Modelling performance optimizations for content-based publish/subscribe
Bibliographic record
Abstract
Content-based Publish/Subscribe (CPS) systems can efficiently deliver messages to large numbers of subscribers with diverse interests and consequently, have often been considered an appropriate technology for large-scale, event-based applications. In fact, a significant amount of existing research addresses the issue of providing scalable CPS services [3, 8, 7, 11]. In these approaches, scalability and high performance matching is often achieved by taking advantage of similarities between subscriptions. However, even though such optimization techniques are widely used, no model has been developed yet to capture them. Such an abstraction would allow CPS matching algorithms to be studied, analyzed, and optimized at a more fundamental and formal level. In this work-in-progress paper, we present the initial results of our work towards modelling and analyzing matching optimizations frequently used by CPS systems. Using our proposed model, we find that probabilistically optimal CPS matching is possible in certain types of subscription sets and that there is also a non-obvious upper bound on the expected cost of some subscription sets. We also provide experimental results that support the model proposed and studied in this paper.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".