This thesis studies the effect of the traffic bursts in the queue. Knowledge of the queueing behavior provides opportunity for additional control and improved performance. Most existing work on queueing today is based on Long-Range-Dependence (LRD) and Self-similarity, two well-known properties of network traffic at large scales. However, network traffic shows bursty behavior on small scales which are not captured by traditional self-similar models. We leverage a decomposition of traffic into two components. The alpha component is the bursty part of the traffic consisting of only few high bandwidth connections. The beta component collects the residual traffic and is a Gaussian LRD process. The alpha component is highly non-Gaussian and bursty. We propose two models for the alpha component, a heavy-tailed self-similar process and a high rate ON/OFF source. Our results explain how size and type of bursts affect the queueing behavior.