A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Random number generators: good ones are hard to find
Communications of the ACM
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
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During the last decades stochastic simulation methods have been utilized more and more in different research fields to describe complicated real-world phenomena. Reasons for the popularity are dramatic increase in processing power and significant decrease of price of computing systems. However, the main reason may be that the probability theory is a well-known tool for presentation and processing of stochastic information. In this article are described fundamental stochastic features of communication network simulation models. Especially, a concept of convergence time is considered and its numerical evaluation is defined by an example simulations. Network models considered here are event based discrete time models, which utilise Markov chain theory for state transitions and Monte Carlo method for duration times of events. Numerous reported case studies of stochastic simulations do not contain any information about a convergence time of the model. Therefore, it is possible that the reported results are randomly biased due to too short simulation (shorter than the convergence time) periods as illustrated in this article by examples.