2010/07/01 by Modjtaba Rouhani, Mohammad Rasoul Tanhatalab, Ali Shokohi-Rostami · 1 citation
Computer Science · Engineering · #Active queue management #Advanced Optical Network Technologies #Algorithm #Artificial intelligence #Artificial neural network #CUBIC TCP #Computer network #Computer science #Control (management) #Control engineering #Control theory (sociology) #Controller (irrigation) #Engineering #Network Traffic and Congestion Control #Network congestion #Network packet #Nonlinear system #PID controller #Queue #Random early detection #Software-Defined Networks and 5G #TCP global synchronization
paper · doi:10.1109/cicsyn.2010.21
openalex publication_date 2010/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Active Queue Management (AQM) has been widely used for congestion avoidance in TCP networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level as RED, PI controller, PID Controller, Adaptive prediction controller (APC) and neural network using the Back-Propagation (BP) most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. In this paper, we design a nonlinear neural network controller using the non-linear model of TCP network. Genetic algorithms are used to train the nonlinear neural controller. We evaluate the performances of the proposed neural network AQM approach via simulation experiments. The proposed approach yields superior performance with faster transient response, larger throughput, and higher link utilization, as compared to other schemes.