Wide-area traffic: the failure of Poisson modeling
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Analysis, modeling and generation of self-similar VBR video traffic
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
On estimating the intensity of long-range dependence in finite and infinite variance time series
A practical guide to heavy tails
Use of &agr;-stable self-similar stochastic processes for modeling traffic in broadband networks
Performance Evaluation - Special issue on performance and control of network systems
Self-Similar Network Traffic and Performance Evaluation
Self-Similar Network Traffic and Performance Evaluation
Macroscopic models for long-range dependent network traffic
Queueing Systems: Theory and Applications
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
The estimation of stable distribution parameters from teletrafficdata
IEEE Transactions on Signal Processing
FGN based telecommunication traffic models
WSEAS Transactions on Computers
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The paper analyzes the applicability of α-stable processes in traffic modelling. This study is suggested by the ability of α-stable processes in capturing not only the self-similarity of actual traffic, but also the heavy tails of its marginal distribution. The relevance of this property is proven by means of discrete event simulations carried out considering two different traffic data sets, related to a LAN-to-LAN interconnection and to an entertainment video service, respectively. The performance of the α-stable model is evaluated in terms of the ability in capturing the queuing behavior of actual traffic, in cases of realistic systems (i.e. with finite buffer) and ideal models (i.e. with infinite buffer). In both cases, the analysis is mainly carried out by means of discrete event simulations; in addition to this, in the infinite buffer scenario, a theoretical lower bound is also given and its tightness is discussed. The queuing simulations emphasize the improvements in the performance prediction introduced by the higher flexibility of the α-stable model with respect to the widely used fractional Brownian motion.