On the locality of BitTorrent-based video file swarming
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it