{"title":"基于多尺度熵的移动终端网络流量分析","authors":"Xiaoming Chen, Huiqiang Wang, Junyu Lin, Guangsheng Feng, Chao Zhao","doi":"10.1109/APSCC.2014.21","DOIUrl":null,"url":null,"abstract":"Future networks devoted much attention to QoS of user experience. This makes it very important to understand the characteristics of user traffic. For network across multiple network layers have the characteristics of varying complexity, put forward a kind of traffic characteristics analysis method based on space and time scales. Firstly, the traffic model is established using multi-scale characterization, and then network behavior at different temporal and spatial scales of structural complexity network behavior is analyzed. Then we explore its change law of time scale, it can success classifies traffic types, so as to accurately forecast the next period of time of business. The results of the experiment data analysis shows that the method can effectively realize online monitoring of the business flow.","PeriodicalId":393593,"journal":{"name":"2014 Asia-Pacific Services Computing Conference","volume":"269 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Network Traffic Analysis for Mobile Terminal Based Multi-scale Entropy\",\"authors\":\"Xiaoming Chen, Huiqiang Wang, Junyu Lin, Guangsheng Feng, Chao Zhao\",\"doi\":\"10.1109/APSCC.2014.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Future networks devoted much attention to QoS of user experience. This makes it very important to understand the characteristics of user traffic. For network across multiple network layers have the characteristics of varying complexity, put forward a kind of traffic characteristics analysis method based on space and time scales. Firstly, the traffic model is established using multi-scale characterization, and then network behavior at different temporal and spatial scales of structural complexity network behavior is analyzed. Then we explore its change law of time scale, it can success classifies traffic types, so as to accurately forecast the next period of time of business. The results of the experiment data analysis shows that the method can effectively realize online monitoring of the business flow.\",\"PeriodicalId\":393593,\"journal\":{\"name\":\"2014 Asia-Pacific Services Computing Conference\",\"volume\":\"269 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Asia-Pacific Services Computing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APSCC.2014.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Asia-Pacific Services Computing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSCC.2014.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Network Traffic Analysis for Mobile Terminal Based Multi-scale Entropy
Future networks devoted much attention to QoS of user experience. This makes it very important to understand the characteristics of user traffic. For network across multiple network layers have the characteristics of varying complexity, put forward a kind of traffic characteristics analysis method based on space and time scales. Firstly, the traffic model is established using multi-scale characterization, and then network behavior at different temporal and spatial scales of structural complexity network behavior is analyzed. Then we explore its change law of time scale, it can success classifies traffic types, so as to accurately forecast the next period of time of business. The results of the experiment data analysis shows that the method can effectively realize online monitoring of the business flow.