Performance evaluation of access control mechanism corresponding to stochastic inputs

Abstract

In this thesis, we construct traffic models, which exhibit both short-range dependence (SRD) and long-range dependence (LRD) using Poisson process and doubly stochastic Poisson (or Cox) process (DSPP) driven by fractional Brownian motion (FBM). We also develop a novel dynamic system model for the token bucket (TB) control algorithm used in computer networks. In this model, token buckets police incoming traffic; and one multiplexor serving all the token pools multiplexes conforming traffic using round robin scheme. The state of the system is formally defined and control strategies are also proposed. We study several issues related to performance corresponding to different stochastic inputs using the proposed model located at the edge of the backbone network. The numerical results demonstrate that this system can be adapted to any kind of stochastic traffic. The results can be served as a tool for the designers of such controllers to set up different system parameters.