Studying the Performance Impact of SOA Design Patterns via Coupled Model Transformations
Bibliographic record
Abstract
Early performance analysis of designs for Service Oriented Architecture (SOA) can be based on performance models derived from the design models using known techniques, such as Performance from Unified Model Analysis (PUMA). When a SOA design pattern is applied to solve some architectural, design or implementation problem, it impacts the design model and its derived performance model. Conventionally, the performance model needs to be reconstructed to reflect the design pattern changes on the design model. This thesis proposes a technique to trace the causality from the design changes introduced by the pattern application to the corresponding changes in the performance model. The approach takes as input a SOA design model expressed in UML extended with two standard profiles: SoaML for expressing SOA solutions and MARTE for performance annotations. The SOA design patterns are specified using Role Based Modeling (RBM) and the performance model is expressed in Layered Queueing Networks (LQN).
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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 | 0.002 | 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".