Active queue management algorithm based on data-driven predictive control

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• 作者：Ping Wang ; Daji Zhu ; Xiaohui Lu
• 关键词：Network congestion control ; Active queue management ; Data ; driven predictive control
• 刊名：Telecommunication Systems
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：64
• 期：1
• 页码：103-111
• 全文大小：
• 刊物类别：Business and Economics
• 刊物主题：IT in Business; Computer Communication Networks; Artificial Intelligence (incl. Robotics); Probability Theory and Stochastic Processes;
• 出版者：Springer US
• ISSN：1572-9451
• 卷排序：64

文摘

Model predictive control (MPC) is a popular strategy for active queue management (AQM) that is able to incorporate physical and user defined constraints. However, the current MPC methods rely on explicit fluid model of TCP behavior with input time delay. In this paper, we propose a novel AQM algorithm based on data-driven predictive control, called Data-AQM. For Internet system with large delay, complex change and bad disturbance, data-driven predictive controller can be obtained directly based on the input–output data alone and does not require any explicit model of the system. According to the input–output data, the future queue length in data buffer, which is the basis of optimizing drop probability, is predicted. Furthermore, considering system constraints, the control requirement is converted to the optimal control objective, then the drop probability is obtained by solving the optimal problem online. Finally, the performances of Data-AQM are evaluated through a series of simulations.