Design and analysis of a model predictive controller for active queue management

Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness.

► Model predictive control as a novel active queue management algorithm is proposed. ► Early packets at the router are dropped by optimizing the network performance. ► The queue length reaches the desired value with minimal tracking error. ► Randomized algorithms are applied to analyze the robustness successfully. ► The stability domain of systems with uncertain network parameters is provided.