How P2P streaming systems scale over time under a flash crowd
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Bibliographic record
Abstract
Abstract—Peer-to-Peer (P2P) live video streaming systems have recently received significant attention, with commercial deployment gaining increased popularity in the Internet. It is evident in our empirical experiences with real-world systems that, it is not uncommon to have hundreds of thousands of viewers trying to join a program in the first few minutes of a live broadcast. This phenomenon in live streaming systems, referred as the flash crowd, poses unique challenges in the system design. In this paper, we develop a mathematical model to capture the inherent relationship between time and scale during a flash crowd. We derive an upper bound on the system scale, and then demonstrate that the timing factor plays a critical role for such a system to scale. In addition, our analysis also brings a more indepth understanding with respect to the use of Gossip protocols, i.e., the effects of partial knowledge. I.
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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 | 0.001 | 0.001 |
| 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