Pushing Server Bandwidth Consumption to the Limit: Modeling and Analysis of Peer-Assisted VoD
Bibliographic record
Abstract
Recent years have witnessed video-on-demand (VoD) as an efficient means for providing reliable streaming service for Internet users. It is known that peer-assisted VoD systems, such as NetFlix and PPlive, generally incur a lower deployment cost in terms of server bandwidth consumption. However, some fundamental issues still need to be further clarified, particularly for VoD service providers. In particular, how far can we push peer-assisted VoD forward, and at the scale of VoD systems, the maximum reduction of server bandwidth consumption that can be achieved with peer-assisted approaches. In this paper, we provide extensive model analysis to understand the minimum server bandwidth consumption for peer-assisted VoD systems. We first propose a basic model that can optimally schedule user demands at given snapshots. Our model analysis reveals the optimal performance bound and shows that the existing peer-assisted protocols are still far from being optimal. How to push the server bandwidth consumption to the limit remains a big challenge in VoD system design. To approach the optimal bandwidth consumption in real deployment, we further extend our model to a realistic case to capture the peer dynamic across continuous time-slots. The simulation result indicates that the optimal load scheduling problem is still achievable through a dynamic programming algorithm. Its design principle further motivates a fast priority-based algorithm that achieves near-optimal performance. These proposed algorithms can significantly reduce the bandwidth consumption of dedicated VoD servers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".