Design and analysis of effective price for congestion control

Congestion control can be regarded a distributed system, which consists of source algorithm like TCP, and link algorithm, such as active queue management (AQM). Shadow price has been derived from optimization theory to be implemented in routers as the AQM algorithm. In this paper, a control theoretic approach to analysis and design of the price is presented to enhance the AQM performance. We analyze the dynamics of random exponential marking (REM) and propose an efficient price-based AQM algorithm. The proposed method uses an effective price with proportional-integral-derivative (PID) property to detect and control congestion proactively. Online learning rules are introduced to adjust the parameters of the effective price for improving adaptability and robustness in nonlinear and time- varying networks. The stability of the system is also analyzed via the Lyapunov stability theory. By extensive simulations, the results verify that our proposed method outperforms many competitive AQM schemes in terms of stability, response and robustness under various network scenarios. The proposed method is able to maintain stable queue size, small jitter, low packet loss and improves the trade-off between queuing delay and link utilization.