Network traffic exhibits drastically different statistics, ranging from nearly Gaussian marginals and long range dependence at very large time scales to highly non-Gaussian marginals and multifractal scaling on small scales. This behavior can be explained by decomposing traffic into two components according to the connection bandwidth: the small bandwidth component absorbs most traffic and is Gaussian, while large bandwidth component constitutes virtually all of the small scale bursts. Based on this understanding, a novel traffic model is proposed that parsimoniously accounts for user behavior, network topology, and the heterogeneous distribution of network bandwidths.