Publisher Placement Algorithms in Content-Based Publish/Subscribe
Why this work is in the frame
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Bibliographic record
Abstract
Many publish/subscribe systems implement a policy for clients to join to their physically closest broker to minimize transmission delays incurred on the clients' messages. However, the amount of delay reduced by this policy is only the tip of the iceberg as messages incur queuing, matching, transmission, and scheduling delays from traveling across potentially long distances in the broker network. Additionally, the clients' impact on system load is totally neglected by such a policy. This paper proposes two new algorithms that intelligently relocate publishers on the broker overlay to minimize both the overall end-to-end delivery delay and system load. Both algorithms exploit live publication distribution patterns but with different optimization metrics and computation methodologies to determine the best relocation point. Evaluations on PlanetLab and a cluster testbed show that our algorithms can reduce the average input load of the system by up to 68%, average broker message rate by up to 85%, and average delivery delay by up to 68%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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