Speeding Up Multi-CDN Content Delivery via Traffic Demand Reshaping
Bibliographic record
Abstract
Nowadays, more and more content providers (CPs) use multiple content delivery networks (CDNs) to deliver their content (a.k.a. content multihoming). Since the decisions on which CDN to use are made by the CP or by a CDN broker based on their local view of network conditions, content multihoming still has much room to improve for a better content delivery performance. In addition, content multihoming may negatively impact CDN vendors since in the price competition they are enforced to lower content delivery price to attract CPs to use their CDNs. To build a better CDN ecosystem, multi-CDN federation has been proposed to interconnect standalone CDNs. The real-world implementation of CDN interconnection (CDNI), however, poses significant technical obstacles not easy to solve in the short term. In order to improve the content delivery performance under current multi-CDN strategies, in this paper, we propose a feasible and efficient solution to multi-CDN, termed as CDN semi-federation, which can better schedule and utilize the resources from multiple CDNs without requiring full CDNI. The benefit of our solution comes from an effective optimization algorithm which reshapes the patterns of traffic from multiple CPs delivered over multipe CDN Points of Presence (PoPs). Experiments across North American and European ISP PoP networks demonstrate that, compared with current multi-CDN solutions, CDN semi-federation can reduce the content delivery latency by around 20% during peak traffic hours.
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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 | 0.001 |
| 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 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".