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.

翻译：新兴的可重构数据中心允许以需求感知的方式动态调整网络拓扑。这些数据中心依赖光交换机，通过重构提供机架间的直接连接，形式为边不相交匹配。尽管先进的光交换机原则上支持微秒级重构，但需求感知的拓扑优化仍构成性能瓶颈。本文提出一种动态算法方法，通过支持对流量需求变化的快速响应，提升可重构数据中心网络的性能。该方法利用流量模式的时间局部性，以增量方式更新互连匹配，而非从头重新计算。具体而言，我们提出六种（批量）动态算法，并与静态算法进行比较。基于176条合成轨迹和39条真实轨迹的广泛实证评估表明：动态算法在保持匹配权值的同时，能显著提升运行效率并减少配置变更次数，尤其在具有高时间局部性的网络中效果更为显著。