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In packet networks, congestion events tend to persist, producing large delays and long bursts of consecutive packet loss resulting in perceived performance degradations. The length and rate of these events have a significant effect on network quality of service (QoS). The packet delay resulting from these congestion events also influences QoS. In this paper a technique for predicting these properties of congestion events in the presence of fractional Brownian motion (fBm) traffic is developed.