The state-of-the-art topologies of datacenter networks are fixed, based on electrical switching technology, and by now, we understand their throughput and cost well. For the past years, researchers have been developing novel optical switching technologies that enable the emergence of reconfigurable datacenter networks (RDCNs) that support dynamic psychical topologies. The art of network design of dynamic topologies, i.e., 'Topology Engineering,' is still in its infancy. Different designs offer distinct advantages, such as faster switch reconfiguration times or demand-aware topologies, and to date, it is yet unclear what design maximizes the throughput. This paper aims to improve our analytical understanding and formally studies the throughput of reconfigurable networks by presenting a general and unifying model for dynamic networks and their topology and traffic engineering. We use our model to study demand-oblivious and demand-aware systems and prove new upper bounds for the throughput of a system as a function of its topology and traffic schedules. Next, we offer a novel system design that combines both demand-oblivious and demand-aware schedules, and we prove its throughput supremacy under a large family of demand matrices. We evaluate our design numerically for sparse and dense traffic and show that our approach can outperform other designs by up to 25% using common network parameters.

翻译：最先进的数据中心网络拓扑是固定的，基于电交换技术，目前我们对其吞吐量和成本已有充分理解。近年来，研究人员一直在开发新型光交换技术，推动支持动态物理拓扑的可重构数据中心网络（RDCN）的出现。动态拓扑的网络设计艺术，即“拓扑工程”，仍处于起步阶段。不同设计提供了独特优势，如更快的交换机重构时间或需求感知拓扑，但至今尚不清楚何种设计能最大化吞吐量。本文旨在提升我们的分析性理解，通过提出一个通用且统一的动态网络及其拓扑与流量工程模型，正式研究可重构网络的吞吐量。我们使用该模型研究需求无关与需求感知系统，并根据拓扑和流量调度方案证明系统吞吐量的新上界。接着，我们提出一种结合需求无关与需求感知调度的新型系统设计，并证明其在大量需求矩阵下的吞吐量优势。我们针对稀疏和密集流量进行数值评估，结果表明，在常见网络参数下，我们的方法相比其他设计性能提升高达25%。