A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks
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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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".