  The performance of distributed and data-centric applications often critically
depends on the interconnecting network. Emerging reconfigurable datacenter
networks (RDCNs) are a particularly innovative approach to improve datacenter
throughput. Relying on a dynamic optical topology which can be adjusted towards
the workload in a demand-aware manner, RDCNs allow to exploit temporal and
spatial locality in the communication pattern, and to provide topological
shortcuts for frequently communicating racks. The key challenge, however,
concerns how to realize demand-awareness in RDCNs in a scalable fashion.
  This paper presents and evaluates Chopin, a hybrid scheduler for
self-adjusting networks that provides demand-awareness at low overhead, by
combining centralized and distributed approaches. Chopin allocates optical
circuits to elephant flows, through its slower centralized scheduler, utilizing
global information. Chopin's distributed scheduler is orders of magnitude
faster and can swiftly react to changes in the traffic and adjust the optical
circuits accordingly, by using only local information and running at each rack
separately.
