基于业务流数量自适应的资源限制分组抽样

Resource constraints adaptive packet sampling based on flow counting

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Net-Flow的缺陷提出了一种基于业务流数量自适应的资源限制分组抽样算法,该算法结合"分层抽样"的思想,把"累积业务流数量"作为重要的参数,来自适应地调节抽样概率,该抽样方法简单、易于实现,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,this paper proposes a novel sketch called resource constraints adaptive packet sampling based on flow counting.Based on the idea of stratified sampling,the proposed sketch introduces a parameter called cumulative number of flows in order to control the memory resource.The easily-implemented packet sampling method presented can not only automatically adapt the sampling rate,but also give the right tradeoff between resource consumption and accuracy for all traffic mixes.Experiments are also 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.