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In this paper, we equip the conventional discrete-time queueing network witha Markovian input process, that, in addition to the usual short-termstochastics, governs the mid- to long-term behavior of the links between thenetwork nodes. This is reminiscent of so-called Jump-Markov systems in controltheory and allows the network topology to change over time. We argue that thecommon back-pressure control policy is inadequate to control such networkdynamics and propose a novel control policy inspired by the paradigms ofmodel-predictive control. Specifically, by defining a suitable but arbitraryprediction horizon, our policy takes into account the future network states andpossible control actions. This stands in clear contrast to most other policieswhich are myopic, i.e. only consider the next state. We show numerically thatsuch an approach can significantly improve the control performance andintroduce several variants, thereby trading off performance versuscomputational complexity. In addition, we prove so-called throughput optimalityof our policy which guarantees stability for all network flows that can bemaintained by the network. Interestingly, in contrast to general stabilityproofs in model-predictive control, our proof does not require the assumptionof a terminal set (i.e. for the prediction horizon to be large enough).Finally, we provide several illustrating examples, one of which being a networkof synchronized queues. This one in particular constitutes an interestingsystem class, in which our policy exerts superiority over general back-pressurepolicies, that even lose their throughput optimality in those networks.

Model-Predictive Control for Discrete-Time Queueing Networks with Varying Topology