Adaptive Learning of Metric Correlations for Temperature-Aware Database Provisioning
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Bibliographic record
Abstract
This paper introduces a transparent self-configuring architecture for automatic scaling with temperature awareness in the database tier of a dynamic content Web server. We use a unified approach to achieving the joint objectives of performance, efficient resource usage and avoiding temperature hot-spots in a replicated database cluster. The key novelty in our approach is a lightweight on-line learning method for fast adaptations to bottleneck situations. Our approach is based on deriving a lightweight performance model of the replicated database cluster on the fly. The system trains its own model based on perceived correlations between various system and application metrics and the query latency for the application. The model adjusts itself dynamically to changes in the application workload mix. We use our performance model for query latency pre diction and determining the number of database replicas necessary to meet the incoming load. We adapt by adding the necessary replicas, pro-actively in anticipation of a bottleneck situation and we remove them automatically in underload. Finally, the system adjusts its query scheduling algorithm dynamically in order to avoid temperature hot- spots within the replicated database cluster. We investigate our transparent database provisioning mechanism in the database tier using the TPC-W industry- standard e-commerce benchmark. We demonstrate that our technique provides quality of service in terms of both performance and avoiding hot-spot machines under different load scenarios. We further show that our method is robust to dynamic changes in the workload mix of the application.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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