A large deviation analysis of errors in measurement based admission control to buffered and bufferless resources

In measurement based admission control, measured traffic parameters are use d to determine the maximum number of connections that can be admitted to a resource within a given quality constraint. The assumption that the measure d parameters are the true ones can compromise admission control; measured p arameters are random quantities, causing additional variability. This paper analyzes the impact of measurement error within the framework of Large Dev iation theory. For a class of admission controls, large deviation principle s are established for the number of admitted connections, and for the attai ned overflow rates. These are applied to admission to bufferless resources, and buffered resources in both the many sources and large buffer asymptoti c. The sampling properties of effective bandwidths are presented, together with a discussion the impact of the temporal extent of individual samples o n estimator variability. Sample correlations are shown to increase estimato r variance; procedures to make admission control robust with respect to the se are described.