Efficient fault diagnosis using incremental alarm correlation and active investigation for internet and overlay networks
Bibliographic record
Abstract
Fault localization is the core element in fault management. Symptom-fault map is commonly used to describe the symptom-fault causality in fault reasoning. For Internet service networks, a well-designed monitoring system can effectively correlate the observable symptoms (i.e., alarms) with the critical network faults (e.g., link failure). However, the lost and spurious symptoms can significantly degrade the performance and accuracy of a passive fault localization system. For overlay networks, due to limited underlying network accessibility, as well as the overlay scalability and dynamics, it is impractical to build a static overlay symptom-fault map. In this paper, we firstly propose a novel active integrated fault reasoning (AIR) framework to incrementally incorporate active investigation actions into the passive fault reasoning process based on an extended symptom-fault-action (SFA) model. Secondly, we propose an overlay network profile (ONP) to facilitate the dynamic creation of an overlay symptom-fault-action (called O-SFA) model, such that the AIR framework can be applied seamlessly to overlay networks (called O-AIR). As a result, the corresponding fault reasoning and action selection algorithms are elaborated. Extensive simulations and Internet experiments show that AIR and O-AIR can significantly improve both accuracy and performance in the fault reasoning for Internet and overlay service networks, especially when the ratio of the lost and spurious symptoms is high.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".