Machine learning approaches for predicting link failures in production networks
Why this work is in the frame
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Bibliographic record
Abstract
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 for a total improvement of 24 percentage points. • Optical metrics, in addition to IP metrics, are important when building predictive link failure models. • Capturing time-based relations improves the predictive performance. • Similarly, aggregating data from neighboring links via GNNs is beneficial. • Combining spatial and temporal dimensions, we can develop predictive link failure models with high performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it