Effective bandwidths at multi-class queues
Queueing Systems: Theory and Applications
D-BIND: an accurate traffic model for providing QoS guarantees to VBR traffic
IEEE/ACM Transactions on Networking (TON)
On the nonstationarity of Internet traffic
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Longitudinal study of Internet traffic in 1998-2003
WISICT '04 Proceedings of the winter international synposium on Information and communication technologies
Understanding Internet traffic streams: dragonflies and tortoises
IEEE Communications Magazine
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
Admission control for statistical QoS: theory and practice
IEEE Network: The Magazine of Global Internetworking
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Current methods for modelling network traffic bandwidth do not sufficiently take into account the impacts of traffic variability. The most common models for characterizing network traffic bandwidth are those based on the theories of Illka Norros using fractal Brownian motion [1] or those based on Frank Kelly's theories using stochastic processes [2]. In both cases the accuracy of the model is dependant on values derived from measured Internet data. These measurements are often sampled at a limited number of points on the network. The task of collecting, evaluating, and constantly updating the data in order to maintain required accuracy is extremely difficult. Data characteristics vary greatly according to time, location on the network, applications in use, and the behavior of users. This paper details the results of a survey examining the degree of network traffic variability on an example broadband network. Impacts are evaluated and the need for adaptive modelling techniques that utilize computational intelligence and adaptive capabilities is proposed.