On generalized max-min rate allocation and distributed convergence algorithm for packet networks

We consider the fundamental problem of bandwidth allocation among flows in a packet-switched network. The classical max-min rate allocation has been widely regarded as a fair rate allocation policy. But, for a flow with a minimum rate requirement and a peak rate constraint, the classical max-min policy no longer suffices to determine rate allocation since it is not capable of supporting either the minimum rate or the peak rate constraint from a flow. We generalize the theory of the classical max-min rate allocation with the support of both the minimum rate and peak rate constraints for each flow. Additionally, to achieve generalized max-min rate allocation in a fully distributed packet network, we present a distributed algorithm that uses a feedback-based flow control mechanism. Our design not only offers a fresh perspective on flow marking technique, but also advances the state-of-the-art flow marking technique favored by other researchers. We provide proof that such a distributed algorithm, through asynchronous iterations, will always converge to the generalized max-min rate allocation under any network configuration and any set of link distances. We use simulation results to demonstrate the fast convergence property of the distributed algorithm.