Log filtering and interpretation for root cause analysis
Bibliographic record
Abstract
Problem diagnosis in large software systems is a challenging and complex task. The sheer complexity and size of the logged data make it often difficult for human operators and administrators to perform problem diagnosis and root cause analysis. A challenge in this area is to provide the necessary means, tools, and techniques for the operators to focus their attention to specific parts of the logged data reducing thus the complexity of the diagnostic process. In this paper, we propose a framework for filtering logs according to specific analysis goals and diagnostic hypotheses set by the user or by an automated process. More specifically, the proposed framework uses annotated goal trees to model the constraints and the conditions by which the functionality of a particular system is being delivered. Next, a transformation process maps such constraints and conditions to a collection of queries that can be either applied to a relational database that stores the logged data or use Latent Semantic Indexing to identify the most relevant log entries for the given query. The results of such queries provide a subset of the logged data that is compliant with the goal tree and can be used by a diagnostic SAT-solver based algorithm. Experimental results show that the filtering process can reduce the time and complexity of the diagnosis when applied to multi-tier heterogeneous service oriented systems.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".