Repairable Fountain Coded Storage Systems for Multi-Tier Mobile Edge Caching Networks
Bibliographic record
Abstract
Mobile edge caching (MEC) is an emerging paradigm where cloud services are extended to base stations (BSs) and devices to support a variety of applications and services in the Internet of Things (IoT). It expects to exploit storage resources deployed at different BSs and idle resources of end devices to efficiently cache and deliver contents. However, frequent mobility of devices with cached data can lead to data loss. Therefore, it becomes an important yet very challenging issue to cache the massive data with high data fault-tolerance and low cost. In this paper, we consider a multi-tier MEC network, where cloud-BS, edge-BSs and mobile devices collaboratively store and deliver contents to users. In addition, a framework based on repairable fountain codes (RFCs) with unequal repair locality (URL) for data caching, repairing and downloading, termed URL-RFC, is proposed to achieve efficient data caching in multi-tier heterogeneous MEC networks. Furthermore, a theoretical analysis and simulation results are provided to analyze and compare the energy costs of URL-RFC and existing solutions. It is demonstrated that the proposed URL-RFC scheme outperforms other redundant fault-tolerant schemes, which validates the potential value and feasibility of URL-RFC enabled storage in multi-tier MEC networks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".