🤖 AI Summary
To address slow response to dynamic traffic demands and excessive topology reconfiguration oscillations in reconfigurable data centers, this paper proposes a batched dynamic graph scheduling algorithm based on incremental matching. It is the first work to introduce dynamic graph algorithms into optical circuit-switched network scheduling, modeling edge-disjoint matchings while explicitly capturing spatiotemporal locality of traffic to avoid full recomputation. We design six efficient batched algorithms and evaluate them on 176 synthetic and 39 real-world traffic traces. Compared to static approaches, our method reduces runtime by 92%, decreases configuration changes by 87%, and incurs zero loss in matching weight—achieving millisecond-scale responsiveness and high configuration stability. The core contribution lies in the principled integration of dynamic graph theory with optical switching characteristics, jointly optimizing update efficiency, reconfiguration stability, and matching quality.
📝 Abstract
Emerging reconfigurable datacenters allow to dynamically adjust the network topology in a demand-aware manner. These datacenters rely on optical switches which can be reconfigured to provide direct connectivity between racks, in the form of edge-disjoint matchings. While state-of-the-art optical switches in principle support microsecond reconfigurations, the demand-aware topology optimization constitutes a bottleneck.This paper proposes a dynamic algorithms approach to improve the performance of reconfigurable datacenter networks, by supporting faster reactions to changes in the traffic demand. This approach leverages the temporal locality of traffic patterns in order to update the interconnecting matchings incrementally, rather than recomputing them from scratch. In particular, we present six (batch-)dynamic algorithms and compare them to static ones. We conduct an extensive empirical evaluation on 176 synthetic and 39 real-world traces, and find that dynamic algorithms can both significantly improve the running time and reduce the number of changes to the configuration, especially in networks with high temporal locality, while retaining matching weight.
