S3: Smart selection of sampling function for passive network measurement

Flow size statistics is a fundamental task of passive measurement. In order to bound the estimation error of passive measurement for both small and large flows, previous probabilistic counter updating algorithms used linear or nonlinear sampling function to automatically adjust the sampling rate. However, each of these methods employed a pre-set and fixed sampling function during the measurement period. As a result, the performance would vary for different flow distributions. In this paper, we propose a Smart Selection Sampling (S3) approach, which can tune the sampling function to reach a comparatively lower relative error. The key component of S3 is a heuristic algorithm leveraging the flow distribution information to determine a better sampling function so as to achieve better measurement accuracy. Experiments under real trace and synthetic traces demonstrate that S3 is more accurate than the previous work if given the same memory sizes to accommodate flow statistics counters.