A course of H∞0Econtrol theory
A course of H∞0Econtrol theory
Congestion avoidance and control
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
Control systems engineering
Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
TCP Vegas: new techniques for congestion detection and avoidance
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Dynamics of random early detection
SIGCOMM '97 Proceedings of the ACM SIGCOMM '97 conference on Applications, technologies, architectures, and protocols for computer communication
A control theoretic approach to active queue management
Computer Networks: The International Journal of Computer and Telecommunications Networking
MSWIM '01 Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
Analysis and design of an adaptive virtual queue (AVQ) algorithm for active queue management
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
TCP-Peach: a new congestion control scheme for satellite IP networks
IEEE/ACM Transactions on Networking (TON)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Improving TCP Congestion Control over Internets with Heterogeneous Transmission Media
ICNP '99 Proceedings of the Seventh Annual International Conference on Network Protocols
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
The Mathematics of Internet Congestion Control (Systems and Control: Foundations and Applications)
The Mathematics of Internet Congestion Control (Systems and Control: Foundations and Applications)
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Learning Bayesian Networks
Digital Signal Processing
A comparison of active queue management algorithms using the OPNET Modeler
IEEE Communications Magazine
IEEE Network: The Magazine of Global Internetworking
On the use of a full information feedback to stabilize RED
Journal of Network and Computer Applications
Computers and Electrical Engineering
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Active Queue Management (AQM) is a proven strategy to efficiently maintain queues and ensure high utilization of Transmission Control Protocol (TCP) network resources. The fundamental mechanism is to manage incoming packet rates at a router to prevent incipient network congestion. In this paper, we present an efficient neural network AQM system as a queue controller. The recurrent neural network has a Multi-layer Perceptron-Infinite Impulse Response (MLP-IIR) structure. Three distinct neural AQMs are trained under different network scenarios involving traffic levels. Selecting one of three neural AQMs is based on posterior probability history of traffic level. In addition, we investigate stochastic modeling of the network dynamics by a Dynamic Bayesian Network (DBN). This model allows implementation of a predictive AQM system in which queue dynamics are predicted and used for error prediction via online DBN estimation. Our AQM method is evaluated through simulation experiments both using an Ordinary Differential Equation (ODE) network model and using OPNET^(C). The simulation results demonstrate that our adaptive neural AQM outperforms Random Early Detection (RED) and Proportional-Integral-Derivative (PID) based AQM.