Bibliographic record
Abstract
Buffer pools are blocks of memory used in database systems to retain frequently referenced pages. Configuring the buffer pools is a difficult and manual task that involves determining the amount of memory to devote to the buffer pools, the number of buffer pools to use, their sizes, and the database objects assigned to each buffer pool. A good buffer configuration improves query response times and system throughput by reducing the number of disk accesses. Determining a good buffer configuration requires knowledge of the database workload. \nEmpirical studies have shown that optimizing the initial buffer configuration (determined at database design time) can improve system throughput. A good initial configuration can also provide a faster convergence towards a favorable dynamic buffer allocation. Previous studies have not considered automating the buffer pool configuration process. \nThis thesis presents two techniques that facilitate the initial buffer configuration task. First, we develop an analytic model of the GCLOCK buffer replacement policy that can be used to evaluate the effectiveness of a particular buffer configuration for a given workload. Second, to obtain the necessary model parameters, we propose a workload characterization scheme that extracts workload parameters, describing the query reference patterns, from the query access plans. In addition, we extend an existing multifractal model and present a multifractal skew model to represent query access skew. \nOur buffer model has been validated against measurements of the buffer manager of a commercial database system. The model has also been compared to an alternative GCLOCK buffer model. Our results show that our proposed model closely predicts the actual physical read rates and recognizes favourable buffer configurations. This work provides a foundation for the development of an automated buffer configuration tool.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".