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ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 17 Issue 4, September 2007

Common defects in initialization of pseudorandom number generators
Makoto Matsumoto, Isaku Wada, Ai Kuramoto, Hyo Ashihara
Article No.: 15
DOI: 10.1145/1276927.1276928

We demonstrate that a majority of modern random number generators, such as the newest version of rand.c, ranlux, and combined multiple recursive generators, have some manifest correlations in their outputs if the initial state is filled up using...

Multistep-ahead neural-network predictors for network traffic reduction in distributed interactive applications
Aaron Mccoy, Tomas Ward, Seamus Mcloone, Declan Delaney
Article No.: 16
DOI: 10.1145/1276927.1276929

Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is...

Discrete-time heavy-tailed chains, and their properties in modeling network traffic
José Alberto Hernández, Iain W. Phillips, Javier Aracil
Article No.: 17
DOI: 10.1145/1276927.1276930

The particular statistical properties found in network measurements, namely self-similarity and long-range dependence, cannot be ignored in modeling network and Internet traffic. Thus, despite their mathematical tractability, traditional Markov...

Inverse transformed density rejection for unbounded monotone densities
Wolfgang Hörmann, Josef Leydold, Gerhard Derflinger
Article No.: 18
DOI: 10.1145/1276927.1276931

A new algorithm for sampling from largely arbitrary monotone, unbounded densities is presented. The user has to provide a program to evaluate the density and its derivative and the location of the pole. Then the setup of the new algorithm...

A framework for locally convergent random-search algorithms for discrete optimization via simulation
L. Jeff Hong, Barry L. Nelson
Article No.: 19
DOI: 10.1145/1276927.1276932

The goal of this article is to provide a general framework for locally convergent random-search algorithms for stochastic optimization problems when the objective function is embedded in a stochastic simulation and the decision variables are...