Abstract
In recent years, active queue management (AQM) has gained more and more attention as an important part of network congestion control. Although there are many AQM algorithms, these algorithms show weaknesses to detect and control congestion due to the complexity and dynamics of the networks. Hence, this paper proposes a new AQM algorithm based on model predictive control (MPC) theory which has been widely applied in nonlinear and time-delay systems. In order to adjust the parameters of the MPC-based AQM algorithm adaptively according to network scenario variations, the adaptive mechanism is introduced into the new algorithm, named PHAQM, by using the Hebb learning rules from the neural network control theory. The simulation results show that the algorithm is effective in avoiding network congestion. Compared to the traditional AQM schemes, such as PI, REM, and GPC algorithm, the PHAQM has a faster convergence rate and smaller queue length fluctuations and outperforms especially under dynamically changing network situations.
Highlights
The congestion control of the transmission control protocol (TCP) network is an important tool to improve the quality of service (QoS), which can prevent network collapse, avoid lockout behavior and effectively reduce the probability of control-loop synchronization [1]
This paper is aimed to solve the above problem by applying the model predictive control (MPC) theory and employing the adaptive scheme
An adaptive active queue management (AQM) algorithm, named PHAQM, is proposed based on the model by using MPC theory, and the adaptive scheme is designed with Hebb learning rules
Summary
The congestion control of the transmission control protocol (TCP) network is an important tool to improve the quality of service (QoS), which can prevent network collapse, avoid lockout behavior and effectively reduce the probability of control-loop synchronization [1]. The fluid-flow model for the congestion control process in TCP networks was established by using stochastic theory [8], which helps the researches design and understand the behaviors of internet systems better. When the TCP/AQM system is analyzed by control-theory as a feedback system, the probability of packet mark/drop in the router would be the control signal and the queue length would be the controlled variable. As the network congestion control system is a typical nonlinear and time-delay system, the MPC-based AQM algorithms were developed to determine the optimal control signal during each sampling time by predicting the future system dynamics. In order to adjust the parameters of MPC-based AQM schemes adaptively, this paper investigates an adaptive Hebb-learning rules for TCP/AQM systems with MPC control and proposes a new algorithm called PHAQM.
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