Greedy heuristic for replica server placement in Cloud based Content Delivery Networks
Why this work is in the frame
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Bibliographic record
Abstract
Recently, Cloud based Content Delivery Network (CCDN) has emerged as efficient content delivery architecture to provide content delivery services with improved Quality of Service (QoS), scalability and resource efficiency. Replica server placement is a key design issue in CCDNs and involves deciding the placement of replica servers on geographically dispersed cloud sites that minimizes the operational cost and satisfies QoS of the end-users. Since replica server placement problem is NP-hard, it is necessary to design an efficient heuristic for CCDNs. In this paper, we propose an efficient greedy heuristic for the replica server placement problem. The heuristic consists of two main procedures: placement and refinement. The placement procedure obtains an initial placement of replica servers on cloud sites with low operational cost. The refinement procedure removes the redundant cloud sites to reduce the operational cost further. The simulation results demonstrate that the proposed greedy heuristic outperforms the existing greedy heuristics in terms of computation time and the operational cost.
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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 | 0.000 | 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 | 0.000 |
| 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 it