{"title":"跟踪每流状态-分级持续流跟踪","authors":"B. Whitehead, Chung-Horng Lung, P. Rabinovitch","doi":"10.4304/jnw.7.1.37-51","DOIUrl":null,"url":null,"abstract":"Recent advances in network monitoring have increasingly focused on obtaining per-flow information, such as flow state. Tracking the state of network flows opens up a new dimension of information gathering for network operators, allowing previously unattainable data to be captured. This paper presents a time efficient novel method — Binned Duration Flow Tracking (BDFT) — of tracking per-flow state by grouping valid flows into “bins”. BDFT is intended for high-speed routers where CPU time is crucial. BDFT is time efficient by adopting Bloom filters as the primary data structures. Simulation results show that BDFT can achieve over 99% accuracy on traces of real network traffic.","PeriodicalId":426447,"journal":{"name":"Proceedings of the 2010 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS '10)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Tracking per-flow state — Binned Duration Flow Tracking\",\"authors\":\"B. Whitehead, Chung-Horng Lung, P. Rabinovitch\",\"doi\":\"10.4304/jnw.7.1.37-51\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent advances in network monitoring have increasingly focused on obtaining per-flow information, such as flow state. Tracking the state of network flows opens up a new dimension of information gathering for network operators, allowing previously unattainable data to be captured. This paper presents a time efficient novel method — Binned Duration Flow Tracking (BDFT) — of tracking per-flow state by grouping valid flows into “bins”. BDFT is intended for high-speed routers where CPU time is crucial. BDFT is time efficient by adopting Bloom filters as the primary data structures. Simulation results show that BDFT can achieve over 99% accuracy on traces of real network traffic.\",\"PeriodicalId\":426447,\"journal\":{\"name\":\"Proceedings of the 2010 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS '10)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2010 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS '10)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4304/jnw.7.1.37-51\",\"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 2010 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS '10)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4304/jnw.7.1.37-51","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tracking per-flow state — Binned Duration Flow Tracking
Recent advances in network monitoring have increasingly focused on obtaining per-flow information, such as flow state. Tracking the state of network flows opens up a new dimension of information gathering for network operators, allowing previously unattainable data to be captured. This paper presents a time efficient novel method — Binned Duration Flow Tracking (BDFT) — of tracking per-flow state by grouping valid flows into “bins”. BDFT is intended for high-speed routers where CPU time is crucial. BDFT is time efficient by adopting Bloom filters as the primary data structures. Simulation results show that BDFT can achieve over 99% accuracy on traces of real network traffic.