Optical Circuit Switching (OCS) technology is increasingly being adopted in data centers due to its advantages of low power consumption and low technology refresh costs. Unlike electrical packet switches, OCS provides programmable bandwidth for directly connected devices by configuring the mapping relationships of internal ports. Thus, how to calculate these internal port mapping relationships, i.e., Topology Engineering (ToE), is one of the key designs of OCS-based clusters. Current deployments usually design ToE algorithms by solving Integer Linear Programming (ILP) models, with the aim of minimizing modifications to links occupied by running tasks as much as possible. However, ILP-based ToE algorithms may incur excessive runtime overhead in large-scale clusters. Some existing ToE algorithms convert the ILP model into a Minimum-Cost Flow model through greedy construction, yet such greedy strategies may increase the number of affected links during the OCS reconfiguration process. To solve the aforementioned problems, we propose a novel bidirectional modeling approach, along with a corresponding FastReChain algorithm in this paper. We verify the superiority of this algorithm through simulation experiments based on real-trace data.

翻译：光路交换（OCS）技术因其低功耗和低技术刷新成本的优势，在数据中心中的应用日益广泛。与电分组交换机不同，OCS通过配置内部端口的映射关系，为直连设备提供可编程带宽。因此，如何计算这些内部端口映射关系，即拓扑工程（ToE），是基于OCS集群的关键设计之一。当前部署通常通过求解整数线性规划（ILP）模型来设计ToE算法，旨在尽可能减少对运行任务所占链路的修改。然而，基于ILP的ToE算法在大规模集群中可能产生过高的运行时开销。现有的一些ToE算法通过贪心构造将ILP模型转化为最小费用流模型，但此类贪心策略可能会增加OCS重配置过程中受影响的链路数量。为解决上述问题，本文提出了一种新颖的双向建模方法，并给出了相应的FastReChain算法。我们基于真实轨迹数据的仿真实验验证了该算法的优越性。