A cache-node selection mechanism for data replication and service composition within cloud-based systems
Bibliographic record
Abstract
Data replication services provide clients with faster application access time. Since cloud applications generate tremendous amounts of stored information, data distribution is a must. Such distribution requires data to be replicated on third-party cloud storage sites for faster access. Current replica services exhibit an increase in data acquisition by clients and thus requires a partial file caching strategy. This paper proposes a mechanism for selecting mobile nodes to partially cache highly requested files. The proposed scheme relies on candidate mobile cache nodes that can efficiently provide file access services to other nearby nodes. The candidate cache nodes are chosen according to their devices' hardware and software resources, flow time, residual power, mobility characteristics and transmission capabilities. Additionally, services are composed on-demand using a fuzzy-induced service-specific overlay composition technique. Simulation results demonstrate the significant gains achieved by the proposed scheme in terms of access time, enhanced service availability, overlay composition delay reduction and high file hit ratios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".