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A nonlinear fluid flow model is used in this paper to analyze and control DiffServ networks. The controller design is based on the integrated dynamic congestion control strategy. In this paper different second order sliding mode controllers are proposed to solve the infinite switching problem. The same as standard sliding mode control, second order sliding mode technique is also robust to model uncertainties...
For the congestion problems in high-speed networks, a Q-learning model-independent flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the Q-learning, which is independent of mathematic model and prior-knowledge, has good performance. In this paper, the flow with higher...
For the congestion problems in high-speed networks, a multi-agent flow controller (MFC) based on Q-learning algorithm conjunction with the theory of Nash equilibrium is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks, especially for the multi-bottleneck case. The Nash Q-learning algorithm, which is...
It presents an economic model for a communication network with utility-maximizing elastic users who adapt to congestion by adjusting their flows. Users are heterogeneous with respect to both the utility they attach to different levels of flow and their sensitivity to delay or other measures of congestion. It shows that allowing heterogeneity with respect to delay sensitivity introduces a fundamental...
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