In order to stem the increasing packet loss rates caused by an exponential increase in network traffic, the <sc>ietf</sc> has been considering the deployment of active queue management techniques such as <sc>Red</sc> . While active queue management can potentially reduce packet loss rates in the Internet, we show that current techniques are ineffective in preventing high loss rates. The inherent problem with these queue management algorithms is that they use queue lengths as the indicator of the severity of congestion. In light of this observation, a fundamentally different active queue management algorithm, called <sc>Blue</sc>, is proposed, implemented, and evaluated. <sc>Blue</sc> uses packet loss and link idle events to manage congestion. Using both simulation and controlled experiments, <sc>Blue</sc> is shown to perform significantly better than <sc>Red</sc>, both in terms of packet loss rates and buffer size requirements in the network. As an extension to <sc>Blue</sc>, a novel technique based on Bloom filters  is described for enforcing fairness among a large number of flows. In particular, we propose and evaluate Stochastic Fair <sc>Blue</sc> (SFB), a queue management algorithm which can identify and rate-limit nonresponsive flows using a very small amount of state information.