On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Analysis, modeling and generation of self-similar VBR video traffic
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
What are the implications of long-range dependence for VBR-video traffic engineering?
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Fast, approximate synthesis of fractional Gaussian noise for generating self-similar network traffic
ACM SIGCOMM Computer Communication Review
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Calculus with Analytic Geometry: Using Maple, Mathematica, and MATLAB (Laboratory Manual)
Calculus with Analytic Geometry: Using Maple, Mathematica, and MATLAB (Laboratory Manual)
On resource management and QoS guarantees for long range dependent traffic
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 2)-Volume - Volume 2
Traffic Modeling of IP Networks Using the Batch Markovian Arrival Process
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Modeling IP traffic using the batch Markovian arrival process
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
Proceedings of the 35th conference on Winter simulation: driving innovation
Pricing of risk for loss guaranteed intra-domain internet service contracts
Computer Networks: The International Journal of Computer and Telecommunications Networking
Two approximation methods to synthesize the power spectrum of fractional Gaussian noise
Computational Statistics & Data Analysis
Generating Traffic Time Series Based on Generalized Cauchy Process
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Modeling of network traffic with self-similar process
CompSysTech '08 Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
A note on simulation of LRD network traffic
IMCAS'09 Proceedings of the 8th WSEAS international conference on Instrumentation, measurement, circuits and systems
Self-similarity and long-range dependence in teletraffic
MUSP'09 Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processing
Effective DDoS Attacks Detection Using Generalized Entropy Metric
ICA3PP '09 Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing
On fast generation of fractional Gaussian noise
Computational Statistics & Data Analysis
Algorithm for modeling self-similar ethernet traffic
CompSysTech '09 Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
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The present paper focuses on self-similar network traffic generation. Network traffic modeling studies the generation of synthetic sequences. The generated sequences must have similar features to the measured traffic. Exact methods for generating self-similar sequences are not appropriate for long traces. Our main objective in the present paper is to improve the efficiency of Paxson's method for synthesizing self-similar network traffic. Paxson's method uses a fast, approximate synthesis for the power spectrum of the FGN and uses the inverse Fourier transform to obtain the time-domain sequences. We demonstrate that a linear approximation can be used to determine the power spectrum of the FGN. This linear approximation reduces the complexity of the computation without compromising the accuracy in synthesizing the power spectrum of the FGN. Our results show that long traces can be generated in much less time. To compare our method with existing ones, we will measure the running time in generating long and short sample paths from the FGN. We will also conduct experiments to show that our method can generate self-similar traffic for specified Hurst parameters with high accuracy.