A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper provides a comprehensive survey of content placement (CP) algorithms for cloud-based content delivery networks (CCDNs). CP algorithms are essential for content delivery for their major role in selecting content to be stored in the geographically distributed surrogate servers in the cloud to meet end-user demands with quality of service (QoS). Evidently, the key objectives of CP, i.e., cost and QoS, are competing. Cost is determined by the underlying cost model of the CCDN infrastructure while the delivered QoS is determined by where the content is placed in the CCDN. Therefore, we provide an overview of the content and the CCDN infrastructure. The overview of the content includes content characteristics and the influence of Online Social Networking on CP. The overview of the CCDN infrastructure includes elasticity and cost model, which affect CP. Our goal is to provide a holistic perspective of the aspects that impact CP algorithms and their efficiency. From the influential factors, we derive a set of design criteria for CP algorithms in CCDNs. We discuss the state-of-the-art CP algorithms for CCDNs and evaluate them against the well-motivated design criteria. We also delineate practical implications and uncover future research challenges.
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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 | 0.002 | 0.001 |
| Open science | 0.003 | 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