Matrix analysis
IEEE/ACM Transactions on Networking (TON)
Modeling TCP Reno performance: a simple model and its empirical validation
IEEE/ACM Transactions on Networking (TON)
End-to-end congestion control for the internet: delays and stability
IEEE/ACM Transactions on Networking (TON)
Stochastic Equilibria of AIMD Communication Networks
SIAM Journal on Matrix Analysis and Applications
Congestion control in high-speed communication networks using the Smith principle
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief paper: Modelling TCP congestion control dynamics in drop-tail environments
Automatica (Journal of IFAC)
On queue provisioning, network efficiency and the transmission control protocol
IEEE/ACM Transactions on Networking (TON)
Experimental evaluation of TCP protocols for high-speed networks
IEEE/ACM Transactions on Networking (TON)
TCP-Illinois: A loss- and delay-based congestion control algorithm for high-speed networks
Performance Evaluation
Automatica (Journal of IFAC)
Brief paper: On linear co-positive Lyapunov functions for sets of linear positive systems
Automatica (Journal of IFAC)
On the design of load factor based congestion control protocols for next-generation networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Buffer sizing for 802.11-based networks
IEEE/ACM Transactions on Networking (TON)
Brief paper: Positivity-preserving H∞ model reduction for positive systems
Automatica (Journal of IFAC)
Analysis of DCTCP: stability, convergence, and fairness
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Analysis of DCTCP: stability, convergence, and fairness
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
Reachability of a Class of Discrete-Time Positive Switched Systems
SIAM Journal on Control and Optimization
Automatica (Journal of IFAC)
Congestion control with multipacket feedback
IEEE/ACM Transactions on Networking (TON)
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We study communication networks that employ drop-tail queueing and Additive-Increase Multiplicative-Decrease (AIMD) congestion control algorithms. It is shown that the theory of nonnegative matrices may be employed to model such networks. In particular, important network properties, such as: 1) fairness; 2) rate of convergence; and 3) throughput, can be characterized by certain nonnegative matrices. We demonstrate that these results can be used to develop tools for analyzing the behavior of AIMD communication networks. The accuracy of the models is demonstrated by several NS studies.