  The state-of-the-art topologies of datacenter networks are fixed, based on
electrical switching technology, and by now, we understand their throughput and
cost well. For the past years, researchers have been developing novel optical
switching technologies that enable the emergence of reconfigurable datacenter
networks (RDCNs) that support dynamic psychical topologies. The art of network
design of dynamic topologies, i.e., 'Topology Engineering,' is still in its
infancy. Different designs offer distinct advantages, such as faster switch
reconfiguration times or demand-aware topologies, and to date, it is yet
unclear what design maximizes the throughput.
  This paper aims to improve our analytical understanding and formally studies
the throughput of reconfigurable networks by presenting a general and unifying
model for dynamic networks and their topology and traffic engineering. We use
our model to study demand-oblivious and demand-aware systems and prove new
upper bounds for the throughput of a system as a function of its topology and
traffic schedules.
  Next, we offer a novel system design that combines both demand-oblivious and
demand-aware schedules, and we prove its throughput supremacy under a large
family of demand matrices. We evaluate our design numerically for sparse and
dense traffic and show that our approach can outperform other designs by up to
25% using common network parameters.
