Dynamic Learning-based Link Restoration in Traffic Engineering with Archie

Fiber cuts reduce network capacity and take a long time to fix in optical wide-area networks. It is important to select the best restoration plan that minimizes throughput loss by reconfiguring wavelengths on remaining healthy fibers for affected IP links. Recent work studies optimal restoration plan or ticket selection problem in traffic engineering (TE) in a one-shot setting of only one TE interval (5 minutes). Since fiber repair often takes hours, in this work, we extend to consider restoration ticket selection with traffic dynamics over multiple intervals. To balance restoration performance with reconfiguration overhead, we perform dynamic ticket selection every T time steps. We propose an end-to-end learning approach to solve this T-step ticket selection problem as a classification task, combining traffic trend extraction and ticket selection in the same learning model. It uses convolution LSTM network to extract temporal and spatial features from past demand matrices to determine the ticket most likely to perform well T steps down the road, without predicting future traffic or solving any TE optimization. Trace-driven simulation shows that our new TE system, Archie, reduces over 25% throughput loss and is over 3500x faster than conventional demand prediction approach, which requires solving TE many times.