In this thesis, we study the cause and impact of burstiness in computer network traffic. A connection-level analysis of traffic at coarse time scales (time scales greater than a round-trip-time) reveals that a single connection dominates during the period of the burst. The number of dominating connections that cause bursts is found to be a small fraction of the total number of connections. Removing the burst causing connections from the traffic yields a trace whose marginal is close to a Gaussian. This observation motivates a network traffic model comprised of two components, namely the Gaussian part and the bursty part. The Gaussian part of the traffic models the aggregate of majority of the connections, whereas the bursty part models the behavior of few dominant connections that transmit data at unusually high rates. The Gaussian component imparts long-range-dependence (LRD) to the traffic, whereas the bursty component gives rise to spikiness. We argue that heterogeneity in bottleneck link speeds gives rise to burstiness, and heavy tailed connection durations results in LRD. We perform simulations in ns to validate the proposed model and synthesize realistic traffic that is both non-Gaussian and LRD. We demonstrate the impact of the bursty component in queueing behavior. Although the bursty component constitutes a small fraction of the total traffic, it significantly affects the queueing behavior, in particular at large queue sizes.