Token bucket characterization of long-range dependent traffic

The token bucket characterization provides a deterministic yet concise representation of a traffic source. In this paper, we study the impact of the long-range dependence LRD) property of traf®c generated by today's multimedia applications on the optimal dimensioning of token bucket parameters. To this aim, we empirically illustrate the difference between the token bucket characteristics of traf®c exhibiting different degrees of time dependence but with identical macroscopic properties i.e. inter-arrival time and packet size distributions). In addition, we use a statistical model to analytically determine optimal token bucket parameters under various optimization criteria. The statistical model is based on fractional Brownian motion and takes LRD into account. We apply this model to several aggregated MPEG video sources. We then assess the validity of these analytic results by comparing them to empirical results. We conclude that the analytic approach presented here is effective in optimally sizing token buckets for LRD traffic, and promises to be applicable under different traffic conditions and for various optimization criteria.