Joint Content Distribution and Traffic Engineering of Adaptive Videos in Telco-CDNs
Bibliographic record
Abstract
Telco-CDNs refer to content distribution networks deployed and managed by Internet Service Providers (ISPs). They are getting popular among major ISPs because they offer new revenue streams and have the potential of providing better performance compared to traditional CDNs. Managing telco-CDNs is, however, a complex problem, because it requires jointly managing the network resources (links and switches) and the caching resources (processing and storage capacities), while supporting the adaptive nature and skewed popularity of multimedia content. To address this problem, we present a new algorithm called CAD (Cooperative Active Distribution), which strives to serve as much as possible of the requested multimedia objects within the ISP while carefully engineering the traffic paths through the network. This is achieved by enabling the cooperation among caches within the ISP not only to serve various representations of multimedia objects, but also to create them on demand using the available processing capacity of caches. We have implemented CAD and evaluated it on top of a network emulator that runs deployment code and processes real traffic. Using an actual ISP topology, our experimental results show that CAD achieves substantial performance improvements compared to the closest work in the literature, e.g., up to 64% reduction in the total inter-domain traffic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".