Locality-aware fountain codes for massive distributed storage systems
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Bibliographic record
Abstract
Low repair locality of a distributed storage code has been shown to reduce strain on storage node input-output (I/O) resources during node repair operations after a failure. In this paper, we consider the use of Fountain codes for distributed storage systems and aim to understand the relationship between repair locality and code parameters for a systematic Fountain code. While the information-theoretic trade-off between repair locality and storage overhead has been understood and characterized, the challenge of choosing a locality value that satisfies multiple storage system design metrics is yet to be resolved. We approach this problem by deriving an expression for the probability distribution of repair locality in terms of the rateless code degree distribution coefficients and suggest that factoring this relationship into the code design process enables the design of rateless codes better adjusted to the needs of a massive distributed storage system.
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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 | 0.001 |
| 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 it