Effective Bandwidth Estimation in Data Networks: An Analysis for Two Traffic Characterizations

José Bavio (Departamento de Matemática, Universidad Nacional del Sur, Av. Alem 1253, Bahía Blanca, 8000, Argentina)
Carina Fernández (Departamento de Matemática, Universidad Nacional del Sur, Av. Alem 1253, Bahía Blanca, 8000, Argentina)
Beatriz Marrón (Departamento de Matemática, Universidad Nacional del Sur, Av. Alem 1253, Bahía Blanca, 8000, Argentina)

Abstract


The Generalized Markov Fluid Model (GMFM) is assumed for modeling sources in the network because it is versatile to describe the traffic
fluctuations. In order to estimate resources allocations or in other words the channel occupation of each source,the concept of effective bandwidth (EB) proposed by Kelly is used. In this paper we use an expression to determine the EB for this model which is of particular interest because it allows expressing said magnitude depending on the parameters of the model.This paper provides EB estimates for this model applying Kernel Estimation techniques in data networking.In particular we will study two differentiated cases:dispatches following a Gaussian and Exponential distribution.The performance of the proposed method is analyzed using simulated traffic traces generated by Monte Carlo Markov Chain algorithms.The estimation process worked much better in the Gaussian distribution case than in the Exponential one.


Keywords


Effective bandwidth;Markov fluid model;Kernel estimation;Data networking;Monte Carlo Markov Chain algorithms

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References


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DOI: https://doi.org/10.30564/ese.v3i1.3368

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Copyright © 2021 José Bavio, Carina Fernández, Beatriz Marrón


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