Active Queue Management (AQM) is an enhancement mechanism to end-to-end congestion control

which can maintain smaller queuing delay and higher throughput by purposefully dropping the packets at intermediate nodes.Almost all the existing AQM schemes follow the probability dropping mechanism originated from Random Early Detection (RED).It is essentially a decision process with aid of information about network congestion that AQM decides whether or not to drop an incoming packet

thus the probability discrimination should not be only one way.We firstly summarize the properties of an ideal AQM scheme

and then develop a novel framework for AQM scheme based on the two-category classifier

which is considerably simple and extensible.A two-dimensional two-category classifier (TCC) for AQM is designed using the Fisher Linear Discriminate approach.The simulation results show that TCC outperforms other well-known AQM schemes in the integrated performance

namely TCC is an effective

prompt and robust algorithm.Additionally

TCC scheme requires few CPU cycles

which makes it deploy in high-speed routers simply.