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 used to determine the maximum number of connections that can be admitted to a resource within a given quality constraint. It has been pointed out that the assumption that the measured parameters are the true ones can compromise admission control. This is because the measured parameters are themselves random quantities, and so contribute additional variability to the attained quality. This paper analyzes the impact of measurement error within the framework of large deviation theory. For a class of admission controls, large deviation principles are established for the number of admitted connections, and for the attained overflow rates. The technique is applied initially to the case that many sources seek admission to a bufferless resource, but it is shown how to extend to buffered resources in both the many sources and large buffer asymptotic. The sampling properties of effective bandwidths are presented, together with a discussion the impact of the temporal extent of individual samples on estimator variability. Sample correlations are shown to increase estimator variance; procedures to make admission control robust with respect to these are described.