Traffic engineering of IP networks requires the characterization and modeling of network traffic on multiple time scales due to the existence of several statistical properties that are invariant across a range of time scales, such as selfsimilarity, LRD and multifractality. These properties have a significant impact on network performance and, therefore, traffic models must be able to incorporate them in their mathematical structure and parameter inference procedures. In this work, we address the modeling of network traffic using a multi-timescale framework. We evaluate the performance of two classes of traffic models (Markovian and Lindenmayer-Systems based traffic models) that incorporate the notion of time scale using different approaches: directly in the model structure, in the case of the Lindenmayer-Systems based models, or indirectly through a fitting of the second-order statistics, in the case of the Markovian models. In addition, we address the importance of modeling packet si...