Storage workload estimation for database management systems
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 storage systems are sophisticated. Simple directattached storage devices are giving way to storage systems that are shared, flexible, virtualized and network-attached. Today, storage systems have their own administrators, who use specialized tools and expertise to configure and manage storage resources. Although the separation of storage management and database management has many advantages, it also introduces problems. Database physical design and storage configuration are closely related tasks, and the separation makes it more difficult to achieve a good end-toend design. In this paper, we attempt to close this gap by addressing the problem of predicting the storage 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 storage workload that will result. Such a characterization can be used by a storage administrator to guide storage configuration. The ultimate goal of this work is to enable effective end-to-end design and configuration spanning both the database and storage system tiers. We present an empirical assessment of the cost of workload prediction as well as the accuracy of the result.
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 | 0.001 |
| Open science | 0.001 | 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