Optimal Uncoded Placement and File Grouping Structure for Improved Coded Caching under Nonuniform Popularity
Bibliographic record
Abstract
This paper considers the caching design for coded caching under nonuniform file popularity. We investigate the optimal cache placement for the modified coded caching scheme (MCCS) recently proposed with an improved delivery strategy for rate reduction over the original coded caching scheme (CCS). We use the optimization framework for the cache placement problem to minimize the average delivery rate. Exploring several properties of the optimization problem and analyzing its structure, we obtain the file grouping structure under the optimal cache placement. We show that, regardless of file popularity, there are at most three file groups under the optimal cache placement. We further characterize the complete structure of the optimal cache placement and obtain the closed-form placement solution in these three possible file group cases. Following these, we develop a simple algorithm to obtain the final optimal cache placement solution, which only requires to compute a set of candidate solutions in closed-form. Simulation verifies the optimal solution produced by our algorithm. The optimal MCCS is shown to outperform existing schemes for both MCCS and CCS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".