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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 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.