Congestion avoidance and control
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Equation-based congestion control for unicast applications
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
A duality model of TCP and queue management algorithms
IEEE/ACM Transactions on Networking (TON)
End-to-end congestion control schemes: utility functions, random losses and ECN marks
IEEE/ACM Transactions on Networking (TON)
Exponential-RED: a stabilizing AQM scheme for low- and high-speed TCP protocols
IEEE/ACM Transactions on Networking (TON)
A globally stable adaptive congestion control scheme for internet-style networks with delay
IEEE/ACM Transactions on Networking (TON)
Rate adaptive multimedia streams: optimization and admission control
IEEE/ACM Transactions on Networking (TON)
Deterministic packet marking for time-varying congestion price estimation
IEEE/ACM Transactions on Networking (TON)
FAST TCP: motivation, architecture, algorithms, performance
IEEE/ACM Transactions on Networking (TON)
QoS support in Wireless/Wired networks using the TCP-Friendly AIMD protocol
IEEE Transactions on Wireless Communications
Stable adaptive neuro-control design via Lyapunov function derivative estimation
Automatica (Journal of IFAC)
TCP Vegas: end to end congestion avoidance on a global Internet
IEEE Journal on Selected Areas in Communications
Fairness guarantees in a neural network adaptive congestion control framework
IEEE Transactions on Neural Networks
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A novel neuro-adaptive congestion controller is presented, capable of regulating the per packet round trip time (RTT) around a piecewise constant desired RTT, thus achieving almost piecewise constant delay. The controller is implemented at the source and is proven robust against modeling imperfections, exogenous disturbances (UDP traffic) and delays (propagation, queueing). The notion of communication channels is introduced for throughput improvement. The analysis is nonlinear and the tools used are approximation-based control and linear-in-the-weights neural networks. The proposed controller is guaranteed to be saturated. Moreover, modifications are also provided to achieve rate reduction whenever congestion is detected. Simulation studies illustrate the performance of the proposed control scheme and compare it with other well-established congestion control mechanisms.