  The performance of large-scale computing systems often critically depends on
high-performance communication networks. Dynamically reconfigurable topologies,
e.g., based on optical circuit switches, are emerging as an innovative new
technology to deal with the explosive growth of datacenter traffic.
Specifically, \emph{periodic} reconfigurable datacenter networks (RDCNs) such
as RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have
been shown to provide high throughput, by emulating a \emph{complete graph}
through fast periodic circuit switch scheduling.
  However, to achieve such a high throughput, existing reconfigurable network
designs pay a high price: in terms of potentially high delays, but also, as we
show as a first contribution in this paper, in terms of the high buffer
requirements. In particular, we show that under buffer constraints, emulating
the high-throughput complete graph is infeasible at scale, and we uncover a
spectrum of unvisited and attractive alternative RDCNs, which emulate regular
graphs, but with lower node degree than the complete graph.
  We present Mars, a periodic reconfigurable topology which emulates a
$d$-regular graph with near-optimal throughput. In particular, we
systematically analyze how the degree~$d$ can be optimized for throughput given
the available buffer and delay tolerance of the datacenter. We further show
empirically that Mars achieves higher throughput compared to existing systems
when buffer sizes are bounded.
