Towards automating the configuration of a distributed storage system
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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".