A procedure for generating time-dependent arrivals for queueing simulations (original) (raw)
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Queueing models can be used to model and analyze the performance of various subsystems in telecommunication networks; for instance, to estimate the packet loss and packet delay in network routers. Since time is usually synchronized, discretetime models come natural. We start this paper with a review of suitable discrete-time queueing models for communication systems. We pay special attention to two important characteristics of communication systems. First, traffic usually arrives in bursts, making the classic modeling of the arrival streams by Poisson processes inadequate and requiring the use of more advanced correlated arrival models. Second, different applications have different quality-of-service requirements (packet loss, packet delay, jitter, etc.). Consequently, the common first-come-first-served (FCFS) scheduling is not satisfactory and more elaborate scheduling disciplines are required. Both properties make common memoryless queueing models (M/M/1-type models) inadequate. After the review, we therefore concentrate on a discrete-time queueing analysis with two traffic classes, heterogeneous train arrivals and a priority scheduling discipline, as an example analysis where both time correlation and heterogeneity in the arrival This invited paper is discussed in the comments available
International Journal of Engineering Research and Technology (IJERT), 2012
https://www.ijert.org/determination-of-expected-busy-periods-in-faster-and-slower-arrival-rates-of-an-interdependent-mm1-gd-queueing-model-with-controllable-arrival-rates https://www.ijert.org/research/determination-of-expected-busy-periods-in-faster-and-slower-arrival-rates-of-an-interdependent-mm1-gd-queueing-model-with-controllable-arrival-rates-IJERTV1IS5249.pdf In the present paper, an interdependent M/M/1 :(; GD) queueing model with controllable arrival rates has been taken into our consideration with an objective to determine the expected busy periods in both cases of faster and slower arrival rates taking into account that both the arrival and service processes are interdependent and these discrete random variables follow a bivariate Poisson distribution. By the end of the present paper, a special attention has also been made in our conclusion in order to focus the significance of the investigated average busy periods.