Order and balance in continuously-fault-tolerant distributions of objects
Bibliographic record
Abstract
A distributed object systems is said to be K-tolerant if every object is available after the simultaneous failure of up to K nodes. The problem is that a K-tolerant system, after failures, is no longer K -- tolerant; that is, subsequent failures may compromise the availability of the objects. A continuously K-tolerant system is one which starting from a K-tolerant configuration, after the failure of up to K nodes, reconfigures itself so to remain K-tolerant. The existing protocols for maintaining continuous K-tolerance do so without regard to the resulting structure of the available data. For example, if the distributed set of objects was sorted, this ordering would be most likely lost after restructuring. Analogously, a balanced distribution of the objects among the nodes might also be not achieved in the new distribution after reorganization. In this paper, we present a mechanism for maintaining continuous K-tolerance while keeping the load balanced and the objects sorted. The proposed solution uses minimum amount of replication and has a cost comparable to the one of the known unstructured solutions.
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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 | 0.000 | 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 | 0.000 | 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 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".