1,720,975 research outputs found
Estimation of loss probability in Gaussian queue
No full textWe discuss the application of the simulation to estimate the loss probability in a queueing system with a finite buffer, which is fed by a Gaussian input. Such estimation is based on known approximation, but less known than estimation of the overflow probability in an infinite buffer system. We focus on queues with fractional Brownian input (fBi) and Brownian input (Bi). For the Bi, regenerative simulation is applied to analyze the accuracy of the analytical approximation
Performance analysis of Bridge Monte-Carlo Estimator
No full textThe overflow probability is an important QoS (Quality of Service) parameter. In this paper, we analyze the performance of Bridge Monte-Carlo (BMC), an interesting approach for the estimation of the overflow probability for queueing systems fed by a Gaussian input process
Regenerative simulation of the loss probability in the finite buffer queue with Brownian input
No full textOn the efficiency of bridge Monte-Carlo estimator
No full textНаличие 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
Regenerative analysis of a finite buffer fluid queue
No full textWe discuss the application of the regenerative simulation of estimate the loss probability in a queueing system with finite buffer which is fed by a Gaussian input. We mainly consider queue with Brownian input. Stability analysis is discussed and some numerical examples are also included
Some analytical aspects of regenerative simulation of fluid models
No full textBrownian input is an important particular case of the Gaussian processes, which are now well-recognized model to describe the traffic dynamics of a wide class of modern telecommunication networks. We discuss application of the regenerative theory and estimate the loss (blocking) probability in a queueing system with finite buffer which is fed by a Brownian input. The simulation technique is used because the explicit analytical result is not available for such type of systems. To validate the correctness of simulation we compare received numerical results with known values for infinite buffer systems which is hopefully exist for systems with Brownian input.
The loss rate process has typically very complicated dependence structure, and it makes evaluation of its parameters very hard problem in the framework of classical statistics. Hopefully, Brownian process has independent increments, and thus stationary performance of the considered model can be accurately estimated by means of well-developed regenerative simulation technique.
In particular, we develop confidence estimation of the stationary loss probability using classical regenerations which occur when the system (server and buffer) becomes empty. And different types of regeneration points are considered. Another important aspect is discretization. We explore the influence of discretization step to abstained results. Some numerical results are assumed to be included
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