ThriftStore: Finessing Reliability Trade-Offs in Replicated Storage Systems
Bibliographic record
Abstract
This paper explores the feasibility of a storage architecture that offers the reliability and access performance characteristics of a high-end system, yet is cost-efficient. We propose ThriftStore, a storage architecture that integrates two types of components: volatile, aggregated storage and dedicated, yet low-bandwidth durable storage. On the one hand, the durable storage forms a back end that enables the system to restore the data the volatile nodes may lose. On the other hand, the volatile nodes provide a high-throughput front-end. Although integrating these components has the potential to offer a unique combination of high throughput and durability at a low cost, a number of concerns need to be addressed to architect and correctly provision the system. To this end, we develop analytical and simulation-based tools to evaluate the impact of system characteristics (e.g., bandwidth limitations on the durable and the volatile nodes) and design choices (e.g., the replica placement scheme) on data availability and the associated system costs (e.g., maintenance traffic). Moreover, to demonstrate the high-throughput properties of the proposed architecture, we prototype a GridFTP server based on ThriftStore. Our evaluation demonstrates an impressive, up to 800 Mbps transfer throughput for the new GridFTP service.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".