Machine Learning-Based Monitoring Trail: On Fast and Accurate Optical Path Failure Localization in All-Optical DCNs

We propose Machine Learning-based Monitoring Trail (mlm-trail) to monitor optical path failures for large model training. Existing monitoring trail (m-trail) method can only localize optical link failures, whereas mlm-trail differs from it by combining with machine learning and providing bidirectional voltage constraint. mlm-trail can provide fast, accurate and integrated failure localization of both optical links and optical switches using only a small number of monitors. Firstly, we construct an input dataset based on the edge relationships of 10000 virtual network topologies (the mappings of optical links and optical switches in all-optical data center networks (DCNs)). Then, monitoring trail under bidirectional voltage constraint is formulated by integer liner program (ILP) to minimize the overall monitoring cost (including monitor and bandwidth costs), and thus construct an output dataset. Finally, we train the learning model based on above dataset using classical and proposed hybrid machine learning models to achieve fast generation of monitoring trails. Based on the constraints of full coverage of monitoring trail and uniqueness of alarms, the generated output results based on machine learning are modified to achieve unambiguous localization for each optical path. Numerical results show that mlm-trail outperforms m-trail in localization speed and scalability, and also outperforms machine learning algorithms in accuracy and cost.