Systematic Fountain Codes for Massive Storage Using the Truncated Poisson Distribution
Bibliographic record
Abstract
Erasure codes for distributed storage systems (DSS) are required to offer systematic encoding, low repair locality, low encoding/decoding complexity, and low decoding/storage overhead. However, the information theoretical bounds have shown that all these metrics might need to be carefully traded-off with one another. In this paper, we consider systematic Fountain codes with belief propagation (BP) decoding for massive scale DSSs. Analyzing the role of some degrees in the BP decoding process, we propose using the truncated Poisson distribution (TPD) for encoded symbol degrees. Identifying encoded symbol redundancy as a factor that degrades decoding overhead, we derive the probability of redundancy during the BP decoding process and use this as a tool for determining the Poisson parameter. Our proposed solution nicely addresses the first three DSS metrics with a slight toll on storage/decoding overhead. Through simulations, we show that the decoding overhead performance of the proposed scheme exhibits significant improvement over some existing Fountain code degree distributions in the literature. For instance, at a decoding overhead of 60%, we achieve a data loss probability closely approaching 10-4, while other Fountain codes compared are about a factor of 102higher.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".