Modeling and simulation of self-similar variable bit rate compressed video: a unified approach
SIGCOMM '95 Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Fast, approximate synthesis of fractional Gaussian noise for generating self-similar network traffic
ACM SIGCOMM Computer Communication Review
Correlational and distributional effects in network traffic models
Performance Evaluation
Practical aspects of simulating systems having arrival processes with long-range dependence
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Numerical Methods for Fitting and Simulating Autoregressive-To-Anything Processes
INFORMS Journal on Computing
Generation of self-similar processes for simulation studies of telecommunication networks
Mathematical and Computer Modelling: An International Journal
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
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Stochastic discrete-event simulation studies of communication networks often require a mechanism to transform self-similar processes with normal marginal distributions into self-similar processes with arbitrary marginal distributions. The problem of generating a self-similar process of a given marginal distribution and an autocorrelation structure is difficult and has not been fully solved. Our results presented in this paper provide clear experimental evidence that the autocorrelation function of the input process is not preserved in the output process generated by the inverse cumulative distribution function (ICDF) transformation, where the output process has an infinite variance. On the other hand, it preserves autocorrelation functions of the input process where the output marginal distributions (exponential, gamma, Pareto with α= 20.0, uniform and Weibull) have finite variances, and the ICDF transformation is applied to long-range dependent self-similar processes with normal marginal distributions.