Toward new peering strategies for push-pull based P2P streaming systems
Bibliographic record
Abstract
Recently, several mesh-based P2P live streaming systems are adopting a push-pull mechanism instead of the classical pull mechanism. A push-pull mechanism is more efficient in terms of overhead and leads to much better playback delay performance because it eliminates the need of the three steps of pull content retrieval: buffer map broadcast, data request and data sending. Thus, using the pull mechanism is not the best way to evaluate the performance of peering strategies especially the ones targeting playback delay minimization. We propose to revisit the peering strategies with a focus on playback delay minimization. Such strategies will benefit from the push-pull mechanism as the pull part is used mainly at the beginning of the session or to recover lost content. We believe that making the right decisions about node relationships will boost the performance of P2P systems. We propose new peering strategies that are compared, through simulations, with some recent strategies. Results show that one of the proposed strategies outperforms significantly the existing ones with respect to the playback delay experienced by participating nodes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 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".