Leveraging Spatiotemporal Relations for Predicting Potential Link Failures
Bibliographic record
Abstract
Being able to predict link failures in advance would be of great benefit to network operators. We use Machine Learning (ML) techniques to extract temporal and spatial relations from real network data and use them to predict link failures. We use Interior Gateway Protocol (IGP) configuration changes as a guide to achieve this. We predict link failures in the next five days based on data collected from the previous five days. We propose a modified Variational Auto Encoder (VAE) model to compress the higher dimensional dataset into a latent space that captures time-based relations in the data. We demonstrate that five days is the smallest look-back window of time required to get satisfactory prediction results. Using feature importance plots, we learned that the VAE model was able to capture intricate time-based dependencies in the error counter features to achieve good performance. In addition, using a Graph Convolutional Network (GCN), we were able to aggregate data from neighboring links to improve the model's performance. Neighbors up to two hops away carried relevant information in IGP metric settings and in traffic metric counter features. The relevance of the correlation of the features in time and space is confirmed using standard feature importance wrapper methods. Finally, by combining the VAE and GCN components, we were able to extract spatial and temporal features in conjunction, leading to further improvements. These ML approaches significantly improve existing manual methods of tracking metrics in time and space currently followed by the operator.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".