Nonlinear dynamical control systems
Nonlinear dynamical control systems
Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
TCP and explicit congestion notification
ACM SIGCOMM Computer Communication Review
Notions of Observability for Uncertain Linear Systems with Structured Uncertainty
SIAM Journal on Control and Optimization
End-to-end congestion control schemes: utility functions, random losses and ECN marks
IEEE/ACM Transactions on Networking (TON)
Scalable TCP: improving performance in highspeed wide area networks
ACM SIGCOMM Computer Communication Review
Linear stability of TCP/RED and a scalable control
Computer Networks: The International Journal of Computer and Telecommunications Networking
TCP-Illinois: a loss and delay-based congestion control algorithm for high-speed networks
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Experimental evaluation of TCP protocols for high-speed networks
IEEE/ACM Transactions on Networking (TON)
SIAM Journal on Control and Optimization
Automatica (Journal of IFAC)
Feedback Systems: An Introduction for Scientists and Engineers
Feedback Systems: An Introduction for Scientists and Engineers
Exponential stability of filters and smoothers for hidden Markovmodels
IEEE Transactions on Signal Processing
Paper: On stochastic observability and controllability
Automatica (Journal of IFAC)
TCP Vegas: end to end congestion avoidance on a global Internet
IEEE Journal on Selected Areas in Communications
Hi-index | 22.14 |
A hidden Markov model for the traffic congestion control problem in transmission control protocol (TCP) networks is developed, and the question of observability of this system is posed. Of specific interest are the dependence of observability on the congestion control law and the interaction between observability ideas and the effectiveness of feedback control. Analysis proceeds with a survey of observability concepts and an extension of some available definitions for linear and nonlinear stochastic systems. The key idea is to link the improvement of state estimator performance to the conditioning on the output data sequence. The observability development proceeds from linear deterministic systems to linear Gaussian systems, nonlinear systems, etc., with backwards compatibility to deterministic ideas. The principal concepts relate to the entropy decrease of scalar functions of the state, which in the linear case are describable in terms of covariance matrices. A feature of nonlinear systems is that the estimator properties may affect the closed-loop control performance. Results are derived linking stochastic reconstructibility to strict improvement of the optimal closed-loop control performance over open-loop control for the hidden Markov model. The entropy provides a means to quantify and thus order simulation results for a simplified TCP network. Motivated by the link between feedback control and reconstructibility, the entropy formulation is also explored as a means to discriminate between different control strategies for improving estimator performance. This approach has connections to dual-adaptive control ideas, where the control has the simultaneous and opposing goals of regulating the system and of exciting the system to prevent estimator divergence.