Data allocation for multi-class distributed storage systems
Bibliographic record
Abstract
Distributed storage systems (DSSs) are vastly used for reliably storing large amounts of data generated by current and future wireless networks, e.g. social mobile networks or Internet of things. Depending on the features of the data source, various data files may require different levels of quality of service (QoS), e.g. in terms of the probability of successful recovery or data recovery delay. This means that data files can be divided into different classes in terms of their QoS requirements. To address the requirements of each class of data, efficient data (storage) allocation methods, meaning how data is spread over the storage nodes, should be devised. In this paper, we study the optimal data allocation for maximizing the weighted sum of the probability of successful recovery of the data of different classes. Finding such optimal allocations is intractable in general. Therefore, we focus on finding the optimal minimal spreading allocation (MSA) where the data of each class is spread minimally over the storage nodes. MSA possesses several benefits including minimum expected recovery delay and maximum average service rate. Simulation results show that our proposed MSA is indeed the optimal storage allocation in many cases.
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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) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".