Towards Adaptive Resource Allocation for Database Workloads.
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
Modern computer systems provide hardware resources that allow database systems to execute a large number of tasks in parallel. However, no software system is perfectly scalable, and allocating more resources does not necessarily result in better performance. For commensurate resource allocation and increased efficiency, it is desirable to dynamically allocate hardware resources according to workload demands and conduct hardware consolidation. Given the complexity of database systems and their workloads, it is challenging to design such an adaptive algorithm. This paper addresses this problem using a simple feedback mechanism. The contributions of this work are twofold. First, an application-agnostic performance metric based on hardware performance counters is proposed to measure system performance online. This fine-grained metric enables agile feedback even for long running analytical workloads. Second, an allocation algorithm is presented that is designed based on fuzzy control techniques. The controller does not need a system model or prior training. Evaluation results show that a good correlation exists between the system-level metric and application-specific performance metrics. Further, a database system with our controller can achieve performance comparable to that obtained with manual tuning.
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 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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