On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Proof of a fundamental result in self-similar traffic modeling
ACM SIGCOMM Computer Communication Review
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Fractal Geometry in Digital Imaging
Fractal Geometry in Digital Imaging
Explaining World Wide Web Traffic Self-Similarity
Explaining World Wide Web Traffic Self-Similarity
Self-similar and fractal nature of internet traffic
International Journal of Network Management
Digital Signal Processing: Mathematical And Computational Methods, Software Development And Applications (Second Edition)
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There are many models in the literature that consider the main characteristics of Internet traffic noise either by processing real measurements of packets (or bytes) traffic in the time or the frequency domain. Fractals are applicable when the underlying processes being modeled have a similar appearance regardless of the time or observation scale and much of the traffic 'riding' the Internet can be modeled using fractals. Further, it is arguable that as the Internet has become larger and larger, the fractal nature of the traffic has become more and more pronounced. In this paper we introduce a novel method that depends on the frequency analysis through which we attempt to capture the fractal behavior of Internet traffic by adopting a Random Scaling Fractal model to characteristics of the traffic. The relevance and validation of the proposed model are demonstrated by application studies for measured Internet traffic.