跟踪每流状态-分级持续流跟踪

B. Whitehead, Chung-Horng Lung, P. Rabinovitch
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引用次数: 13

摘要

网络监控的最新进展越来越多地集中在获取每个流的信息,如流状态。跟踪网络流的状态为网络运营商开辟了一个新的信息收集维度,允许捕获以前无法获得的数据。本文提出了一种时间效率高的新方法——分桶持续流跟踪(BDFT),该方法通过将有效流分组到“箱”中来跟踪每个流的状态。BDFT适用于CPU时间非常重要的高速路由器。BDFT采用Bloom过滤器作为主要数据结构,节省了时间。仿真结果表明,BDFT对真实网络流量的跟踪准确率达到99%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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