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)
Why we don't know how to simulate the Internet
Proceedings of the 29th conference on Winter simulation
Fast, approximate synthesis of fractional Gaussian noise for generating self-similar network traffic
ACM SIGCOMM Computer Communication Review
Self-similarity and heavy tails: structural modeling of network traffic
A practical guide to heavy tails
ACM SIGCOMM Computer Communication Review
An Introduction to Digital Signal Processing with Mathcad
An Introduction to Digital Signal Processing with Mathcad
Simplifying the synthesis of internet traffic matrices
ACM SIGCOMM Computer Communication Review
Non-Gaussian and Long Memory Statistical Characterizations for Internet Traffic with Anomalies
IEEE Transactions on Dependable and Secure Computing
Stochastic processes for computer network traffic modeling
Computer Communications
Generation of self-similar processes for simulation studies of telecommunication networks
Mathematical and Computer Modelling: An International Journal
Self-similar processes in communications networks
IEEE Transactions on Information Theory
Traffic models in broadband networks
IEEE Communications Magazine
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This paper addresses the generation of long-range dependent (LRD) network traffic with very big variance. In doing so, it first reviews two commonly used methods of random data generation. The one is the random data generation according to a given probability density function (PDF). The other is the method of synthesizing random data based on a given power spectrum density (PSD) function. Then, the limitations of both methods in synthesizing long-range dependent (LRD) network traffic with very big variance are explained. Based on those, the method of random data generation for a predetermined autocorrelation function (ACF) is discussed. Its advantages in comparison with the PDF-based method and the PSD-based one are interpreted. In addition, three methods of the white noise generation are addressed.