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by Santosh P. Abraham, Anurag Kumar
http://ece.iisc.ernet.in/~anurag/papers/anurag/ieeetac_submitted_00a.ps.gz
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Abstract:
A significant portion of the traffic in packet data networks is from store-and-forward sessions. Such traffic flows are elastic, i.e., they can adapt to the transfer rate that the network can provide them. The network reserves bandwidth for real-time sessions, such as interactive voice, and dynamically shares the remaining bandwidth among the elastic sessions, in order to achieve some bandwidth sharing objective, such as max-min fairness. A distributed algorithm is required for this purpose. Several distributed algorithms for max-min fair rate allocation are available in the literature [9],[15], [16],[6]. The underlying assumption in these algorithms has been the availability of a fixed available capacity on each link for an extended period of time. However, in practice the available capacity will have rapid fluctuations due to the intrinsic rate variations of the guaranteed bandwidth flows. In this paper we develop an approach based on the distributed stochastic approximation algorithm that computes the max-min fair rate allocation when the available capacity is a stochastic process. Each session can request a minimum rate guarantee, hence we work with a notion of max-min fairness with minimum rates. The stochastic approximation iterations converge to the stable point of a certain differential equation. A major part of this paper is a proof that this stable point is the desired vector of max-min fair rates. 1
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