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 di#erent behavior of token buckets when acting on LRD vs. SRD traffic with identical average and peak rates. The comparison shows that LRD tra#c 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 e#ective 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.
On Long Range Dependance and Token Buckets
PROCISSI, GREGORIO;
2001-01-01
Abstract
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 di#erent behavior of token buckets when acting on LRD vs. SRD traffic with identical average and peak rates. The comparison shows that LRD tra#c 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 e#ective 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
