An update model for network coding in cloud storage systems
Bibliographic record
Abstract
The fast emerging cloud services have received a tremendous amount of attention in both industry and academia. Storage services such as Dropbox and iCloud enabled us to share files among multiple users or devices. Providing the benefits of network coding in distributed systems such as Peer-to-Peer file sharing and multimedia streaming, researchers have also been trying to apply network coding in storage systems. Existing works have been focusing on mechanisms for preserving the level of redundancy when one or more nodes fail or leave the system. However, file updates, the most frequent operations performed on files, pose challenges in maintaining coded information in the system up to date. In other words, any change in the file will impact all coded blocks in the system, so all traces of the file must be completely replaced. This becomes costly since recomputing coded blocks is very CPU intensive and replacing all the coded blocks consumes an excessive amount of bandwidth. To the best of our knowledge, there has been no work addressing this update problem. To this end, we propose a Differential Update Model (DUM) that will update coded blocks by delivering only the changes in the file. We also present our objective view of the model through a complete analysis on computational complexity and bandwidth saving and simulated experiments.
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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 | 0.005 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".