Dynamic swarm management for improved BitTorrent performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BitTorrent is a very scalable file sharing protocol that utilizes the upload bandwidth of peers to offload the original content source. With BitTorrent, each file is split into many small pieces, each of which may be downloaded from different peers. While BitTorrent allows peers to effectively share pieces in systems with sufficient participating peers, the performance can degrade if participation decreases. Using measurements of over 700 trackers, which collectively maintain state information of a combined total of 2.8 million unique torrents, we identify many torrents for which the system performance can be significantly improved by re-allocating peers among the trackers. We propose a light-weight distributed swarm management algorithm that manages the peer torrents while ensuring load fairness among the trackers. The algorithm achieves much of its performance improvements by identifying and merging small swarms, for which the performance is more sensitive to fluctuations in the peer participation, and allows load sharing for large torrents. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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