Dynamic file-selection policies for bundling in BitTorrent-like systems
Bibliographic record
Abstract
BitTorrent-like swarming technologies are very effective for popular content, but less so for the `long tail' of files with disparate popularities, which do not have sufficiently many peers to enable efficient collaboration. Performance degradations are especially pronounced in swarms with reduced file availability. Static bundling groups files into a single data content. It requires no modification to the BitTorrent client, and has been shown to improve availability of unpopular files in BitTorrent swarms. However, as peers are forced to download undesired file pieces, download times increase, especially for peers downloading popular files. We propose to use Stochastic Games and Markov Decision Process (MDP) to model and analyze optimal peer strategies, in a selfish and a cooperative setting respectively, for a BitTorrent-like system with multiple files. Each peer wishes to download a subset of the files, and we allow peers to dynamically decide whether to collaborate with peers targeting a different set of files or not, given the current system state. The Stochastic Game and MPD models take into account both piece availability and average download times, and allow us to study if and when downloading unwanted content can be beneficial. We use dynamic programming to solve the two models, contrast the level of collaboration observed in the selfish and the cooperative settings, and propose an enhanced piece selection mechanism for BitTorrent-like systems with dynamic download decision making. We demonstrate the effectiveness of dynamic file piece selection through both simulations and experiments using a modified BitTorrent client.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".