Exploring Peer-to-Peer Locality in Multiple Torrent Environment
Bibliographic record
Abstract
The fast-growing traffic of Peer-to-Peer (P2P) applications, most notably BitTorrent (BT), is putting unprecedented pressure to Internet Service Providers (ISPs). P2P locality has, therefore, been widely suggested to mitigate the costly inter-ISP traffic. In this paper, we for the first time examine the existence and distribution of the locality through a large-scale hybrid PlanetLab-Internet measurement. We find that even in the most popular Autonomous Systems (ASes), very few individual torrents are able to form large enough local clusters of peers, making state-of-the-art locality mechanisms for individual torrents quite inefficient. Inspired by peers' multiple torrent behavior, we develop a novel framework that traces and recovers the available contents at peers across multiple torrents, and thus effectively amplifies the possibilities of local sharing. We address the key design issues in this framework, in particular, the detection of peer migration across the torrents. We develop a smart detection mechanism with shared trackers, which achieves 45 percent success rate without any tracker-level communication overhead. We further demonstrate strong evidence that the migrations are not random, but follow certain patterns with correlations. This leads to torrent clustering, a practical enhancement that can increase the detection rate to 75 percent, thus greatly facilitating locality across multiple torrents. The simulation results indicate that our framework can successfully reduce the cross-ISP traffic and minimize the possible degradation of peers' downloading experiences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".