Improving the streaming capacity in P2P VoD systems with helpers
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
Peer-to-peer (P2P) video-on-demand (VoD) is a promising solution to provide video service to a large number of users. Streaming capacity in a P2P VoD system is defined as the maximal streaming rate that every user can receive. Due to the upload bottleneck, the streaming capacity in the P2P VoD system is limited. In this paper, we introduce helpers in the P2P VoD system and then optimize the helper resources to improve the streaming capacity. Specifically, we first optimize the helper assignment using a greedy algorithm. Then we develop a proximal distributed algorithm to maximize the streaming capacity by optimizing the link rates. Through simulations, we demonstrate that the P2P VoD system with optimized helpers can obtain a much higher streaming capacity compared to the P2P VoD system without any helper or the one with randomly assigned helpers.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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