Bibliographic record
Abstract
Peer-to-peer traffic constitutes more than 60% of today's Internet traffic, resulting in high bandwidth cost for ISPs. Recent efforts have been made to modify BitTorrent clients to reduce inter-ISP traffic. Although the results have been encouraging, recent research also reveals that global adaptation of such an approach may harm the download time as there is no clear evidence of persistent clustering in all ISPs. To this end, many large scale measurements on BitTorrent topology have been conducted by analyzing different snapshots of the BitTorrent network. However, the analysis overlooked the download time, the actual contributions of peers, and the distribution of peers throughout the file download period since the snapshots were obtained by querying the tracker for IP addresses of peers at a certain time. In this paper, we seek to understand to what extent the distribution of peers in BitTorrent relates to their contributions in data swarming and transmission rates by studying real BitTorrent download traces. In order to present an unbiased view, we collected the traces from over 100 different files, including books (in different languages), music (in different languages), movies, and software (for different operating systems). The file size ranges from 4 MB to 4 GB. We also compared traces from a regular BitTorrent client with an ISP-friendly BitTorrent client to examine the actual impact of an ISP-friendly algorithm on download time and peer contributions. Our major findings include that distance has no effect on the download rate in general, seeds or lechers cannot always be found within the same ISP, and a torrent can only benefit from an ISP-friendly approach in certain situations. Suggestions are given on how BitTorrent clients can be more ISP-friendly without sacrificing download rate.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".