Z. Qazi, Jeongkeun Lee, Tao Jin, G. Bellala, M. Arndt, G. Noubir
{"title":"SDN中的应用感知","authors":"Z. Qazi, Jeongkeun Lee, Tao Jin, G. Bellala, M. Arndt, G. Noubir","doi":"10.1145/2486001.2491700","DOIUrl":null,"url":null,"abstract":"We present a framework, Atlas, which incorporates application-awareness into Software-Defined Networking (SDN), which is currently capable of L2/3/4-based policy enforcement but agnostic to higher layers. Atlas enables fine-grained, accurate and scalable application classification in SDN. It employs a machine learning (ML) based traffic classification technique, a crowd-sourcing approach to obtain ground truth data and leverages SDN's data reporting mechanism and centralized control. We prototype Atlas on HP Labs wireless networks and observe 94% accuracy on average, for top 40 Android applications.","PeriodicalId":159374,"journal":{"name":"Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"159","resultStr":"{\"title\":\"Application-awareness in SDN\",\"authors\":\"Z. Qazi, Jeongkeun Lee, Tao Jin, G. Bellala, M. Arndt, G. Noubir\",\"doi\":\"10.1145/2486001.2491700\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a framework, Atlas, which incorporates application-awareness into Software-Defined Networking (SDN), which is currently capable of L2/3/4-based policy enforcement but agnostic to higher layers. Atlas enables fine-grained, accurate and scalable application classification in SDN. It employs a machine learning (ML) based traffic classification technique, a crowd-sourcing approach to obtain ground truth data and leverages SDN's data reporting mechanism and centralized control. We prototype Atlas on HP Labs wireless networks and observe 94% accuracy on average, for top 40 Android applications.\",\"PeriodicalId\":159374,\"journal\":{\"name\":\"Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"159\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2486001.2491700\",\"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 the ACM SIGCOMM 2013 conference on SIGCOMM","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2486001.2491700","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present a framework, Atlas, which incorporates application-awareness into Software-Defined Networking (SDN), which is currently capable of L2/3/4-based policy enforcement but agnostic to higher layers. Atlas enables fine-grained, accurate and scalable application classification in SDN. It employs a machine learning (ML) based traffic classification technique, a crowd-sourcing approach to obtain ground truth data and leverages SDN's data reporting mechanism and centralized control. We prototype Atlas on HP Labs wireless networks and observe 94% accuracy on average, for top 40 Android applications.
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