Non-stationarity and high-order scaling in TCP flow arrivals: a methodological analysis
ACM SIGCOMM Computer Communication Review
Multifractality in TCP/IP traffic: the case against
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
Infinitely Divisible Cascades to Model the Statistics of Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov models of internet traffic and a new hierarchical MMPP model
Computer Communications
Stochastic processes for computer network traffic modeling
Computer Communications
Multifractality in TCP/IP traffic: the case against
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
Describing network traffic using the index of variability
IEEE/ACM Transactions on Networking (TON)
Discriminating internet applications based on multiscale analysis
NGI'09 Proceedings of the 5th Euro-NGI conference on Next Generation Internet networks
Internet traffic source based on hidden Markov model
NEW2AN'11/ruSMART'11 Proceedings of the 11th international conference and 4th international conference on Smart spaces and next generation wired/wireless networking
Framework based on stochastic L-Systems for modeling IP traffic with multifractal behavior
Computer Communications
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Infinitely divisible cascades are a model class previously introduced in the field of turbulence to describe the statistics of velocity fields. In this paper, using a wavelet reformulation of the cascades, we investigate their ability to analyze band model scaling properties of data and compare their fundamental ingredients to those of other scaling model classes such as self-similar and multifractal processes. We also propose an estimation procedure for the propagator or kernel of the cascades. Finally the cascade model is successfully applied to describe Internet TCP network traffic data, bringing new insights into their scaling properties and revealing a pitfall in existing techniques.