Zebra: Demand-aware erasure coding for distributed storage systems
Bibliographic record
Abstract
Erasure coding has been increasingly replacing replication in distributed storage systems, thanks to its lower storage overhead with the same level of failure tolerance. However, with lower storage overhead, the reconstruction overhead of erasure codes can increase significantly as well. Under the ever-changing workload, in which the data access can be highly skewed, it is difficult to achieve a well trade-off between the storage overhead and the reconstruction overhead. In this paper, we propose Zebra, a framework that encodes data into multiple tiers by their demand. Given the overall storage overhead and the number of failures to tolerate, Zebra determines the parameters of erasure coding in each tier by solving a geometric programming problem. Based on the demand of data, Zebra can dynamically assign data into the corresponding tiers to minimize the overall reconstruction overhead, and achieve a flexible tradeoff between the storage overhead and the reconstruction overhead in multiple tiers, such that hot data can enjoy less overhead of reconstruction and cold data can be stored with lower storage overhead. When demand changes, Zebra can adjust itself accordingly with a marginal amount of network transfer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".