model is constructed, so as to realize the intelligent routing selection, through actual simulation analysis and engineering verification. Simulation results show that after the historical routing regression test, the path coincidence rate of the route obtained by the algorithm and the historical real decision-making route reaches 81%, and the resource hit rate reaches 84%, which meets the requirements for actual production. Conclusion. This method can accurately and effectively generate network weight parameters so that the software routing is more intelligent. |
| format | Article |
| id | doaj-art-cf997333a6bb4ad2a2e39f18468ae5d6 |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-cf997333a6bb4ad2a2e39f18468ae5d62025-02-03T01:24:09ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/2006930Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine LearningJianhua Zhao0Jingquan Li1Huicong Fan2Wenxiao Li3Jingna Zhang4Xiaoyuan Dai5State Grid Hebei Economic Research InstituteState Grid Hebei Electric Power Co., Ltd.State Grid Hebei Economic Research InstituteState Grid Hebei Economic Research InstitutePowerchina Hebei Electric Power Engineering Co., Ltd.Powerchina Hebei Electric Power Engineering Co., Ltd.In order to solve the problem that the influencing factors are difficult to parameterize in the design and development of WDM/OTN backbone network routing planning tools, the author proposes an optimal scheduling model for WDM/OTN network transmission lines based on machine learning. Using the machine learning classification algorithm as a tool, the weight coefficients of each constraint factor are extracted from the historical design decisions, and the routing parameter model is constructed, so as to realize the intelligent routing selection, through actual simulation analysis and engineering verification. Simulation results show that after the historical routing regression test, the path coincidence rate of the route obtained by the algorithm and the historical real decision-making route reaches 81%, and the resource hit rate reaches 84%, which meets the requirements for actual production. Conclusion. This method can accurately and effectively generate network weight parameters so that the software routing is more intelligent.http://dx.doi.org/10.1155/2022/2006930 |
| spellingShingle | Jianhua Zhao Jingquan Li Huicong Fan Wenxiao Li Jingna Zhang Xiaoyuan Dai Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning Journal of Control Science and Engineering |
| title | Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning |
| title_full | Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning |
| title_fullStr | Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning |
| title_full_unstemmed | Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning |
| title_short | Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning |
| title_sort | optimal scheduling model of wdm otn network transmission line based on machine learning |
| url | http://dx.doi.org/10.1155/2022/2006930 |
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