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Flow cardinality estimation is the problem of estimating the number ofdistinct elements in a data flow, often with a stringent memory constraint. Ithas wide applications in network traffic measurement and in database systems.The virtual LogLog algorithm proposed recently by Xiao, Chen, Chen and Lingestimates the cardinalities of a large number of flows with a compact memory.The purpose of this thesis is to explore two new perspectives on the estimationprocess of this algorithm. Firstly, we propose and investigate a family ofestimators that generalizes the original vHLL estimator and evaluate theperformance of the vHLL estimator compared to other estimators in this family.Secondly, we propose an alternative solution to the estimation problem byderiving a maximum-likelihood estimator. Empirical evidence from bothperspectives suggests the near-optimality of the vHLL estimator for per-flowestimation, analogous to the near-optimality of the HLL estimator forsingle-flow estimation.

Per-Flow Cardinality Estimation Based On Virtual LogLog Sketching