Advanced Methods for the Estimation of the Origin Destination Traffic Matrix

Sandrine Vaton, Jean-Sébastien Bedo and Annie Gravey

Chapter Chapter 8 in Performance Evaluation and Planning Methods for the Next Generation Internet, 2005, pp 189-225 from Springer

Abstract: Abstract For lots of traffic engineering tasks, telecommunications operators need good knowledge about the traffic which transit through their networks. This information is fully represented by the matrix of the volumes of data which go from any entry node to any exit node during a period of time. This matrix is called the origin-destination (OD) traffic matrix. However such a matrix is not directly available. Only measures of the volumes of data which transit through a link between routers can be obtained easily with the help of Simple Network Management Protocol (SNMP). These measures are called link counts. Lots of techniques have been proposed to estimate the traffic matrix from the link counts. Among those, statistical methods propose to model the demand for each OD pair in order to chose a possible traffic matrix which fits the reality of networks. Nevertheless, the model is often arbitrary and don’t take into account the temporal dimension of traffic. In this paper, we claim that a temporal model of the traffic for each OD pair could be trained from the link counts only. We prove the validity of our approach on a one router network on which direct measurements of the OD counts were made available. Then, we compare our results to other methods and show that their accuracy is the best.

Keywords: Markov Chain Monte Carlo; Prior Distribution; Gravity Model; Expectation Maximization Algorithm; Hide Data (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-387-25551-4_8

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DOI: 10.1007/0-387-25551-6_8

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