Repair Delay Analysis of Mobile Storage Systems Using Erasure Codes and Relay Cooperation
Bibliographic record
Abstract
Mobile storage systems (MSSs) which store popular content in mobile devices can reduce the heavy traffic burden on the base station (BS). To deal with data loss caused by the mobility of devices, erasure codes are widely used in practice to improve system reliability. The duration to repair all lost data is defined therepair delay. However, the repair delay via device-to-device communications is usually large due to intermittent contacts among devices within a given communication range, which causes files to be irreparable and permanently lost. This paper focuses on analyzing the repair delay of the MSSs through D2D communications. We adopt a coded repair process running periodically and derive analytical expressions of the average repair delay by taking into account factors, i.e., limited communication range, device mobility, and coded repair scheme. Moreover, considering that devices need multipath contacts to repair lost data for large files, we propose a relay cooperation repair scheme, in which some mobile devices can act as relay nodes to transmit data cooperatively. Furthermore, we design a heuristic algorithm to optimize the number of relay devices and the amount of data allocated to each path, with the objective of minimizing the average repair delay. We find that maximum distance separable codes can yield smaller average repair delays for small files, and the average repair delay can be reduced by selecting faster moving devices to participate. With the file size increasing, the relay cooperation repair scheme can significantly improve repair efficiency in MSSs using the proposed heuristic algorithm.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".