Overlay Neighborhoods for Distributed Publish/Subscribe Systems
Bibliographic record
Abstract
The Publish/Subscribe (pub/sub) model has been widely applied in a variety of application scenarios which demand loose-coupling and asynchronous communication between a large number of information sources and sinks. In this model, clients are granted the flexibility to specify their interests at a high level and rely on the pub/sub middleware for delivery of their publications of interest. This increased flexibility and ease of use on the client side results in substantial complexity on part of the pub/sub middleware implementation. Furthermore, for several reasons including improved scalability, availability and avoiding a single point of failure, the pub/sub middleware is commonly composed of a set of collaborating message routers, a.k.a. brokers. The distributed nature of this design further introduces new challenges in ensuring end-to-end reliability as well as efficiency of operation. These challenges are largely unique to the pub/sub model and hence absent in both point-to-point or multicast protocols. This thesis develops solutions that ensure the dependable operation of the pub/sub system by exploiting the notion of overlay neighborhoods in a formal manner. More specifically, brokers maintain information about their neighbors within a configurable distance in the pub/sub overlay and exploit this knowledge to construct alternative forwarding paths or make smart forwarding decisions that improves efficiency, bandwidth utilization and delivery delay, all at the same time. Furthermore, in the face of failures overlay neighborhoods enable fast reconstruction of forwarding paths in the system without compromising its reliability and availability. Finally, as an added benefit of overlay neighborhoods, this thesis develops large-scale algorithms that bring the advantages of the pub/sub model to the domain of file sharing and bulk content dissemination applications. Experimental evaluation results with deployments as large as 1000 nodes illustrate that the pub/sub system scales well and outperforms the traditional BitTorrent protocol in terms of content dissemination delay.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".