Dimensioning bandwidth for elastic traffic in high-speed data networks

TLDR: The model is compared with simulations, the accuracy of the asymptotic approximations are examined, the increase in bandwidth needed to satisfy the tail-probability performance objective as compared with the mean objective, and regimes where statistical gain can and cannot be realized are shown.

Abstract: Simple and robust engineering rules for dimensioning bandwidth for elastic data traffic are derived for a single bottleneck link via normal approximations for a closed-queueing network (CQN) model in heavy traffic. Elastic data applications adapt to available bandwidth via a feedback control such as the transmission control protocol (TCP) or the available bit rate transfer capability in asynchronous transfer mode. The dimensioning rules satisfy a performance objective based on the mean or tail probability of the per-flow bandwidth. For the mean objective, we obtain a simple expression for the effective bandwidth of an elastic source. We provide a new derivation of the normal approximation in CQNs using more accurate asymptotic expansions and give an explicit estimate of the error in the normal approximation. A CQN model was chosen to obtain the desirable property that the results depend on the distribution of the file sizes only via the mean, and not the heavy-tail characteristics. We view the exogenous "load" in terms of the file sizes and consider the resulting flow of packets as dependent on the presence of other flows and the closed-loop controls. We compare the model with simulations, examine the accuracy of the asymptotic approximations, quantify the increase in bandwidth needed to satisfy the tail-probability performance objective as compared with the mean objective, and show regimes where statistical gain can and cannot be realized.