Orchestra: Extensible Block-Level Support for Resource and Data Sharing in Networked Storage Systems
Bibliographic record
Abstract
High-performance storage systems are evolving towards decentralized commodity clusters that can scale in capacity, processing power, and network throughput. Building such systems requires: (a)Sharing physical resources among applications; (b)Sharing data among applications; (c) Allowing customized views of data for applications. Current solutions satisfy typically the first two requirements through a distributed file-system, resulting in monolithic, hard-to-manage storage systems. In this paper, we present Orchestra, a novel storage system that addresses all three above requirements below the file-system by extending the block layer. To provide customized views, Orchestra allows applications to create semantically-rich virtual block devices by combining simpler ones. To achieve efficient resource and data sharing it supports block-level allocation and byte-range locking as in-band mechanisms. We implement Orchestra under Linux and use it to build a shared cluster file-system. We evaluate it on a 16-node cluster, finding that the flexibility offered by Orchestra introduces little overhead beyond mandatory communication and disk access costs.
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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 | 0.004 |
| 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 | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".