Parallelism-Aware Locally Repairable Code for Distributed Storage Systems
Bibliographic record
Abstract
Distributed storage systems store a substantial amount of data in a large number of servers built with commodity hardware. In order to protect data against server failures, erasure coding has been deployed in many distributed storage systems because of its low storage overhead. In particular, since disk I/O is, in many cases, a bottleneck in the distributed storage system, locally repairable codes, have been proposed that incur low volumes of disk I/O when reconstructing missing data after server failures. However, since original data can only be read from specific servers, existing designs of locally repairable codes suffer from limited data parallelism. Besides, if the performance of servers is heterogeneous, slow servers may become the bottleneck when accessing data in parallel. In this paper, we propose Galloper codes, a novel family of locally repairable codes, that achieve low disk I/O during reconstruction and meanwhile extend data parallelism from specific servers to all servers. Moreover, the amount of original data in each server can be arbitrarily determined based on the performance of corresponding servers. We have implemented a prototype of Galloper codes on Apache Hadoop, and our experimental results have shown that Galloper codes can reduce the completion time of MapReduce jobs by up to 42.9%, with a comparable performance as existing locally repairable codes, in terms of disk I/O overhead, as well as encoding and reconstruction overhead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".