RIGEL：基于分布式交换机内GNN的有状态网络内推理的实时光异常诊断系统
RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs
浏览论文内容
中文总结 AI 辅助
本研究提出RIGEL系统，通过Tofino交换机的分布式GNN实现有状态网络内推理，结合自动编码器与GraphSAGE的模型经软硬件适配后，在测试平台上实现了高精度实时光异常诊断，性能优于现有方法。
中文摘要 AI 辅助
数据密集型应用的快速发展使光网络管理变得复杂，实时光异常诊断成为必备功能。然而，现有方法多基于集中式数据分析，难以避免数据平面与控制平面间消息交换带来的延迟和开销。本研究提出并原型实现RIGEL，据我们所知，它是首个通过Tofino交换机上的协作图神经网络（GNN）实现有状态分布式网络内推理的实时光异常诊断系统。该系统设计为全网络内架构，提出软硬件协同设计方案对高维光谱数据进行预处理，以适配硬件化的网络内推理。我们先开发了将自动编码器与基于GraphSAGE的GNN相结合的有效模型，再提出将该模型适配到Tofino交换机的通用方法。在真实的光分组网络测试平台上验证了RIGEL的有效性，结果显示它能高度准确地及时检测和定位光异常，且优于现有最先进方法。
英文摘要
The recent booming of data-intensive applications has complicated optical network management, making real-time optical anomaly diagnosis a must-have feature. However, existing approaches are mostly based on centralized data analytics and thus can hardly avoid the latency and overhead due to message exchanges between data and control planes. In this work, we propose and prototype RIGEL, which, to the best of our knowledge, is the first real-time optical anomaly diagnosis system that realizes stateful distributed in-network inference through collaborative graph neural networks (GNNs) on Tofino switches. The system is designed to be fully in-network, and a software-hardware co-design is proposed to preprocess high-dimensional spectral data for being suitable for hardware-based in-network inference. Next, we first develop an effective model to combine an autoencoder with a GraphSAGE-based GNN, and then propose a generalizable method to adapt the model to Tofino switch. The effectiveness of RIGEL is showcased in a realistic packet-over-optical network testbed, verifying that it achieves highly accurate diagnosis to detect and locate optical anomalies timely and highlighting its benefits over the state-of-the-art methods.
