Bibliographic record
Abstract
Mobile data traffic is increasing explosively in recent years due to tremendous growth in demands from mobile users for multimedia contents. However, current mobile networking technologies, including network architectures and data transmission techniques, cannot support the anticipated traffic load without degrading mobile user's quality of service (QoS) or quality of experience (QoE). Much ongoing research efforts are targeted at developing technologies for fifth generation (5G) mobile Internets and beyond to overcome these limitations. Content-centric edge caching has recently emerged as a promising technique to satisfy the demands of popular multimedia contents that are requested repeatedly by multiple mobile users over a period of time. This talk will motivate and explore the design principles and goals of content-centric edge caching. We shall present a generalized architectural framework as a basis of differentiating different edge caching designs. We shall present three trace-driven case studies involving a single tier cellular network, a multi-tier heterogeneous cellular network, and a two-tier caching scheme involving a cellular network and device-to-device communications, to illustrate the optimization of some design alternatives. We shall conclude the talk with a discussion of research opportunities and challenges in content-centric edge caching.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".