Intelligent price-based congestion control for communication networks

Abstract

Numerous active queue management (AQM) schemes have been proposed to stabilize the queue length in routers, but most of them lack adequate adaptability to TCP dynamics, due to the nonlinear and time-varying nature of communication networks. To deal with the above problems, we propose an intelligent price-based congestion control algorithm named IPC. IPC measures congestion through using an intelligent price derived from neural network. To meet the purpose of AQM, we design learning algorithms to optimize the weights of neural network and the key parameter of IPC automatically. IPC acts as an adaptive controller which is able to detect both incipient and current congestion proactively and adaptively under dynamic network conditions. The simulation results demonstrate that IPC significantly outperforms the well-known AQM algorithms in terms of stability, responsiveness and robustness over a wide range of network scenarios.