A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance

TLDR

The study evaluates the performance of a statistical multiplexer handling combined packetized voice and data traffic. The authors model the traffic as a correlated Markov modulated Poisson process and use matrix analytic techniques, including Laplace–Stieltjes transforms, to compute delay and queue metrics while accounting for finite buffers and overload control. The analysis yields accurate voice and data delay and queue length distributions, with simulations confirming the results and demonstrating that traffic variability and correlation markedly increase voice packet delay tails.

Abstract

We study the performance of a statistical multiplexer whose inputs consist of a superposition of packetized voice sources and data. The performance analysis predicts voice packet delay distributions, which usually have a stringent requirement, as well as data packet delay distributions. The superposition is approximated by a correlated Markov modulated Poisson process (MMPP), which is chosen such that several of its statistical characteristics identically match those of the superposition. Matrix analytic methods are then used to evaluate system performance measures. In particular, we obtain moments of voice and data delay distributions and queue length distributions. We also obtain Laplace-Stieitjes transforms of the voice and data packet delay distributions, which are numerically inverted to evaluate tails of delay distributions. It is shown how the matrix analytic methodology can incorporate practical system considerations such as finite buffers and a class of overload control mechanisms discussed in the literature. Comparisons with simulation show the methods to be accurate. The numerical results for the tails of the voice packet delay distribution show the dramatic effect of traffic variability and correlations on performance.