Design and Performance Evaluation of Cloud-Based XML Publish/Subscribe Services
Bibliographic record
Abstract
XML message filtering and routing have been recognized as a standard for data exchange for XML dissemination services. These services are often realized using the Publish/Subscribe (pub/sub) model. The Pub/sub model is commonly used by various web-based systems, such as Web content syndication, RSS feeds, location-based services. Conventional XML filtering and forwarding is an application-layer multicast approach that relies on XML-capable brokers. Those XML-capable brokers are built above the network layer using an overlay model for message dissemination. Such an overlay model introduces a great deal of overhead in terms of initial deployment and subsequent operational cost for those XML-capable brokers that typically are supported by Internet Service Providers (ISPs). In other words, those XML-capable brokers need to be set up across geographically distributed areas and may need to be deployed and maintained even by multiple ISPs or special providers. This paper presents a XML message dissemination system using both the conventional XML multicast model and the peer model executing on a cloud, that can be deployed rapidly without the need of any ISP or special arrangements of physical brokers across the networks. In addition, changes to software deployed in the cloud can be made directly and easily. The paper demonstrates experiments over the Amazon EC2 clouds spanning different geographical locations. In addition, the paper presents a performance comparison between the conventional XML multicast model and the peer model deployed on a cloud.
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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 | 0.002 | 0.000 |
| 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 | 0.001 |
| 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".