A Fault Detection Mechanism for Database Management Systems on Mobile Edge Computing
Bibliographic record
Abstract
The ever-increasing demand for reliable data storage solutions at mobile edge computing makes it a necessity to develop timely fault detection mechanisms. In this paper, we address the research challenge of detecting failed data queries in an application and infrastructure agnostic way. In this context, the mobile edge infrastructure includes a Database Management System (DBMS) that runs on server nodes and client nodes that retrieve, store, update and delete data in the DBMS. Specifically, we propose a rule-based algorithm with thresholds that takes as input utilization metrics and detects the faults. It does so without using sensitive DBMS credentials. To verify the applicability and the generality of the proposed algorithm, we made an experimental evaluation with six different query generation functions and testbed configurations. The comparison with other popular machine learning methods used for anomaly detection in monitoring systems, showed that the proposed algorithm significantly surpasses the other methods in terms of Precision and F1-score. During the operation of the mobile edge computing we gathered the utilization metrics, the queries success and fail status and created six datasets that we make them publicly available.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".