Towards automating the configuration of a distributed storage system
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
Versatile storage systems aim to maximize storage resource utilization by supporting the ability to `morph' the storage system to best match the application's demands. To this end, versatile storage systems significantly extend the deployment- or run-time configurability of the storage system. This flexibility, however, introduces a new problem: a much larger, and potentially dynamic, configuration space makes manually configuring the storage system an undesirable if not unfeasible task. This paper presents our initial progress towards answering the question: “How can we configure a distributed storage system (i.e., enable/disable its various optimizations and configure their parameters) with minimal human intervention?” We discuss why manually configuring the storage system is undesirable; present the success criteria for an automated configuration solution; propose a generic architecture that supports automated configuration; and, finally, instantiate this architecture into a first prototype, which controls the configuration of similarity detection optimizations in the MosaStore distributed storage system. Our evaluation results demonstrate that the prototype can provide performance close to the optimal configuration at the cost of minimal overhead.
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 | 0.000 | 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 | 0.001 | 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