Content Placement in a Cluster-Centric Mobile Edge Caching Network
Bibliographic record
Abstract
Mobile Edge Caching (MEC) enables low latency communication by offering caching capabilities close to ground users. Performance of MEC network, however, can significantly degrade because of the small storage of caching nodes and high diversity of multimedia content. To tackle this issue, existing research works focus on integrating coded/uncoded content placement with clustered MEC networks. Despite all the research that has been done in this area, there is still no framework that specifies how various coded content fragments should be dispersed among neighboring caching nodes to broaden the variety of content. To deal with this challenge, the paper proposes a coded/uncoded content placement where content will be spread in nearby caching nodes according to their popularity. The performance of the proposed content placement framework is evaluated in term of the users' access delay, and the cache-hit ratio. The efficiency of the proposed coded/uncoded content placement in comparison to the coded and uncoded counterparts is supported by simulation results.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".