On the locality of BitTorrent-based video file swarming
Bibliographic record
Abstract
Abstract — In the past few years, there have been tremendous interest in the peer-to-peer(P2P) content delivery. Although this communication paradigm does not need a dedicated server infrastructure, it dramatically increases the traffic over inter-ISP links. In particular, the most popular P2P application, BitTorrent(BT) generates a huge amount of traffic on the Internet. To address this challenge, P2P locality has been examined, which explores the access to local resources to optimize the inter-ISP traffic. However, most of these approaches have focused on a global strategy, and attempted to change the peer selection mechanism, which potentially affects the random topology of BT and thus reduces its robustness. The content and the peer diversities are seldom discussed, particularly the video file swarms of distinct characteristics. In this paper, we for the first time examine the different BT contents and peer properties in regards to the locality issues through a large-scale measurement. We demonstrate the distinct characteristics of video file swarms, and find that the distribution of the AS clusters (a set of peers belonging to the same AS) follows the Mandelbrot-zipf law. Our results also suggest that the peer in a few ASes are more likely to form large AS clusters and most ASes on the Internet do not have enough potential for locality. Therefore, a global locality approach may not be our best choice. We then address the problem through a selective locality approach based on a novel peer prediction method. I.
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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 | 0.010 |
| 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 | 0.002 |
| 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".