On the design of hybrid peer-to-peer systems
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we consider hybrid peer-to-peer systems where users form an unstructured peer-to-peer network with the purpose of assisting a server in the distribution of data. We present a mathematical model that we use to analyze the scalability of hybrid peer-to-peer systems under two query propagation mechanisms: the random walk and the expanding ring. In particular, we characterize how the query load at the server, the load at peers as well as the query response time scale as the number of users in the peer-to-peer network increases. We show that, under a properly designed random walk propagation mechanism, hybrid peer-to-peer systems can support an unbounded number of users while requiring only bounded resources both at the server and at individual peers. This important result shows that hybrid peer-to-peer systems have excellent scalability properties. To the best of our knowledge, this is the first time that a theoretical study characterizing the scalability of such hybrid peer-to-peer systems has been presented. We illustrate our results through numerical studies.
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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 | 0.001 | 0.001 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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