Bibliographic record
Abstract
BitTorrent is the most popular peer-to-peer software for file sharing, which has contributed to a significant portion of today's Internet traffic. Many measurement studies have been devoted to the BitTorrent system at the peer-level; yet few have examined the microscopic piece-level, in particular, the piece populations. This information is very useful in understanding the dynamics and evolution of BitTorrent swarms, and especially the effectiveness of its rarest-first policy that strives to ensure an even distribution of pieces. In this paper, we present a systematic measurement study on the distribution and evolution of the piece population in BitTorrent. Our measurement is based on real BitTorrent data gathered from both the Internet and controlled PlanetLab swarms. The data is collected by multiple administrated clients distributed in different parts of the network, which collectively offer a global view of the piece distribution. We analyze both snapshot data of the near-instantaneous population of pieces in BitTorrent swarms, and long-term data of the evolution of the piece population over several days, especially during the early phases of the swarm's lifetime. Our results validate that the downloading policy of BitTorrent is quite effective from a piece distribution and evolution perspective; yet enhancements are still possible to achieve the ideal piece distribution.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".