System State Discovery Via Information Content Clustering of System Logs
Bibliographic record
Abstract
Self-awareness is an important attribute for any system to have before it is capable of self-management. A system needs to have a continuous stream of real-time data to analyze to allow it be aware of its internal state. To this end, previous approaches have utilized system performance metrics and system log data to characterize system internal state. In using system logs to characterize system internal state, the computation of strongly correlated message types is necessary. In this work, we show that strongly correlated message types can be easily discovered without much computation. Our work explores a natural behaviour of system logs where system log data partitioned using source and time information contain correlated message types. We demonstrate how the groups of partitions, which contain correlated message types, can be found by clustering the partitions based on their entropy-based information content. We evaluate our method using cluster cohesion, cluster separation and cluster conceptual purity as metrics. The results show that our proposed method not only produces well-formed clusters but also clusters that can be mapped to different alert states with a high degree of confidence.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".