A new method, based on the maximum likelihood principle, through the numerical Expectation–Maximization algorithm, is proposed to estimate traffic matrices when traffic exhibits long-range dependence. The methods proposed so far in the literature do not account for long-range dependence. The method proposed in the present paper also provides an estimate of the Hurst parameter. Simulation results show that: (i) the estimate of the traffic matrix is more efficient than those obtained via existing techniques; (ii) the estimation error of the traffic matrix is lower for larger values of the true traffic intensity; (iii) the estimate of the Hurst parameter is slightly negatively biased. Keywords. Traffic matrices, Long-range dependence, Traffic matrix estimation
Blind Maximum Likelihood Estimation of Traffic Matrices under Long Range Dependent Traffic / Conti, P. L; De Giovanni, Livia; Naldi, M.. - In: COMPUTER NETWORKS. - ISSN 1389-1286. - 54:15(2010), pp. 2626-2639. [10.1016/j.comnet.2010.04.012]
Blind Maximum Likelihood Estimation of Traffic Matrices under Long Range Dependent Traffic
DE GIOVANNI, LIVIA;
2010
Abstract
A new method, based on the maximum likelihood principle, through the numerical Expectation–Maximization algorithm, is proposed to estimate traffic matrices when traffic exhibits long-range dependence. The methods proposed so far in the literature do not account for long-range dependence. The method proposed in the present paper also provides an estimate of the Hurst parameter. Simulation results show that: (i) the estimate of the traffic matrix is more efficient than those obtained via existing techniques; (ii) the estimation error of the traffic matrix is lower for larger values of the true traffic intensity; (iii) the estimate of the Hurst parameter is slightly negatively biased. Keywords. Traffic matrices, Long-range dependence, Traffic matrix estimationFile | Dimensione | Formato | |
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