Long-Range Dependence

On the Distribution of Traffic Volumes in the Internet and its Implications

Conference paper
Proc IEEE Infocom

This paper updates previous work on fitting traffic profiles. We use more modern statistical techniques to question (and refute) previous assumptions about heavy tails in statistics. In this case we believe that the best fit for traffic volume per unit time is the log-normal distribution. Tail distributions an have big impacts for capacity planning and for prediction of pricing (say 95th percentile).

Criticisms of modelling packet traffic using long-range dependence (extended version)

Journal Paper
Journal of Computer and System Sciences, 77(5)

This paper looks at the phenomenon of long-range dependence. It shows that certain long-range dependent models give answers which contain infinities and also that this behaviour will not be detected by a naive modelling approach. The work is an extension of an earlier published PMECT paper.

Criticisms of modelling packet traffic using long-range dependence

Workshop paper
Performance Modeling and Evaluation of Computer and Telecommunications (PMECT), ICCCN Workshop

This paper looks at the phenomenon of long-range dependence. It shows that certain long-range dependent models give answers which contain infinities and also that this behaviour will not be detected by a naive modelling approach. The work is extended in the JCCS paper

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