Multivariate Fairly Normal Traffic Model for Aggregate Load in Large-Scale Data Networks

Traffic models are crucial for network planning, design, performance evaluation and optimization. However, it is first necessary to assess the validity of the newly proposed models. In this paper we present the validation of a multivariate fairly normal model for aggregate traffic that exploits the well-known day-night traffic pattern, which was first assumed and applied in a former work to detect changes in the Internet links’ load on-line. The validation process entails several normality analytical and graphical tests which are applied to real network traffic measurements, on attempts to assess fairly normality both in the marginal and joint distributions of the multivariate model. The results of the normality tests provide evidence that our design is adequate to model aggregate traffic accurately capturing the day-night traffic pattern.

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