Many congestion control mechanisms have been proposed to solve the problems of a high loss rate and inefficient utilization of network resources in the present Internet. This problem is caused by competition between traffic flows while the network is congested. Differentiated Services (DiffServ) architecture permits the allocation of various levels of traffic resource requirements needed for Quality of Service (QoS). Random Early Detection (RED) is an efficient mechanism to pre-drop packets before actual congestion occurs, and it is capable of introducing a random early packet dropping scheme, and based on the queue length in reaching a certain degree of fairness for resource utilization. However, it still suffers from a lack of robustness among light traffic load, or in heavy traffic load using fixed RED parameters. In this dissertation, we modified the RED scheme and proposed a novel adaptive RED model, which we named the OURED model, to enhance the robustness of resource utilization so that it could be utilized in the DiffServ edge router. The OURED model introduces two additional packet dropping traces, one is Over Random Early Detection (ORED), which is used to speed up the dropping of packets when the actual rate is higher than the target rate, and the other one is the Under Random Early Detection (URED), used to slow down the packet dropping rate in the reverse situation. The simulation results show that OURED is not only more robust than MRED in resource utilization, but that it also can be implement efficiently in the DiffServ edge router. Another model proposed in this dissertation is the Age-Based packet discarding Traffic Conditioner. For the reason that the file sizes of on going flows are fairly disparate on the current network, we propose an ¡§Age-Based¡¨ packet discard scheme in the Traffic Conditioner of a gateway, to improve the performance of file transmission. The on going flows will be grouped to three classes of priority according to their ¡§age¡¨ as network congestion occurs and the simulation results show that the proposed model can work efficiently in most of the congestion conditions.