Cache-Aided Delivery Network in a Shared Cache Framework with Correlated Sources
Bibliographic record
Abstract
The increasing number of internet users and IoT devices, in addition to the widespread use of social media and streaming services, has put significant strain on current delivery networks. Content caching has emerged as a viable solution to combat the high delivery data rate and improve the quality of services (QoS) for the end-users in such networks. In this paper, we propose a cache-aided delivery network with correlated sources in a shared cache framework where a different group of users connects to each cache. We address the caching strategy and examine the trade-off between the delivery rate and the memory size from an information-theoretic perspective. We propose a correlation-aware clustering scheme to extract the most efficient side information for the entire library during the placement phase considering the similarity among sources and the maximum distortion constraint in the system. We also formulate the expected delivery rate by joint consideration of the rate-distortion function and caching strategy, where the limit for the maximum allowable distortion in the system is determined based on the Lagrange multipliers technique and reverse water-filling algorithm. Moreover, we introduce the optimum library partitioning formulated to minimize the worst-case delivery rate in the system. We also study the proposed solution in a special case where only one shared cache is available in the network. Our extensive simulations validate the proposed scheme providing a considerable boost in network efficiency compared to legacy caching schemes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".