Bibliographic record
Abstract
Modern storage systems are complex. Simple direct-attached storage devices are giving way to storage systems that are flexible, network-attached, consolidated and virtualized. Today, storage systems have their own administrators, who use specialized tools and expertise to configure \nand manage storage resources. As a result, database administrators are no longer in direct control of the design and configuration of their database systems' underlying storage resources. \nThis introduces problems because database physical design and storage configuration are closely related tasks, and the separation \nmakes it more difficult to achieve a good end-to-end design. For instance, the performance of a database system depends strongly on the storage layout of database objects, such as tables and indexes, and the separation makes it hard to design a storage layout that is tuned to the I/O workload generated by the database system. In this thesis we address this problem and attempt to close the information gap between database and storage tiers by addressing the problem of predicting the storage (I/O) workload that will be generated by a database management system. Specifically, we show how to translate a database workload description, together with a database physical design, into a characterization of the I/O workload that will result. Such a characterization can directly be used by a storage configuration tool and thus enables effective end-to-end design and configuration \nspanning both the database and storage tiers. \nWe then introduce our storage layout optimization tool, which leverages such workload characterizations to generate an optimized layout for a given set of database objects. We formulate the layout problem as a non-linear programming (NLP) problem and \nuse the I/O characterization as input to an NLP solver. We have incorporated our I/O estimation technique into the PostgreSQL database management system and our layout optimization technique into a database layout advisor. We present an empirical assessment of the cost of both tools as well as the efficacy and accuracy of their results.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".