A theory of cloud bandwidth pricing for video-on-demand providers
Why this work is in the frame
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Bibliographic record
Abstract
Current-generation cloud computing is offered with usage-based pricing, with no bandwidth capacity guarantees, which is however unappealing to bandwidth-intensive applications such as video-on-demand (VoD). We consider a new type of service where VoD providers, such as Netflix and Hulu, make reservations for bandwidth guarantees from the cloud at negotiable prices to support continuous media streaming. We argue that it is beneficial to multiplex such bandwidth reservations in the market using a profit-making broker while controlling the performance risks. We ask the question-in such a market, how much should each VoD provider pay for bandwidth reservation? We prove that the market has a unique Nash equilibrium where the bandwidth reservation price for a VoD provider critically depends on its demand correlation to the market. Real-world traces verify that our theory can significantly lower the market price for cloud bandwidth reservation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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