Hierarchical Virtual Bitmaps for Spread Estimation in Traffic Measurement
Hierarchical Virtual Bitmaps for Spread Estimation in Traffic Measurement
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DOI：
10.5121/csit.2021.110718
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发表时间：
2021-05
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通讯作者：
Olufemi O. Odegbile;Chaoyi Ma;Shigang Chen;Dimitrios Melissourgos;Haibo Wang
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作者：
Olufemi O. Odegbile;Chaoyi Ma;Shigang Chen;Dimitrios Melissourgos;Haibo Wang
本文介绍了一种用于网络流量分布测量的分层流量模型。分层模型将较低级别的流量以分层结构聚合为较高级别的流量，使我们能够同时测量不同粒度的网络流量，以支持从大视图到细粒度细节的多样化流量分析。流的扩展是流中不同元素（正在测量）的数量，其中流标签（标识属于流的数据包）和元素（根据应用程序需要定义）可以在数据包标头或有效负载中找到。传统的流量分布估计器在设计时没有考虑分层流量建模，并且当它们应用于流量层次结构的每个级别时会产生很高的开销。在本文中，我们提出了一种新的分层虚拟位图估计器（HVE），它以与传统估计器相同的成本执行同时多级流量测量，而不会降低测量精度。我们实施所提出的解决方案并根据真实的流量轨迹进行实验。实验结果表明，由于每个数据包处理开销的减少，HVE 将测量吞吐量提高了 43% 至 155%。对于中小型流量，其测量精度在很大程度上类似于一次在一个级别上工作的传统估算器。对于大型骨料和基流，其准确性更好，在我们的实验中误差可减小高达 97%。
This paper introduces a hierarchical traffic model for spread measurement of network traffic flows. The hierarchical model, which aggregates lower level flows into higher-level flows in a hierarchical structure, will allow us to measure network traffic at different granularities at once to support diverse traffic analysis from a grand view to fine-grained details. The spread of a flow is the number of distinct elements (under measurement) in the flow, where the flow label (that identifies packets belonging to the flow) and the elements (which are defined based on application need) can be found in packet headers or payload. Traditional flow spread estimators are designed without hierarchical traffic modeling in mind, and incur high overhead when they are applied to each level of the traffic hierarchy. In this paper, we propose a new Hierarchical Virtual bitmap Estimator (HVE) that performs simultaneous multi-level traffic measurement, at the same cost of a traditional estimator, without degrading measurement accuracy. We implement the proposed solution and perform experiments based on real traffic traces. The experimental results demonstrate that HVE improves measurement throughput by 43% to 155%, thanks to the reduction of perpacket processing overhead. For small to medium flows, its measurement accuracy is largely similar to traditional estimators that work at one level at a time. For large aggregate and base flows, its accuracy is better, with up to 97% smaller error in our experiments.