Equipment allocation in video-on-demand network deployments
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
Video-on-Demand (VoD) services are very user-friendly, but also complex and resource demanding. Deployments involve careful design of many mechanisms where content attributes and usage models should be taken into account. We define, and propose a methodology to solve, the VoD Equipment Allocation Problem of determining the number and type of streaming servers with directly attached storage (VoD servers) to install at each potential location in a metropolitan area network topology such that deployment costs are minimized. We develop a cost model for VoD deployments based on streaming, storage and transport costs and train a parametric function that maps the amount of available storage to a worst-case hit ratio. We observe the impact of having to determine the amount of storage and streaming cojointly, and determine the minimum demand required to deploy replicas as well as the average hit ratio at each location. We observe that common video-on-demand server configurations lead to the installation of excessive storage, because a relatively high hit-ratio can be achieved with small amounts of storage so streaming requirements dominate.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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