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 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 | 0.002 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".