A nonstationary traffic train model for fine scale inference from coarse scale counts

The self-similarity of network traffic has been convincingly established based on detailed packet traces. This fundamental result promises the possibility of solving on-line and off-line traffic engineering problems using easily-collectible coarse time-scale data, such as SNMP measurements. This paper proposes a statistical model that supports predicting fine time-scale behavior of network traffic from coarse time-scale aggregate measurements. The model generalizes the commonly used fractional Gaussian noise process in two important ways: (1) it accommodates the recurring daily load patterns commonly observed on backbone links; and (2) features of long range dependence and self-similarity are modeled only at fine time scales and are progressively damped as the time period increases. Using the data we collected on the Chinese Education and Research Network, we demonstrate that the proposed model fits five-minute data and generates ten-second aggregates that are similar to actual ten-second data. Index Terms Daily pattern, Fractional Gaussian Noise, Poisson process.