Search Results for « Synthetic Network Traffic »

This paper presents a synthetic traffic modeling approach to generate bursty, long-range depen dent (LRD) traffic flows for ATM network simulations. The approach uses Fractional- ARIMA processes and a set of mathematical transformations to generate traffic streams with a wide range of user-specified traffic char acteristics. With this modeling approach, the user can control the short-range and long- range correlation structure in the generated traffic, as well as the marginal distribution. A description of the techniques is provided, along with a case study illustrating the use of these traffic generation techniques in a capacity planning study for ATM networks.

Abstract Variable bit rate (VBR) video traffic models, which accurately represent the traffic characteristics and statistical properties of real videos, can provide significant information about expected traffic behavior. This knowledge can be used in the development of effective control schemes and improved network quality of service. An interesting class of models based on the idea of generating a number of chi‐square sequences, by passing a Gaussian autoregressive (AR) process through a simple nonlinearity, was recently introduced. The gamma process in this class of models is obtained by a linear combination of the chi‐square sequences. This model is simple and allows for arbitrary selection of both the AR model and the shape parameter of the gamma probability density function. However, the AR filter order is chosen mainly on a trial‐and‐error basis. In addition, while the approach uses a linear combination of K chi‐square sequences, it fixes ( K − 2) coefficients and solves for only the remaining two because it has more equations than unknowns. Occasionally, the resulting solution is not feasible and additional trials for different solutions are required. It is therefore the objective of this paper to use genetic algorithms to provide a more systematic approach to find the various model parameters. The paper also presents a thorough statistical analysis of the generated synthetic data in order to assess its suitability for representing MPEG video traffic. A comparison with published results is carried out in terms of how close are the means, standard deviations, and the autocorrelation functions to those of the real data. A comparison of over 10000 replications and a number of different video traces reveals that a significant improvement can be achieved in almost all measures and for almost all the movies tested. Copyright © 2006 John Wiley & sons, Ltd.