Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it