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Hamilton, John A.; Ratterree, Gary R.; Brutch, Paul C.; Pooch, Udo W.
doi: 10.1177/003754979606700303pmid: N/A
Simulation of computer networks is an area of growing interest. Network simulation is a multidisciplinary endeavor requiring expertise in computer networking, modeling and simulation, and software engineering. Network modeling and simulation tools in the research community provide important exemplar technology that has the potential for reuse. NetSim, developed at the Massachusetts Institute of Technology, is an excellent example of a network simulation infrastructure that can support a variety of modifications. In this paper we discuss NetSim and two additional tools: the Maryland Routing Simulator and the Texas A&M Asynchronous Transfer Mode Simulator, which are both modifications of NetSim. NetSim can serve as a simple but effective Ethernet or point-to-point network simulator. Additionally, NetSim can serve as a rapid prototyping tool for other network simulators.
Özdemirel, Nur E.; Yurttas, Gazanfer Y.; Köksal, Gülser
doi: 10.1177/003754979606700304pmid: N/A
As simulation has become a major tool in studying discrete manufacturing systems, experimental design issues have started to receive much attention. Although the importance of proper experimentation is often emphasized in the literature on discrete event simulation, it is neglected in most practical simulation studies. The main reason for this neglect is that the design and analysis of a simulation experiment require expertise in experimental design methodology as well as familiarity with traditional statistical output analysis methods. This article presents an attempt to structure the process of planning and designing simulation experiments, and proposes a knowledge- based system, namely Design Of Experi ments for Simulation (DOES), that assists the inexperienced analyst in this process. The proposed system takes the analyst through a planning process based on the simulation objectives.
Shahkar, G.H.; Tareghian, H.R.
doi: 10.1177/003754979606700305pmid: N/A
In this paper we determine and compare the optimal capacity (K) of a GI/G/1/K queuing system under social and individual optimization.It is shown by simulation that irrespective of the traffic intensity, p, and arrival and service time distributions, the K obtained from social optimization of the system is always equal to or less than the K obtained from its individual optimization.
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