Towards Adaptive Resource Allocation for Database Workloads.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".