Risk-Averse Caching Policies for YouTube Content in Femtocell Networks using Density Forecasting
Bibliographic record
Abstract
The paper presents risk-neutral and risk-averse caching policies that can be deployed in a femtocell network with limited storage capacity to reduce the time delay of servicing content requests. The caching policies use a forecasting algorithm to estimate the cumulative distribution function of content requests based on the content features. Given the cumulative distribution function, a mixed-integer linear program is used to compute where to cache content in the femtocell network. The caching policies account for the uncertainty associated with estimating the content requests using the coherent Conditional Value-at-Risk (CVaR) measure. For a large number of content, a risk-neutral caching policy is constructed that accounts for both the content features and routing protocol that only requires the evaluation of a unimodular linear program. Using data from YouTube (comprising 25,000 videos) and the NS-3 simulator, the caching policies reduce the delay of retrieving content in femtocell networks compared with industry standard caching policies. Specifically, a 6 percent reduction in delay is achieved by accounting for the uncertainty, and a 60 percent reduction in delay is achieved if both the uncertainty and femtocell routing protocol are accounted for compared to the risk-neutral caching policy that neglects the routing protocol.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".