Self-adaptive congestion control for multi-class intermittent connections in a communication network
Carl Graham, Philippe Robert

TL;DR
This paper develops a Markovian and mean-field model for multi-class intermittent connections in communication networks, proposing a self-adaptive congestion control mechanism that adjusts transmission rates based on network feedback.
Contribution
It introduces a novel Markovian framework and mean-field analysis for multi-class intermittent connections with a self-adaptive congestion control strategy.
Findings
Mean-field limit reduces model complexity for large networks.
Stationary distribution characterized by a finite-dimensional fixed-point equation.
Self-adaptive control effectively manages congestion and delays.
Abstract
A Markovian model of the evolution of intermittent connections of various classes in a communication network is established and investigated. Any connection evolves in a way which depends only on its class and the state of the network, in particular as to the route it uses among a subset of the network nodes. It can be either active (ON) when it is transmitting data along its route, or idle (OFF). The congestion of a given node is defined as a functional of the transmission rates of all ON connections going through it, and causes losses and delays to these connections. In order to control this, the ON connections self-adaptively vary their transmission rate in TCP-like fashion. The connections interact through this feedback loop. A Markovian model is provided by the states (OFF, or ON with some transmission rate) of the connections. The number of connections in each class being…
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Taxonomy
TopicsNetwork Traffic and Congestion Control · Complex Network Analysis Techniques · Advanced Queuing Theory Analysis
