Modeling residual-geometric flow sampling

Traffic monitoring and estimation of flow parameters in high speed routers have recently become challenging as the Internet grew in both scale and complexity. In this paper, we focus on a family of flow-size estimation algorithms we call Residual-Geometric Sampling (RGS), which generates a random point within each flow according to a geometric random variable and records all remaining packets in a flow counter. Our analytical investigation shows that previous estimation algorithms based on this method exhibit certain bias in recovering flow statistics from the sampled measurements. To address this problem, we derive a novel set of unbiased estimators for RGS, validate them using real Internet traces, and show that they provide an accurate and scalable solution to Internet traffic monitoring.