Adaptive Sampling Methods to Determine Network Traffic Statistics including the Hurst Parameter

Accurate traffic characterization by packet source is needed to predict network behavior and to properly allocate network resources to achieve a desired Quality of Service for all network users. As networks have become faster, the processing load required for complete packet sampling has also grown. In some cases, for example Gigabit Ethernet, the network can deliver packets faster than a network management subsystem can process them. In order to prevent inaccurate traffic statistics due to “clipping” of traffic peaks, Claffy et al. applied several static sampling strategies to network traffic characterization. As shown in this paper, static sampling may produce inaccurate traffic statistics. In this paper, adaptive sampling methods are developed and evaluated to address inaccuracies of static sampling. In addition, the estimation of the Hurst parameter, a measure of traffic self-similarity, is studied for static and adaptive sampling. It is shown that adaptive sampling results in a more accurate estimation of the mean, variance, and Hurst parameter for packet counts.