LATENCY-RATE SERVERS: A GENERAL MODEL FOR ANALYSIS OF TRAFFIC SCHEDULING ALGORITHMS
LATENCY-RATE SERVERS: A GENERAL MODEL FOR ANALYSIS OF TRAFFIC SCHEDULING ALGORITHMS
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
Performance Evaluation - Long range dependence and heavy tail distributions
Two different approaches for providing QoS in the Internet backbone
Computer Communications
Modeling a multi-queue network node with a fuzzy predictor
Fuzzy Sets and Systems
An efficient parallel optimization algorithm for the Token Bucket control mechanism
Computer Communications
Variance envelopes of the output traffic of leaky bucket policers
IEEE Transactions on Communications
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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 traffic 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 traffic 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.