  In this paper, we equip the conventional discrete-time queueing network with
a Markovian input process, that, in addition to the usual short-term
stochastics, governs the mid- to long-term behavior of the links between the
network nodes. This is reminiscent of so-called Jump-Markov systems in control
theory and allows the network topology to change over time. We argue that the
common back-pressure control policy is inadequate to control such network
dynamics and propose a novel control policy inspired by the paradigms of
model-predictive control. Specifically, by defining a suitable but arbitrary
prediction horizon, our policy takes into account the future network states and
possible control actions. This stands in clear contrast to most other policies
which are myopic, i.e. only consider the next state. We show numerically that
such an approach can significantly improve the control performance and
introduce several variants, thereby trading off performance versus
computational complexity. In addition, we prove so-called throughput optimality
of our policy which guarantees stability for all network flows that can be
maintained by the network. Interestingly, in contrast to general stability
proofs in model-predictive control, our proof does not require the assumption
of a terminal set (i.e. for the prediction horizon to be large enough).
Finally, we provide several illustrating examples, one of which being a network
of synchronized queues. This one in particular constitutes an interesting
system class, in which our policy exerts superiority over general back-pressure
policies, that even lose their throughput optimality in those networks.
