Proceedings ArticleDOI
Capturing the complete multifractal characteristics of network traffic
Trang Dinh Dang,Sándor Molnár,István Maricza +2 more
- Vol. 3, pp 2355-2359
TLDR
The model is a combination of a multiplicative cascade with an independent lognormal process that has all the important properties observed in data traffic including long-range dependence, multifractality and lognormality and is flexible enough to capture the complete multifractal characteristics of data traffic.Abstract:
We propose a new multifractal traffic model for network traffic. The model is a combination of a multiplicative cascade with an independent lognormal process. We show that the model has all the important properties observed in data traffic including long-range dependence (LRD), multifractality and lognormality. We also demonstrate that the model is flexible enough to capture the complete multifractal characteristics of data traffic including both the scaling function and the moment factor. On the other hand, we argue that the model is simple from practical point of view having only three parameters. Practical applications for measured data traffic and validation of the model with queueing performance evaluation are also presented.read more
Citations
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Journal ArticleDOI
Queueing performance estimation for general multifractal traffic
TL;DR: An approximation for the tail asymptotics in an infinite capacity single server queue serviced at a constant rate driven by general multifractal input process is presented and it is shown that in the special and important case of the monofractal fractional Brownian motion input traffic it gives the well-known Weibullian tail.
Journal ArticleDOI
An admission control approach for multifractal network traffic flows using effective envelopes
TL;DR: This paper mathematically determines the variance of the aggregated traffic processes as well as their wavelet energy across time scales, considering a multiplicative cascade based multifractal model for the network traffic traces.
Journal ArticleDOI
Adaptive fuzzy flow rate control considering multifractal traffic modeling and 5G communications.
TL;DR: Comparisons with other predictive control schemes in the literature prove the efficiency of the adaptive GOBF-fuzzy based control in enhancing the performance of the system downlink as well as guaranteeing some QoS parameters.
Journal ArticleDOI
An adaptive fuzzy model using orthonormal basis functions based on multifractal characteristics applied to network traffic control
TL;DR: Comparisons to other predictive control schemes prove the efficiency of the proposed adaptive OBF-fuzzy based control and training algorithm and propose a predictive flow control scheme for broadband networks.
Proceedings ArticleDOI
Queueing analysis for multifractal traffic through network calculus and global scaling parameter
TL;DR: The proposed byte loss probability bound was shown to be tighter than that given by the large deviations theory and the performance bound estimation approach is evaluated by simulations with Internet and Ethernet traffic traces, verifying its efficiency.
References
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Journal ArticleDOI
On the self-similar nature of Ethernet traffic (extended version)
TL;DR: It is demonstrated that Ethernet LAN traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal-like behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks.
Book
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A storage model with self-similar input
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Journal ArticleDOI
A multifractal wavelet model with application to network traffic
TL;DR: A new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise) using the Haar wavelet transform and a special multiplicative structure on the wavelet and scaling coefficients to ensure positive results, which provides a rapid O(N) cascade algorithm for synthesizing N-point data sets.
Proceedings ArticleDOI
Data networks as cascades: investigating the multifractal nature of Internet WAN traffic
TL;DR: A simple construction based on cascades that allows for a plausible physical explanation of the observed multifractal scaling behavior of data traffic and suggests that the underlying multiplicative structure is a traffic invariant for WAN traffic that co-exists with self-similarity is provided.