Association for Computing Machinery
Network monitoring in cellular networks requires the tracking of quantiles for data distributions of many evolving network measurements (e.g. number of high signaling subscribers per minute). Most quantile estimation algorithms are based on a summary of the empirical data distribution, using either a representative sample or a global approximation of the entire distribution. In contrast, by viewing data as a quantity from a random distribution, the stochastic approximation (SA) for quantile estimation does not keep a global approximation, but rather local approximations at the quantiles of interest, and therefore uses negligible memory even for estimating tail quantiles.