Bias correction in effective bandwidth estimation

Call admission in ATM networks involves a trade-off between ensuring an adequate quality of service to users and exploiting the scale efficiencies of statistical multiplexing. Achieving a good trade-off requires some knowledge of the source traffic. Its effective bandwidth has been proposed as a measure that captures characteristics which are relevant to quality of service provisioning. The effective bandwidth of a source is not known a priori, but needs to be estimated from an observation of its output. We show that direct estimators that have been proposed for this purpose are biased when the source traffic is autocorrelated. By explicitly computing the bias for auto-regressive and Markov sources, we devise a bias correction scheme that does not require knowledge of the model parameters. This is achieved by exploiting a scaling property of the bias that is insensitive to model parameters, and that has the same form for both auto-regressive and Markov sources. This leads us to conjecture that the scaling property may be valid in greater generality and can be used to obtain unbiased effective bandwidth estimates for real traffic. Use of our bias correction technique enables us to obtain accurate estimates of effective bandwidths using relatively short block lengths. The latter is important both because the variance of the estimator increases with the block length, and because real traffic may well be non-stationary, requiring that estimates be obtained from short data records.