Journal of Applied Mathematics and Stochastic AnalysisVolume 7, Issue 3, Pages 373-396http://dx.doi.org/10.1155/S1048953394000316

## On Markovian traffic with applications to TES processes

NEC USA, Inc., C&C Research Laboratories, 4 Independence Way, Princeton 08540, New Jersey, USA

Received 1 November 1993; Revised 1 July 1994

Copyright © 1994 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Markov processes are an important ingredient in a variety of stochastic applications. Notable instances include queueing systems and traffic processes offered to them. This paper is concerned with Markovian traffic, i.e., traffic processes whose inter-arrival times (separating the time points of discrete arrivals) form a real-valued Markov chain. As such this paper aims to extend the classical results of renewal traffic, where interarrival times are assumed to be independent, identically distributed. Following traditional renewal theory, three functions are addressed: the probability of the number of arrivals in a given interval, the corresponding mean number, and the probability of the times of future arrivals. The paper derives integral equations for these functions in the transform domain. These are then specialized to a subclass, TES+, of a versatile class of random sequences, called TES (Transform-Expand-Sample), consisting of marginally uniform autoregressive schemes with modulo-1 reduction, followed by various transformations. TES models are designed to simultaneously capture both first-order and second-order statistics of empirical records, and consequently can produce high-fidelity models. Two theoretical solutions for TES+ traffic functions are derived: an operator-based solution and a matric solution, both in the transform domain. A special case, permitting the conversion of the integral equations to differential equations, is illustrated and solved. Finally, the results are applied to obtain instructive closed-form representations for two measures of traffic burstiness: peakedness and index of dispersion, elucidating the relationship between them.