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
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
The changing nature of network traffic: scaling phenomena
ACM SIGCOMM Computer Communication Review
Data networks as cascades: investigating the multifractal nature of Internet WAN traffic
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Self-Similar Network Traffic and Performance Evaluation
Self-Similar Network Traffic and Performance Evaluation
Queueing analysis of network traffic: methodology and visualization tools
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Dynamic resource management considering the real behavior ofaggregate traffic
IEEE Transactions on Multimedia
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
A multifractal wavelet model with application to network traffic
IEEE Transactions on Information Theory
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
Small-time scale network traffic prediction based on flexible neural tree
Applied Soft Computing
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This work extends the notion of the widely mentioned and used fractional Brownian traffic model in the literature. Extensive experimental investigations indicate that the proposed traffic model, named extended fractional Brownian traffic, can capture not only the self-similar properties, but also the inherent multifractal characteristics of those traffic flows found in modern communication networks. Additionally, the structure of this traffic model is taken into account in a traffic prediction algorithm that benefits from the more accurate traffic modeling. The experimental results clearly point out the advantages of using the proposed model in traffic modeling as well as in traffic prediction.