  State-of-the-art topologies for datacenters (DC) and high-performance
computing (HPC) networks are demand-oblivious and static. Therefore, such
network topologies are optimized for the worst-case traffic scenarios and can't
take advantage of changing demand patterns when such exist. However, recent
optical switching technologies enable the concept of dynamically reconfiguring
circuit-switched topologies in real-time. This capability opens the door for
the design of self-adjusting networks: networks with demand-aware and dynamic
topologies in which links between nodes can be established and re-adjusted
online and respond to evolving traffic patterns.
  This paper studies a recently proposed model for optical leaf-spine
reconfigurable networks. We present a novel algorithm, GreedyEgoTrees, that
dynamically changes the network topology. The algorithm greedily builds ego
trees for nodes in the network, where nodes cooperate to help each other,
taking into account the global needs of the network. We show that
GreedyEgoTrees has nice theoretical properties, outperforms other possible
algorithms (like static expander and greedy dynamic matching) and can
significantly improve the average path length for real DC and HPC traces.
