Exploring interest correlation for peer-to-peer socialized video sharing
Bibliographic record
Abstract
The last five years have witnessed an explosion of networked video sharing, represented by YouTube, as a new killer Internet application. Their sustainable development however is severely hindered by the intrinsic limit of their client/server architecture. A shift to the peer-to-peer paradigm has been widely suggested with success already shown in live video streaming and movie-on-demand. Unfortunately, our latest measurement demonstrates that short video clips exhibit drastically different statistics, which would simply render these existing solutions suboptimal, if not entirely inapplicable. Our long-term measurement over five million YouTube videos, on the other hand, reveals interesting social networks with strong correlation among the videos, thus opening new opportunities to explore. In this article, we present NetTube, a novel peer-to-peer assisted delivering framework that explores the user interest correlation for short video sharing. We address a series of key design issues to realize the system, including a bi-layer overlay, an efficient indexing scheme, a delay-aware scheduling mechanism, and a prefetching strategy leveraging interest correlation. We evaluate NetTube through both simulations and prototype experiments, which show that it greatly reduces the server workload, improves the playback quality and scales well.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".