Delay-sensitive traffic intellisense scheduling based on optimal decision tree
Currently, network traffic scheduling strategy cannot be intelligent and on-demand, especially in the congestion caused by sudden network failures and escort scenarios of high-value services.They cannot guarantee latency-sensitive service experience on demand.The delay-sensitive attribute requiremen...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-04-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023095/ |
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| _version_ | 1841530833683349504 |
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| author | Xuerong WANG Zhengzhi TANG Yinchuan LI Meiyu QI Jianbo ZHU Liang ZHANG |
| author_facet | Xuerong WANG Zhengzhi TANG Yinchuan LI Meiyu QI Jianbo ZHU Liang ZHANG |
| author_sort | Xuerong WANG |
| collection | DOAJ |
| description | Currently, network traffic scheduling strategy cannot be intelligent and on-demand, especially in the congestion caused by sudden network failures and escort scenarios of high-value services.They cannot guarantee latency-sensitive service experience on demand.The delay-sensitive attribute requirements of different network traffic were analyzed and studied, and the internal correlation between the behavior characteristics of varying network traffic and its delay sensitivity requirements was explored.Then, AI technology was used to learn this inherent relationship and construct its mapping relationship, realizing a traffic scheduling technical solution based on the intelligent awareness of delay-sensitive traffic.At the same time, considering the practical issues of interpretability and deploy-ability of AI models, reinforcement learning (RL) technology was used to prune and optimize the interpretable decision tree model, which improved the robustness of the model and made model lighter and easier to implement in equipment deployment.Through experiments by the collected real network traffic, the decision tree model optimized by reinforcement learning could improve the awareness accuracy by 1.75% in a single inference case, and the inference performance was improved by about 30%.The experiment also proved that using micro-statistical features for multiple inferences could help improve the model accuracy; in all experiments, the scale of the decision tree model optimized by RL was reduced by about 60.0%～87.2%, and the Saras had better optimization performance than Q-learning. |
| format | Article |
| id | doaj-art-72fb951352a549879943065b474fb3fb |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-04-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-72fb951352a549879943065b474fb3fb2025-01-15T02:58:50ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-04-013912013259569053Delay-sensitive traffic intellisense scheduling based on optimal decision treeXuerong WANGZhengzhi TANGYinchuan LIMeiyu QIJianbo ZHULiang ZHANGCurrently, network traffic scheduling strategy cannot be intelligent and on-demand, especially in the congestion caused by sudden network failures and escort scenarios of high-value services.They cannot guarantee latency-sensitive service experience on demand.The delay-sensitive attribute requirements of different network traffic were analyzed and studied, and the internal correlation between the behavior characteristics of varying network traffic and its delay sensitivity requirements was explored.Then, AI technology was used to learn this inherent relationship and construct its mapping relationship, realizing a traffic scheduling technical solution based on the intelligent awareness of delay-sensitive traffic.At the same time, considering the practical issues of interpretability and deploy-ability of AI models, reinforcement learning (RL) technology was used to prune and optimize the interpretable decision tree model, which improved the robustness of the model and made model lighter and easier to implement in equipment deployment.Through experiments by the collected real network traffic, the decision tree model optimized by reinforcement learning could improve the awareness accuracy by 1.75% in a single inference case, and the inference performance was improved by about 30%.The experiment also proved that using micro-statistical features for multiple inferences could help improve the model accuracy; in all experiments, the scale of the decision tree model optimized by RL was reduced by about 60.0%～87.2%, and the Saras had better optimization performance than Q-learning.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023095/traffic analysistraffic schedulingdelay-sensitive attributereinforcement learningdecision tree |
| spellingShingle | Xuerong WANG Zhengzhi TANG Yinchuan LI Meiyu QI Jianbo ZHU Liang ZHANG Delay-sensitive traffic intellisense scheduling based on optimal decision tree Dianxin kexue traffic analysis traffic scheduling delay-sensitive attribute reinforcement learning decision tree |
| title | Delay-sensitive traffic intellisense scheduling based on optimal decision tree |
| title_full | Delay-sensitive traffic intellisense scheduling based on optimal decision tree |
| title_fullStr | Delay-sensitive traffic intellisense scheduling based on optimal decision tree |
| title_full_unstemmed | Delay-sensitive traffic intellisense scheduling based on optimal decision tree |
| title_short | Delay-sensitive traffic intellisense scheduling based on optimal decision tree |
| title_sort | delay sensitive traffic intellisense scheduling based on optimal decision tree |
| topic | traffic analysis traffic scheduling delay-sensitive attribute reinforcement learning decision tree |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023095/ |
| work_keys_str_mv | AT xuerongwang delaysensitivetrafficintellisenseschedulingbasedonoptimaldecisiontree AT zhengzhitang delaysensitivetrafficintellisenseschedulingbasedonoptimaldecisiontree AT yinchuanli delaysensitivetrafficintellisenseschedulingbasedonoptimaldecisiontree AT meiyuqi delaysensitivetrafficintellisenseschedulingbasedonoptimaldecisiontree AT jianbozhu delaysensitivetrafficintellisenseschedulingbasedonoptimaldecisiontree AT liangzhang delaysensitivetrafficintellisenseschedulingbasedonoptimaldecisiontree |