On Long Range Dependence and Token Buckets

The Long Range Dependence (LRD) property of actual traffic in today’s network applications has been shown to have significant impact on network performance. In this paper we consider the problem of optimally dimensioning token bucket parameters for LRD traffic. We first empirically illustrate the different behavior of token buckets when acting on LRD vs. SRD traffic with identical average and peak rates. The comparison shows that LRD traffic requires higher token rates and larger bucket sizes. In this paper we investigate the use of a statistical model to analytically determine optimal bucket parameters under various optimization criteria. The model is based on Fractional Brownian Motion and takes into account the degree of LRD. We apply the model to several aggregation scenarios of MPEG video sources. The analytic results are validated against empirical results. Minimum token bucket parameter curves obtained by analysis and via experiments match well. This is particularly true in the region relevant to the adopted optimization criteria. Thus, the analytic approach presented here is effective in optimally sizing token buckets for LRD traffic, and has potential in wider contexts under different traffic conditions, as well as for various optimization criteria.