Flexible Caching Algorithms for Video Content Distribution Networks
Why this work is in the frame
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Bibliographic record
Abstract
Global video content distribution networks (CDNs) serve a significant fraction of the entire Internet traffic. Effective caching at the edge is vital for the feasibility of these CDNs, which can otherwise incur substantial costs and overloads in the Internet. We analyze the challenges and requirements for content caching on the servers of these CDNs which cannot be addressed by standard solutions. We design multiple algorithms for this problem: a LRU-based baseline to address the requirements; a flexible ingress-efficient algorithm; an offline cache aware of future requests (greedy) to estimate the maximum efficiency we can expect from any online algorithm; an optimal offline cache (for limited scales); and an adaptive ingress control algorithm for reducing the server's peak upstream traffic. We use anonymized actual data from a global video CDN to evaluate the algorithms and draw conclusions on their suitability for different settings.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it