Complex network protocols and various network services require significant processing capability for modern intelligent network applications. One of the significant features in modern networks is differentiated service. Along with differentiated service, rapidly changing network environments result in congestion problems. In this paper, we analyze the characteristics of representative congestion control applications - scheduling and queue management algorithms, and we propose application-specific acceleration technique using ILP (Instruction Level Parallelism) and PLP (Packet Level Parallelism). From the ILP perspective, new instruction set extensions for fast conditional operations are applied for congestion control applications. Based on our experiments, proposed architectural extensions show 10-12% improvement in performance for instruction set enhancements. For PLP, we propose a hardware acceleration model based on detailed analysis of congestion control applications. In order to get large throughputs, large number of processing elements and a parallel comparator are designed. Such hardware accelerators provide large parallelism proportional to the number of processing elements added. As the performance of general purpose processors rapidly increases, defining architectural extensions (e.g., MMX as in multimedia applications) for general purpose processors could be an alternative solution for wide range of network applications.