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|Type:||Artigo de evento|
|Title:||Accurate Heavy Tail Distribution Approximation For Multifractal Network Traffic|
|Abstract:||In this paper, we propose the use of a Gaussian mixture model to represent the heavy tail distribution of modern network traffic traces. Another novel contribution of this work is the derivation of a general expression for loss probability estimation in a single server queueing system for traffic traces with multifractal characteristics. The efficiency of this statistical modeling and the accuracy of the estimated loss probabilities are experimentally validated by comparing with other four multifractal based approaches: two of them considering two specific heavy tail distributions (lognormal, Pareto) and the well-known MSQ (Multiscale Queue) and CDTSQ (Critical Dyadic Time-Scale Queue) methods.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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