Reducing problem space using Bayesian classification on semantic logs for enhanced application monitoring and management
Bibliographic record
Abstract
Monitoring managing large scale applications has always been a crucial and complex task on which enormous efforts and research have been carried out towards making the process efficient, effective and automated. However, the process is still complex, lacks efficiency and effectiveness because execution workflow representation and logging (outcome from real-time execution) is rendered in a syntactic and unstructured manner. The information is quite limited and requires additional manual interpretation till date for effectively handling the process. Hence, it makes the monitoring and management process slow, cumbersome and hard. We propose our solution by semantically (highly structured, formalized and expressive) modeling of execution workflow and logs, and then using adapted Bayesian Classification based inference technique to process formalized logs to help for enhancing the process of monitoring and management by reducing the problem space. Our hybrid approach of partially using semantics to formalize log and workflow data, and adapting classification technique combines the best of both. Semantics help in providing high-level of precision, structure and expressivity to execution workflow and logs. Such kind of formalized data can be used in an effective manner to effectively interpret and process highly structured information from the generated logs during the execution by classification technique to reduce problem space during the process of monitoring and management of applications. This paper first presents a review of related approaches, then methodology towards the hybrid solution, design of our proposed solution and implementation, followed by evaluation of our proposed solution on real-life application scenario.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".