Publiy+: A Peer-Assisted Publish/Subscribe Service for Timely Dissemination of Bulk Content
Bibliographic record
Abstract
Publish/Subscribe (P/S) systems and file sharing applications traditionally share the common goal of disseminating data among large populations of users. Despite this similarity, the former focuses on timely dissemination of small-sized notification messages, while the latter presumes larger types of bulk content with less emphasis on the time needed between release and delivery of data. In this paper, we develop a peer-assisted content dissemination mechanism to bridge this gap by adopting the P/S model. We propose a hybrid two-layer architecture in which P/S brokers act as coordinators and guide their clients with interest in similar content to engage in direct exchange of data blocks in a peer-to-peer and cooperative fashion. Furthermore, we use network coding in order to facilitate data exchange among clients. Our peer-assisted scheme offloads the burden of disseminating huge volumes of data from P/S brokers to subscribers themselves. As an added advantage of our approach, brokers employ strategies that help shape traffic flows in multi-domain network settings. Finally, we have implemented our approach and carried out extensive large-scale experimental evaluation on a cluster with aggregate data transfers of up to 1 TB and involving up to 1000 subscribers. Our results demonstrate good scalability and faster content delivery compared to file sharing protocols such as BitTorrent.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".