{"title":"分析用于 SDN 资源管理的流量识别方法","authors":"J. Dmitrieva, D. Okuneva, V. Elagin","doi":"10.31854/1813-324x-2023-9-6-42-57","DOIUrl":null,"url":null,"abstract":"The article is devoted to the analysis of traffic classification methods in SDN network. The review of analytical approaches of traffic identification to identify the solutions used in them, as well as assessing their applicability in the SDN network. Types of machine learning are considered and input parameters are analyzed. The methods of intelligent analysis covered in the scientific articles are systematized according to the following criteria: traffic identification parameters, neural network model, identification accuracy. Based on the analysis of the review results, the conclusion is made about the possibility of applying the considered solutions, as well as the need to form a scheme of SDN network with a module of artificial intelligence elements for load balancing.","PeriodicalId":298883,"journal":{"name":"Proceedings of Telecommunication Universities","volume":"8 12","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analyzing Traffic Identification Methods for Resource Management in SDN\",\"authors\":\"J. Dmitrieva, D. Okuneva, V. Elagin\",\"doi\":\"10.31854/1813-324x-2023-9-6-42-57\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article is devoted to the analysis of traffic classification methods in SDN network. The review of analytical approaches of traffic identification to identify the solutions used in them, as well as assessing their applicability in the SDN network. Types of machine learning are considered and input parameters are analyzed. The methods of intelligent analysis covered in the scientific articles are systematized according to the following criteria: traffic identification parameters, neural network model, identification accuracy. Based on the analysis of the review results, the conclusion is made about the possibility of applying the considered solutions, as well as the need to form a scheme of SDN network with a module of artificial intelligence elements for load balancing.\",\"PeriodicalId\":298883,\"journal\":{\"name\":\"Proceedings of Telecommunication Universities\",\"volume\":\"8 12\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Telecommunication Universities\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31854/1813-324x-2023-9-6-42-57\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Telecommunication Universities","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31854/1813-324x-2023-9-6-42-57","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本文致力于分析 SDN 网络中的流量分类方法。文章回顾了流量识别的分析方法,以确定其中使用的解决方案,并评估其在 SDN 网络中的适用性。考虑了机器学习的类型并分析了输入参数。根据以下标准对科学文章中涉及的智能分析方法进行了系统化:流量识别参数、神经网络模型、识别精度。在对审查结果进行分析的基础上,得出了应用所考虑的解决方案的可能性的结论,以及形成具有人工智能元素模块的 SDN 网络负载平衡方案的必要性。
Analyzing Traffic Identification Methods for Resource Management in SDN
The article is devoted to the analysis of traffic classification methods in SDN network. The review of analytical approaches of traffic identification to identify the solutions used in them, as well as assessing their applicability in the SDN network. Types of machine learning are considered and input parameters are analyzed. The methods of intelligent analysis covered in the scientific articles are systematized according to the following criteria: traffic identification parameters, neural network model, identification accuracy. Based on the analysis of the review results, the conclusion is made about the possibility of applying the considered solutions, as well as the need to form a scheme of SDN network with a module of artificial intelligence elements for load balancing.