Long-Range Dependence

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).

This paper looks at a markov chain based model and uses queuing theory to analyse its performance. The system is D-BMAP/D/1 and a closed form solution is found

The aim of this paper is to provide a summary and a critique of power law modelling in the internet. Long-range dependence and self-similarity are considered as well as scale-free topology analysis.

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.

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