Scaling Properties of Network Traffic

Scaling Properties of Network Traffic

David Rincón (Technical University of Catalonia (UPC), Spain) and Sebastià Sallent (Technical University of Catalonia (UPC), Spain)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59140-993-9.ch068
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Abstract

The availability of good stochastic models of network traffic is the key to developing protocols and services. A precise statistical characterization of packet interarrival time, size distribution, and connection arrival rate help network engineers to design network equipment and evaluate its performance.

Key Terms in this Chapter

Multifractality: A generalization of self-similarity in which the small time-scale behaviour of the process shows local variations in the scaling parameter.

Heavy-Tailed Distribution: A statistical distribution with tails that decay subexponentially.

Hurst Parameter: A qualitative measure of selfsimilarity related to the scaling parameter a defined for long-range dependent processes.

Aggregation (of a time series): The action of averaging the time series over non-overlapping blocks of constant size.

Long-Range Dependence (also known as long memory): A property found in stochastic processes with strong low-frequency components.

Fractals: Objects (in particular, figures) that have the same appearance when they are seen on fine and coarse scales.

Self-Similarity: When applied to stochastic processes, it indicates that the process follows the same distribution on all time scales.

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