Update-Aware Scheduling Algorithms for Hierarchical Data Dissemination Systems
Bibliographic record
Abstract
Mechanisms to efficiently and effectively transmit up-todate information to clients are of significant interest. Broadcast-based scheduling in hierarchical data dissemination systems are under reported in the literature. In these systems a primary server accepts updates that are broadcast to secondary servers and then to a population of clients upon requests. This paper focuses on data dissemination with update propagation at the primary server side. Our initial study shows that at high update rates, a straightforward broadcast scheduler that ignores clients' access patterns can provide clients with outdated information more than 80% of the time. We propose three broadcast scheduling algorithms that primarily differ in how data dissemination with update propagation is guided at the primary and secondary servers. We present mechanisms based on real and predicted clients' access patterns. We evaluate the new scheduling algorithms by running an extensive set of experiments. The performance study illustrates that the third algorithm, which depends on predictive scheduling at both the primary and the secondary servers, provides the best response time and the reception of up-to-date information.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".