发表机构
Technische Universität Braunschweig; University of Brasília（布伦瑞克工业大学; 巴西利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究城域和数据中心弹性光网络中分布式光纤传感设备的稀疏部署，提出贪心最短路径覆盖启发式算法，5%链路覆盖即可显著减少故障影响，25%覆盖接近全覆盖性能，且15ms和7.5ms预测窗口足以实现即时恢复。
AI 中文摘要
分布式光纤传感（DFOS）利用光纤背向散射信号来预测故障事件，并实现主动的即时（JIT）恢复。现代网络基础设施以光学组件和光纤作为高吞吐量和低延迟的关键使能者。在这种光基础设施中集成传感功能，增加了网络诊断、空间定位和传感等特性，并为实现下一代网络所期望的智能网络控制开辟了机会。在本文中，我们研究了全光网络中DFOS设备的数量和部署位置如何缓解故障事件。在我们的评估中，我们考虑了城域和数据中心弹性光网络。这项工作还评估了具有不同预测时间能力的传感设备，以评估它们在考虑可变数量的部署设备和不同部署启发式算法时的有效性。评估使用了关键性能指标，包括带宽阻塞率、受故障事件影响的电路数量以及服务停机时间。仿真结果表明，当DFOS设备仅覆盖5%的链路时，贪心最短路径覆盖（GSPC）启发式算法显著减少了受故障影响的电路数量，而25%的覆盖率显示出与全覆盖场景相当的性能增益，这为显著降低成本提供了机会。最后，对于5%的传感覆盖率场景，我们的结果表明，15毫秒和7.5毫秒的预测窗口分别足以在城域和数据中心网络中实现JIT恢复。
英文摘要
Distributed Fiber Optical Sensing (DFOS) leverages optical backscattering signals to predict failure events and enable proactive Just-In-Time (JIT) restoration. Modern network infrastructures have optical components and fibers as key enablers of high throughput and low latency. The integration of sensing features in this optical infrastructure adds features such as network diagnosis, spatial positioning and sensing, and opens up opportunities for the implementation of intelligent network control expected for the next-generation networks. In this paper, we study how the quantity and the placement of the DFOS devices in an all-optical network can mitigate failure events. In our evaluation, we consider metropolitan and data center elastic optical networks. This work also evaluates sensing devices with various prediction time capabilities, in order to assess their effectiveness when considering a variable number of deployed devices and different placement heuristics. The evaluation uses key performance metrics, including the bandwidth blocking rate, number of affected circuits by failure events, and the service downtime. Simulation results demonstrate that the Greedy Shortest Path Coverage (GSPC) heuristic substantially reduces the number of circuits affected by failure when DFOS devices cover only 5% of links, while 25% coverage shows a performance gain comparable to the full coverage scenario, which is an opportunity for significant cost reduction. Finally, for the 5% sensing coverage scenarios, our results illustrate that prediction windows of 15 ms and 7.5 ms are sufficient for JIT restoration in metropolitan and data center networks, respectively.
