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Abstract : |
Abstract--We derive estimators and bounds that drive probabilistic polling algorithms for the estimation of the session size, r, of any potentially large scale multicast session. We base our analysis upon a mapping of polling mechanisms to the problem of estimating the parameter r of the binomial (r, p) distribution. From the binomial model, we derive an inter-val estimator for r, and we characterize the tradeoff between the estimator 's quality and its overhead in a manner readily matched to application requirements. We derive other estimators and bounds that enable applications to treat as a tunable parameter the confidence that they will not exceed their overhead limits. We also suggest revised estimators and other improvements for the mechanisms proposed by Bolot, Turletti, and Wakeman [1], and Nonnenmacher and Biersack [2], [3], [4]., |
