Bibliographic record
Abstract
In peer-to-peer (P2P) systems, a receiver needs to be matched with multiple senders, because peers have limited capacity and reliability. Efficient peer matching can reduce the cost on Internet Service Providers (ISPs) for carrying the P2P traffic. We study the following peer-matching problem: given a set of potential senders, find the best subset of them that will minimize the transit cost on ISPs. This problem is fairly general and the proposed algorithms for solving it can be used in many P2P systems. We propose two ISP-friendly algorithms for solving this problem: ISPF and ISPF-Lite. These two matching algorithms leverage public available information, such as BGP tables, to infer the network topology, and to minimize the cost on ISPs. The inference algorithms, however, are fairly complex, and we propose optimization techniques to reduce the inference time and to lower the memory requirement. We use trace-driven simulations to show that the proposed algorithms outperform other popular matching algorithms by a large margin. Between the two proposed algorithms, ISPF results in better matching, but incurs higher complexity. Hence, we recommend ISPF if resources are not stringent, otherwise ISPF-Lite is recommended.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".