Наличие долговременной зависимости в современных сетях передачи данных приводит к тому, что объем передаваемого трафика может быть большим на протяжении значительного периода времени. Это, в свою очередь, влечет перегрузку систем на протяжении длительного периода времени. В данной работе рассматривается задача оценки вероятности занятости системы обслуживания с гауссовским входным потоком в течение некоторого заданного периода T. При больших значениях T интересующее нас событие является редким, и для оценки его вероятности с приемлемой точностью необходимо использовать специальные методы понижения дисперсии оценки. В статье рассмотрен частный случай условного метода Монте Карло, который заключается в том, что искомая вероятность может быть выражена как математическое ожидание некоторой функции от так называемого гауссовского моста. Исследована эффективность предложенной процедуры, а также влияние шага дискретизации на свойство получаемой оценки.

Long-term correlation is a key feature of traffic flows and has a deep impact on network performance. Indeed, the arrival rate can persist on relatively high values for a considerable amount of time, provoking long busy periods and possibly bursts of lost packets. The authors focus on Gaussian processes, well-recognized and flexible traffic models, and consider the probability that the normalized cumulative workload grows at least as the length T of the considered interval. As T increases, such event becomes rare and ad-hoc techniques should be used to estimate its probability. To this aim, the authors present a variant of the well-known conditional Monte-Carlo (MC) method, in which the target probability is expressed as a function of the corresponding bridge process. In more detail, they derive the analytical expression of the estimator, verify its effectiveness through simulations (for different sets of parameters), and investigate the effects of the discretization step.

On the efficiency of bridge Monte-Carlo estimator

Pagano, M.
2017-01-01

Abstract

Long-term correlation is a key feature of traffic flows and has a deep impact on network performance. Indeed, the arrival rate can persist on relatively high values for a considerable amount of time, provoking long busy periods and possibly bursts of lost packets. The authors focus on Gaussian processes, well-recognized and flexible traffic models, and consider the probability that the normalized cumulative workload grows at least as the length T of the considered interval. As T increases, such event becomes rare and ad-hoc techniques should be used to estimate its probability. To this aim, the authors present a variant of the well-known conditional Monte-Carlo (MC) method, in which the target probability is expressed as a function of the corresponding bridge process. In more detail, they derive the analytical expression of the estimator, verify its effectiveness through simulations (for different sets of parameters), and investigate the effects of the discretization step.
2017
Lukashenko, O. V.; Morozov, E. V.; Pagano, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/892629
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