Towards building a conflict‐free mobile distributed file system
Bibliographic record
Abstract
Abstract The rising demand for mobile computing has created a need for an improved file system that supports mobile clients. Current file systems with support for mobility provide availability through file replicas that are cached at the client side. However, mobile clients may experience different obstacles with regards to the local cache, such as the limited network bandwidth, the intermittent connection, and serious conflicts when synchronizing back to the server. In this paper, we propose a novel mobile distributed file system design, which provides high availability and reliable storage for files and guarantees that file operations are executed regardless of concurrency and failure issues. The design is intended to fit mobile clients (e.g. PDAs and cell phones) that have limited storage space and cannot store all of the data they need, and yet require access to these data at all times. We adopt a server‐side caching in order to guarantee sufficient caching space to all mobile clients, and to ensure the availability of files in the case of clients' failures. We present our algorithm, describe its implementation, simulate its high availability functions, and report on its performance evaluation using a cluster of workstations. Our simulation results indicate clearly that our algorithm exhibits a significant degree of automation and conflict‐free mobile file system. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".