Exploring the Sustainability of Credit-incentivized Peer-to-Peer Content Distribution
Bibliographic record
Abstract
Credit-based incentives were proposed to incite peer contributions in P2P content distribution systems. Their effectiveness was extensively analyzed from a game theory perspective. Little attention however has been paid to a potential threat to such systems -- the possible condensation of credits in a small number of peers over time. Credits condensation puts system sustainability on the line: many peers gradually run out of credits and cannot keep up a decent download rate. We study the sustainability of credit-based P2P systems running for a long period of time. We first introduce a new queueing network based model for credit circulation in a P2P content trading market. This model enables the study of credit system sustainability via examining the stability of stochastic traffic flows in a network of queues. We show that a stable job circulation, i.e., an equilibrium market state, always exists. A sufficient and necessary condition for asymptotic condensation at equilibrium is proved. We analyze the degree of condensation in finite networks using the Gini index, and relate condensation to P2P network protocols and parameters. Our theoretical results are verified and supported by extensive simulations under realistic settings. We propose counter-actions for preventing and mitigating credit condensation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".