Tapping the Knowledge of Dynamic Traffic Demands for Optimal CDN Design
Bibliographic record
Abstract
The content delivery network (CDN) intensively uses cache to push the content close to end users. Over both traditional Internet architecture and emerging cloud-based framework, cache allocation has been the core problem that any CDN operator needs to address. As the first step for cache deployment, CDN operators need to discover or estimate the distribution of user requests in different geographic areas. This step results in a statistical spatial model for the user requests, which is used as the key input to solve the optimal cache deployment problem. More often than not, the temporal information in user requests is omitted to simplify the CDN design. In this paper, we disclose that the spatial request model alone may not lead to truly optimal cache deployment and revisit the problem by taking the dynamic traffic demands into consideration. Specifically, we model the time-varying traffic demands and formulate the distributed cache deployment optimization problem with an integer linear program (ILP). To solve the problem efficiently, we transform the ILP problem into a scalable form and propose a greedy diagram to tackle it. Via experiments over the North American ISPs points of presence (PoPs) network, our new solution outperforms traditional CDN design method and saves the overall delivery cost by 16% to 20%. We also study the impact of various traffic demand patterns to the CDN design cost, via experiments with both real-world traffic demand patterns and extensive synthetic trace data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".