Nonhomogeneous Place-Dependent Markov Chains, Unsynchronised AIMD, and Network Utility Maximization

In this paper we derive a convergence result fo rthe non-homogeneous Markov chain that arises in the study of networks employing the additive-increase 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 inter-agent communication, and no common clock.

By: Fabian Wirth , Sonja Stuedli , Jia Yuan Yu, Martin Corless , Robert Shorten

Published in: RC25476 in 2014


This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.


Questions about this service can be mailed to .