GA-BASED OPTIMAL DIMENSIONING OF 3-LEVEL TRAFFIC SHAPER FOR STATISTICAL MULTIPLEXING IN ATM NETWORKS

in this paper we discuss genetic algorithm (GA)-based optimal dimensio ning of the three-level traffic shaper (TLTS) for statistical multiple xing in ATM networks. As the objective function for the optimal dimens ioning, we consider three alternatives - the variance of the output ce ll service periods, the variance of the output cell rates, and the sum of squared differences of adjacent cell service periods. We perform s imulations for optimal dimensioning of the TLTS for statistical multip lexing of VBR video services, and we confirm that the objective functi on based on the variance of the output cell rates is the most suitable among the three alternatives. The framework of the optimal dimensioni ng procedure introduced in this paper can be easily extended to other optimal design problems in ATM networks for which traditional gradient -based optimal dimensioning methods or random search techniques are no t easily applicable due to the nonlinearities and complexities of the related functions and the discreteness of the involved parameters.