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A positive systems model of TCPlike congestion control: Asymptotic results
 IEEE/ACM Transactions on Networking
, 2004
"... In this paper we study communication networks that employ droptail queueing and AdditiveIncrease MultiplicativeDecrease (AIMD) congestion control algorithms. We show that the theory of nonnegative matrices may be employed to model such networks. In particular, we show that important network p ..."
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Cited by 64 (10 self)
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In this paper we study communication networks that employ droptail queueing and AdditiveIncrease MultiplicativeDecrease (AIMD) congestion control algorithms. We show that the theory of nonnegative matrices may be employed to model such networks. In particular, we show that important network properties such as: (i) fairness; (ii) rate of convergence; and (iii) throughput; can be characterised by certain nonnegative matrices that arise in the study of AIMD networks. We demonstrate that these results can be used to develop tools for analysing the behaviour of AIMD communication networks. The accuracy of the models is demonstrated by means of several NSstudies.
TCPillinois: A loss and delaybased congestion control algorithm for highspeed networks
"... Abstract — We introduce a new congestion control algorithm for high speed networks, called TCPIllinois. TCPIllinois uses packet loss information to determine whether the window size should be increased or decreased, and uses queueing delay information to determine the amount of increment or decrem ..."
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Cited by 42 (3 self)
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Abstract — We introduce a new congestion control algorithm for high speed networks, called TCPIllinois. TCPIllinois uses packet loss information to determine whether the window size should be increased or decreased, and uses queueing delay information to determine the amount of increment or decrement. TCPIllinois achieves high throughput, allocates the network resource fairly, and is incentive compatible with standard TCP. We also build a new stochastic matrix model, capturing standard TCP and TCPIllinois as special cases, and use this model to analyze their fairness properties for both synchronized and unsynchronized backoff behaviors. We finally perform simulations to demonstrate the performance of TCPIllinois.
Tools for the analysis and design of communication networks with markovian dynamics,” to appear
 in Proceedings of IEE, Control Theory and Applications
, 2005
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Fairness and convergence results for additiveincrease multiplicativedecrease multiplebottleneck networks
 in: Proc. of IEEE CDC
"... Abstract — We examine the behavior of the AdditiveIncrease MultiplicativeDecrease (AIMD) congestion control algorithm. We present a variant of a recently proposed matrix model that allows us to obtain previous results for competition via a single bottleneck link. We then extend these results to th ..."
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Cited by 1 (0 self)
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Abstract — We examine the behavior of the AdditiveIncrease MultiplicativeDecrease (AIMD) congestion control algorithm. We present a variant of a recently proposed matrix model that allows us to obtain previous results for competition via a single bottleneck link. We then extend these results to the case of multiple bottleneck links paying particular attention to some aspects of fairness and convergence properties for multiple bottleneck systems. We examine both the synchronous (deterministic) and asynchronous (stochastic) cases. A simple simulation example illustrates the results. I.
RouterBased Algorithms for Improving Internet Quality of Service
, 2007
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Supervisors: Prof. Robert Shorten
, 2015
"... I hereby declare that the material presented in this thesis, which I now submit for assessment on the programme of study leading to the award of Doctor of Philosophy from the Hamilton Institute is entirely my own works and has not been previously submitted for any other degree or qualification and h ..."
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I hereby declare that the material presented in this thesis, which I now submit for assessment on the programme of study leading to the award of Doctor of Philosophy from the Hamilton Institute is entirely my own works and has not been previously submitted for any other degree or qualification and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of my work.
Nonhomogeneous PlaceDependent Markov Chains, Unsynchronised AIMD, and Network Utility Maximization
, 2014
"... In this paper we derive a convergence result for the nonhomogeneous Markov chain that arises in the study of networks employing the additiveincrease multiplicative decrease (AIMD) algorithm. We then use this result to solve the network utility maximization (NUM) problem. Using AIMD, we show that t ..."
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In this paper we derive a convergence result for the nonhomogeneous Markov chain that arises in the study of networks employing the additiveincrease multiplicative decrease (AIMD) algorithm. We then use this result to solve the network utility maximization (NUM) problem. Using AIMD, we show that the NUM problem is solvable in a very simple manner using only intermittent feedback, no interagent communication, and no common clock.