Memory-Rate Tradeoff for Decentralized Caching under Nonuniform File Popularity
Bibliographic record
Abstract
We study the memory-rate tradeoff for decentralized caching under nonuniform file popularity. We formulate the cache placement optimization problem for a recently proposed decentralized modified coded caching scheme (D-MCCS) to minimize the average rate. To solve this non-convex optimization problem, we develop two algorithms: a successive Geometric Programming (GP) approximation algorithm, which guarantees convergence to a stationary point but has a high computational complexity, and a low-complexity approach based on a two-file-group-based placement strategy. We further propose a lower bound on the average rate for decentralized caching under nonuniform file popularity. The lower bound is given as a nonconvex optimization problem, for which we propose a similar successive GP approximation algorithm to compute a stationary point. We show that the optimized MCCS attains the lower bound for the special case of no more than two active users requesting files, or for the general case but satisfying a special condition. Thus, the optimized MCCS characterizes the exact memory-rate tradeoff for decentralized caching in these cases. In general, our numerical result shows that the optimized D-MCCS performs close to the lower bound.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".