Minimizing the Update Complexity of Facebook HDFS-RAID Locally Repairable Code
Why this work is in the frame
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Bibliographic record
Abstract
Erasure codes are recently used in real-world distributed storage systems (DSSs) such as Google File System,Microsoft Azure Storage, and Facebook HDFS-RAID for data reliability. When designing erasure codes for DSSs, special attention is given to the associated costs of data handling operations such as repair or update. For example, locally repairable codes (LRC) are designed and used in DSSs to allow for low-cost repair of failed blocks. Update complexity (defined as the number of blocks that need to be updated when an information block is changed) is yet another design parameter. This parameter can be seen as a measure of the computation, I/O and networking costs associated with updating an information block in a DSS. Since information is frequently updated by many applications, lowering update complexity can result in lower power consumptions in DSSs. In this work, we study the update complexity of LRCs. Based on our study, we propose an improvement over the LRC used by Facebook HDFS-RAID. Keeping the same code parameters including length, storage overhead, minimum distance and cost of repair (locality), we improve the update complexity by more than 16%. Moreover, we show that with these parameters achieving a lower update complexity is impossible.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.003 |
| 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