Exploiting Pipelined Encoding Process to Boost Erasure-Coded Data Archival
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses an issue of erasure-coded data archival, where (k + r; k) erasure codes are employed to archive rarely accessed replicas. The traditional synchronous encodingprocess neither leverages the existence of replicas, nor handles encoding operations in a decentralized manner. To overcome these drawbacks, we exploit pipelined encoding processes to boost the data archival performance on storage clusters. First, we propose two data layouts called [D + P] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cd</sub> and [3X] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cd</sub> by applying a chained-declustering mechanism to both Mirrored RAID-5 and triplication redundancy groups. Second, in light of the [D + P] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cd</sub> and [3X] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cd</sub> layouts, we design two archiving schemes named DP and 3X, which exhibit the following three salient features: (i) exploiting data locality-two or three local blocks are read by each involved node for encoding; (ii) decentralized computation load-encoding operations are distributed among k nodes; and (iii) parallel archival processing-two or three encoding pipelines are simultaneously deployed to generate parity blocks. We implement both the DPand 3X schemes and three existing solutions (i.e., SynE, DE, and RapidRAID) in a real-world storage cluster. Experimental results show that our archival schemes outperform the other three solutions in terms of archiving time by a factor of at least 3.41 in a nine-node storage cluster. The experiments strongly indicate that the performance bottleneck of SynE lies in its block-receiving stage; it is disk I/O rather than network traffic that dominates archiving time for both the DE and RapidRAID schemes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it