Dynamic QoS-Aware Resource Assignment in Cloud-Based Content-Delivery Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of resource provisioning and content placement in cloud-based content delivery networks is studied, and a two-stage resource provisioning and cloud assignment is proposed based on dynamic large and small-scale fluctuations of user demand rates as well as considering a constrained minimum lease time for resources. In the first stage, we perform resource provisioning while costs of resources, quality of service (QoS) violation, and cloud-based root server redirection are included in the optimization, and constraints of QoS and limited resource of cloud sites are taken into account. In the second stage, cloud site assignment is conducted where for fixed allocated resources, QoS violation and cloud-based root server redirection costs are minimized or reduced using three different proposed schemes. We further show that reassignment of cloud sites during rising demand rates within a lease time period improves revenue, while reassignment is not very effective for falling demand rates.
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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 | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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