Interaction Analysis of Heterogeneous Monitoring Data for Autonomic Problem Determination
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
Autonomic systems require continuous self-monitoring to ensure correct operation. Available monitoring data exists in a variety of formats, including log files, performance counters, traces, and state and configuration parameters. Such heterogeneity, together with the extremely large volume of data that could be collected, makes analysis very complex. To allow for more-effective problem determination, there is a need for a comprehensive integration of management data. In addition, monitoring should be adaptive to the current perceived operation of the system. In this paper we present an architecture to meet the above goals. We leverage an open-source XML-based format for data integration and describe an approach to automatically adjust monitoring for diagnosis when anomalies are detected. We have implemented a partial prototype using an Eclipse-based open-source platform. We show the effectiveness of our prototype based on fault-injection experiments. We also study issues of disparity of data formats, information overload, scalability, and automated problem determination.
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 | 0.001 | 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