Energy and Latency Efficient Caching in Mobile Edge Networks: Survey, Solutions, and Challenges
Bibliographic record
Abstract
Future wireless networks provide research challenges with many fold increase of smart devices and the exponential growth in mobile data traffic. The advent of highly computational and real-time applications cause huge expansion in traffic volume. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of caches at the edge of the networks. Storing most popular files at the edge of mobile edge networks (MENs) in user terminals (UTs) and small base stations (SBSs) caches is a promising approach to the challenges that face data-rich wireless networks. Caching at the mobile UT allows to obtain requested contents directly from its nearby UTs caches through the device-to- device (D2D) communication. In this survey article, solutions for mobile edge computing and caching challenges in terms of energy and latency are presented. Caching in MENs and comparisons between different caching techniques in MENs are presented. An illustration of the research in cache development for wireless networks that apply intelligent and learning techniques (ILTs) in a specific domain in their design is presented. We summarize the challenges that face the design of caching system in MENs. Finally, some future research directions are discussed for the development of cache placement and cache access and delivery in MENs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".