Fitting Self-Similar Traffic by a Superposition of MMPPs Modeling the Distribution at Multiple Time Scales

Antonio NOGUEIRA  Paulo SALVADOR  Rui VALADAS  Antonio PACHECO  

Publication
IEICE TRANSACTIONS on Communications   Vol.E87-B   No.3   pp.678-688
Publication Date: 2004/03/01
Online ISSN: 
DOI: 
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Fundamental Theories
Keyword: 
traffic modeling,  self-similar,  time scale,  Markov Modulated Poisson Process,  

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Summary: 
Measuring and modeling network traffic is of key importance for the traffic engineering of IP networks, due to the growing diversity of multimedia applications and the need to efficiently support QoS differentiation in the network. Several recent measurements have shown that Internet traffic may incorporate long-range dependence and self-similar characteristics, which can have significant impact on network performance. Self-similar traffic shows variability over many time scales, and this behavior must be taken into account for accurate prediction of network performance. In this paper, we propose a new parameter fitting procedure for a superposition of Markov Modulated Poisson Processes (MMPPs), which is able to capture self-similarity over a range of time scales. The fitting procedure matches the complete distribution of the arrival process at each time scale of interest. We evaluate the procedure by comparing the Hurst parameter, the probability mass function at each time scale, and the queuing behavior (as assessed by the loss probability and average waiting time), corresponding to measured traffic traces and to traces synthesized according to the proposed model. We consider three measured traffic traces, all exhibiting self-similar behavior: the well-known pOct Bellcore trace, a trace of aggregated IP WAN traffic, and a trace corresponding to the popular file sharing application Kazaa. Our results show that the proposed fitting procedure is able to match closely the distribution over the time scales present in data, leading to an accurate prediction of the queuing behavior.