Machine learning approaches for predicting link failures in production networks

Resolving network failures after they occur through human investigation is a costly and time-consuming process. Predicting upcoming failures could mitigate this to a large extent. In this work, we collect data from a large intercontinental network and study the problem of flapping links, which are indicative of link failures. Such flapping links have their routing metric increased to divert traffic away; this is followed by corrective actions, and eventually their routing metric is lowered again to carry traffic. Using the collected data, primarily metrics reported from Internet Protocol (IP) and optical layers of the network, we develop ML models to predict upcoming link failures. Exploring a sequence of increasingly complex models, we study the relevance of optical metrics, the underlying temporal relations, and the topological relations in improving the predictive model performance. We discovered that optical features such as optical maximum and minimum power or unavailable and errored seconds increased the model's performance (measured in average precision) by about 9 percentage points while temporal and spatial features improved it further by 8 and 7 percentage points respectively fora total improvement of 24 percentage points.