Reducing the Overhead of View-Upload Decoupling in Peer-to-Peer Video On-Demand Systems
Bibliographic record
Abstract
View-upload decoupling (VUD) has become a novel and effective strategy in balancing the supply and demand of bandwidth resources in peer-to-peer (P2P) live streaming systems. In this paper, we investigate the strategy of migrating the existing VUD design from live streaming to P2P video on-demand (VoD) systems. To address the immediate concern of the huge overhead while applying VUD to P2P VoD, we formulate the problem into an optimization problem aiming at minimizing the total overhead induced by VUD, which proves to be a 0-1 integer programming problem. Due to the intractability of this NP-hard problem, we propose a simple yet effective heuristic water-leveling algorithm to balance the supply and demand of bandwidth resources among the system while reducing VUD overhead. Finally, numerical results are presented to demonstrate the efficacy of our overhead-aware VUD design for P2P VoD systems.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".