A Frequency Domain Model to Predict the Estimation Accuracy of Packet Sampling

In network measurement systems, packet sampling techniques are usually adopted to reduce the overall amount of data to collect and process. Being based on a subset of packets, they hence introduce estimation errors that have to be properly counteracted by a fine tuning of the sampling strategy and sophisticated inversion methods. This problem has been deeply investigated in the literature with particular attention to the statistical properties of packet sampling and the recovery of the original network measurements. Herein, we propose a novel approach to predict the energy of the sampling error on the real time traffic volume estimation, based on a spectral analysis in the frequency domain. We start by demonstrating that errors due to packet sampling can be modeled as an aliasing effect in the frequency domain. Then, we exploit this theoretical finding to derive closed-form expressions for the Signal-to-Noise Ratio (SNR), able to predict the distortion of traffic volume estimates over time. The accuracy of the proposed SNR metric is validated by means of real packet traces.