Efficient packet sampling for accurate traffic measurements
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Knowing the length distributions of traffic flows passing through a network link is useful for some applications such as inferring traffic demands, characterizing source traffic, and detecting traffic anomalies. The collection of the necessary information on every packet becomes prohibitive in term of the consumption of resources by measurement operation in high speed links. Packet sampling has become an attractive and scalable means to measure flow data on high-speed links. Passive traffic measurement increasingly employs sampling at the packet level and makes inferences from sampled network traffic. However, previous work has shown the inaccuracy of estimating flow length distributions from sampled traffic when the sampling is performed at the packet level. In this paper, we propose a novel Hybrid Algorithm (HA) that uses flow statistics formed from double-sampled packet stream to infer the absolute frequencies of lengths of flows in the unsampled stream. The theoretical analysis shows that the computational complexity of this method is well under control, and the experiment results demonstrate the inferred distributions are accurate.