Optimal anticipative congestion control of flows with time-varying input stream
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This paper is concerned with a new type of congestion control method that we call anticipative congestion control, which exploits probabilistic information available at a network node about congestion at other nodes. Motivated by the Internet flows behaving according to the Transmission Control Protocol, we consider a flow with time-varying input stream. We design a Markov decision process model for flow admission control and characterize the Whittle index in a closed form. This index measures the efficiency of flow data transmission at a router. We prove that such an index policy is optimal and that it further implies optimality of threshold policies. We apply the results to obtain an expression of the index for a single-bottleneck flow under several types of fairness criteria.