摘要
高速网络中,流量抽样测量技术是一种重要可扩展的解决方案,其中NetFlow在流量测量中有着广泛的应用。针对NetFlow的缺陷提出了一种基于流量负载自适应的时间分层分组抽样算法,主要采用了预定义测量误差、时间分层分组抽样、自适应预测流量负载的方法,该抽样测量算法具有以下优点:抽样方法简单、易于实现,抽样概率自适应于流量负载的变化,平衡了资源的消耗量和准确性。并基于实际互联网数据进行了实验比较,结果显示:该方法具有简单性、自适应性、资源可控性的同时不会失去准确性。
The technique of traffic sampling measurement is an important scalable solution in high-speed network.NetFlow is one of the applications which is widely deployed for traffic measurement.However,the sampling method of NetFlow has shortcomings.In order to overcome those deficiencies,a novel sketch called time stratified adaptive packet sampling was proposed based on traffic load.The proposed sketch adopts the following methods：bounding sampling error within a pre-specified tolerance level,time stratified sampling and predicting traffic load adaptively.The easily-implemented packet sampling method presented can not only automatically adapt the sampling rate to traffic variety,but also give the right tradeoff between resource consumption and accuracy for all traffic mixes.Experiments were conducted based on real network traces.Results demonstrate that the proposed method can achieve simplicity,adaptability and controllability of resource consumption without sacrificing accuracy compared with other sampling methods.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2009年第23期7421-7427,共7页
Journal of System Simulation
基金
国家自然科学基金(60572042)
国家重点基础研究发展规划973(2007CB307102)
