Separating durability and availability in self-managed storage
Bibliographic record
Abstract
Building reliable data storage from unreliable components presents many challenges and is of particular interest for peer-to-peer storage systems. Recent work has examined the trade-offs associated with ensuring data availability in such systems. Reliability, however, is more than just availability. In fact, the durability of data is typically of more paramount concern. While users are likely to tolerate occasional disconnection from their data (they will likely have no choice in the matter), they demand a much stronger guarantee that their data is never permanently lost due to failure. To deliver strong durability guarantees efficiently, however, requires decoupling durability from availability. This paper describes the design of a data redundancy scheme that guarantees durability independently from availability. We provide a formula for determining the rate of redundancy repair when durability is the only concern and show that availability requires much more frequent repair. We simulate modified versions of the Total Recall block store that incorporate our design. Our results show that we can deliver durability more cheaply than availability, reducing network overhead by between 50% and 97%.
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.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".