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)
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)
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
On the use of self-similar processes in network simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on modeling and simulation of communication networks
Internet research needs better models
ACM SIGCOMM Computer Communication Review
Traffic models in broadband networks
IEEE Communications Magazine
The application of optical packet switching in future communication networks
IEEE Communications Magazine
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
Characterization of the busy-hour traffic of IP networks based on their intrinsic features
Computer Networks: The International Journal of Computer and Telecommunications Networking
Modeling network traffic in mobile networks implementing offloading
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
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The particular statistical properties found in network measurements, namely self-similarity and long-range dependence, cannot be ignored in modeling network and Internet traffic. Thus, despite their mathematical tractability, traditional Markov models are not appropriate for this purpose, since their memoryless nature contradicts the burstiness of transmitted packets. However, it is desirable to find a similarly tractable model which is, at the same time, rigorous at capturing the features of network traffic. This work presents discrete-time heavy-tailed chains, a tractable approach to characterize network traffic as a superposition of discrete-time “on/off” sources. This is a particular case of the generic “on/off” heavy-tailed model, thus shows the same statistical features as the former, particularly self-similarity and long-range dependence, when the number of aggregated sources approaches infinity. The model is then applicable to characterize a number of discrete-time communication systems, for instance, ATM and optical packet switching, to further derive meaningful performance metrics such as average burst duration and the number of active sources in a random instant.