Mobile Cloud Storage Over 5G: A Mechanism Design Approach
Bibliographic record
Abstract
In order to meet the increasing demand for the data storage, 5G wireless networks embodying mobile edge computing (MEC) features arise as a compelling solution. In this paper, the dense heterogeneous network (HetNet) and the MEC infrastructure are exploited to propose a mobile cloud storage framework that minimizes the data transmission delay. The proposed framework is composed of two parts: A data management with error correction (DMEC) scheme, and a radio resource management (RRM) scheme. The DMEC scheme, derived from the redundant array of inexpensive disks (RAID) technology, is implemented in the user equipment (TIE) side, and it intelligently exploits the overlapping coverage of HetNet to minimize the transmission delay. On the other hand, the RRM scheme, based on mechanism design, presents the physical resource block allocation problem as a graph coloring problem and performs the radio resource allocation in multiuser scenario to maximize the network performance. The RRM scheme also comprises a pricing algorithm, which calculates the price a TIE needs to pay for the resources. The proposed RRM scheme exhibits several desirable characteristics such as incentive compatibility, efficiency, and truthfulness, all derived from the Vickrey-Clarke-Groves mechanism. Simulation results are presented, showing that the proposed framework when compared to baseline techniques, minimizes the transmission delay by 102 %, which places our proposal as effective and efficient solution for the mobile cloud storage problem.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".