Identifying symptoms of recurrent faults in log files of distributed information systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The manual process to identifying causes of failure in distributed information systems is difficult and time-consuming. The underlying reason is the large size and complexity of these systems, and the vast amount of monitoring data they generate. Despite its high cost, this manual process is necessary in order to avoid the detrimental consequences of system downtime. Several studies and operator practice suggest that a large fraction of the failures in these systems are caused by recurrent faults. Therefore, significant efficiency gains can be achieved by automating the identification of these faults. In this work we present methods, which draw from the areas of information retrieval as well as machine learning, to automate the task of infering symptoms pertinent to failures caused by specific faults. In particular, we present a method to infer message types from plain-text log messages, and we leverage these types to train classifiers and extract rules to identify symptoms of recurrent faults automatically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it