On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
On routes and multicast trees in the Internet
ACM SIGCOMM Computer Communication Review
A practical guide to heavy tails
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Evidence for long-tailed distributions in the internet
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
A first-principles approach to understanding the internet's router-level topology
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
A pragmatic approach to dealing with high-variability in network measurements
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Fractals and Scaling In Finance: Discontinuity, Concentration, Risk
Fractals and Scaling In Finance: Discontinuity, Concentration, Risk
On TCP and self-similar traffic
Performance Evaluation - Long range dependence and heavy tail distributions
Understanding internet topology: principles, models, and validation
IEEE/ACM Transactions on Networking (TON)
Sports analogy for modelling of combat in the air domain
WSC '05 Proceedings of the 37th conference on Winter simulation
Callgraph properties of executables
AI Communications - Network Analysis in Natural Sciences and Engineering
Tackling the complexity of future networks
IWAN'04 Proceedings of the 6th IFIP TC6 international working conference on Active networks
Contrasting views of complexity and their implications for network-centric infrastructures
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On name-based inter-domain routing
Computer Networks: The International Journal of Computer and Telecommunications Networking
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One feature of many naturally occurring or engineered complex systems is tremendous variability in event sizes. To account for it, the behavior of these systems is often described using power law relationships or scaling distributions, which tend to be viewed as "exotic" because of their unusual properties (e.g., infinite moments). An alternate view is based on mathematical, statistical, and data-analytic arguments and suggests that scaling distributions should be viewed as "more normal than Normal". In support of this latter view that has been advocated by Mandelbrot for the last 40 years, we review in this paper some relevant results from probability theory and illustrate a powerful statistical approach for deciding whether the variability associated with observed event sizes is consistent with an underlying Gaussian-type (finite variance) or scaling-type (infinite variance) distribution. We contrast this approach with traditional model fitting techniques and discuss its implications for future modeling of complex systems.