Range Of Traffic Intensities Research Articles

ANALISA WAKTU TUNGGU DI LAYANAN ADMINISTRASI AKADEMIK UNIVERSITAS BUNDA MULIA DENGAN METODE SIMULASI

<p><em><span style="font-family: Times New Roman; font-size: medium;">Academic Administration Service management continues to be a major challenge for many Universities or school organizations as Universities are required to provide more services with less resources. Short response times for service are essential to avoid disruptions to University’s day to day activities or Academic Administration Service’s facilities. Managers must regularly assess their manpower needs, and ensure that their allocations and operational decisions lead to the best service at the lowest cost. Service staff allocation and response-time in service involves many challenging problems, because the mean and variance of the response-time in service can increase dramatically with traffic intensity, consequently the design for this system has to be able to cope with this complication. This experiment discusses how to use simulation model to improve response-time in service operations. Simulation experiments for analyzing the steady-state behavior of queuing systems over a range of traffic intensities are considered the best method of solution. </span></em></p><p><em><span style="font-family: Times New Roman; font-size: medium;">The technique of simulation consists of taking random samples from the probability distribution which represents the real-world system. </span></em></p><p><em><span style="font-family: Times New Roman; font-size: medium;">In this research the effects of dependent departure intervals on waiting times are examined for a one-station queuing system, and inter-arrival times are compared to a computer-simulated inter-arrival times having dependent arrivals. Significant differences in service times are found due to the mean and variance of the service times.</span></em></p><p><em><span style="font-family: Times New Roman; font-size: medium;">The purpose of this research is to propose a new dynamic-server queuing model to increase system efficiency and customer satisfaction compared with the current practice. </span></em></p><p> </p><p>Key Words,</p><p><em><span style="font-family: Times New Roman; font-size: medium;">Interarrival Time, Service Time, and Simulation.</span></em></p>

Design and performance issues of slave–slave cluster interconnection in 802.15.4 beacon enabled networks†

In this paper, we consider interconnection of IEEE 802.15.4 beacon enabled network clusters using ordinary network nodes as bridge nodes. Bridge design involves uplink transmission to the coordinator and downlink transmission to the bridge. The bridge periodically visits source and sink cluster and exchanges data using slotted CSMA-CA access. We develop an algorithm for inter-cluster communication and evaluate its performance for the array of three interconnected clusters. We indicate the range of traffic intensity where this design solution is operational.

Blocking in wavelength-routed optical networks with heterogeneous traffic

In this paper, we present a new analytical model that can give an accurate estimation of the blocking probabilities in wavelength-routed optical networks with heterogeneous traffic. By heterogeneous, we mean that each session offered to the network has its own traffic intensity and burstiness. In such cases, the blocking probability of a session is determined by the busy-wavelength distributions of the links seen at the arrival points of its calls. Thus, we first present two single-link models to estimate the arrival-point busy-wavelength distribution of a link with heterogeneous traffic: the full-population (FP) model and the reduced-population (RP) model. Both models are based on the BPP/M/W/W model, where the first two moments of an arbitrary session are matched by those of a birth-death process whose arrival rate linearly varies with the average number of busy wavelengths occupied by its own calls. We show that different sessions have different arrival-point busy-wavelength distributions depending on the burstiness of their traffic, i.e., a bursty session observes the link more congested than a smooth session. Next, we provide two extensions of the single-link models, the FP-full-load link-correlation model and the RP-reduced-load link-correlation model, to estimate the blocking probabilities of optical networks with heterogeneous traffic and sparse wavelength conversion. Both models employ the existing link-correlation models to take into account the occupied-wavelength-index correlation between two adjacent links. By comparing the results from the models with simulation results, we demonstrate that both models well approximate the blocking probabilities of individual sessions, as well as the network-wide blocking probability, for a wide range of traffic intensity, burstiness, and heterogeneity.

Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses

Simulation experiments for analysing the steady-state behaviour of queueing systems over a range of traffic intensities are considered, and a procedure is presented for improving their design. In such simulations the mean and variance of the response output can increase dramatically with traffic intensity; the design has to be able to cope with this complication. A regression metamodel of the likely mean response is used consisting of two factors, namely, a low-degree polynomial and a factor accounting for the exploding mean as the traffic intensity approaches its saturation. The best choice of traffic intensities at which to make simulation runs depends on the variability of the simulation output, and this variability is estimated using analytical heavy traffic results. The optimal numbers of customers simulated at each traffic intensity are built up using a multistage procedure. The asymptotic properties of the procedure are investigated theoretically. The procedure is shown to be robust and to be more efficient than more naive procedures. A result of note is that even when the range of interest includes high traffic intensities, the highest traffic load simulated should remain well away from its upper limit; but the number of customers simulated should be concentrated at the higher traffic intensities used. Empirical results are included for simulations of a single server queue with different priority rules and for a complicated queueing network. These results support the theoretical results, demonstrating that the proposed procedure can increase the accuracy of the estimated metamodel significantly compared with more naive methods.