A Data-Driven Approach for Adaptive Real-Time Log Parsing in Cloud Environments
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
In the era of rapidly expanding cloud computing centers and large-scale services, analyzing system logs has become crucial for monitoring the quality of service. With systems generating vast amounts of logs, manual analysis is no longer feasible, necessitating automatic and precise log analysis techniques. The process begins with log parsing, a critical first step towards automating the analysis by transforming unstructured logs into structured records. However, current log parsing techniques lack adaptability. First, they struggle with software or firmware updates, as previously learned templates fail to recognize new log types. Second, they perform poorly across different services, unable to accurately parse logs from newly introduced services, further hindering effective log parsing. To address these challenges, we propose an adaptive log parsing method for largescale cloud environments called AdapLog. AdapLog leverages an online data-driven approach that efficiently processes grouped log messages without manual parameter tuning. Evaluation results indicate that our log parsing method outperforms state-of-the-art techniques across most experiments with respect to parsing accuracy (up to 4.2 x higher) and time (up to 13.4 x less per log).
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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