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).To support the exploration of different patterns, the thesis proposes the following approaches: 1) Systematic identification of SOA design problem, selecting an appropriate pattern and binding the design with the RBM problem specification of the pattern; 2) Systematic recording of the SOA design changes (refactoring) using the RBM pattern solution specification; 3) Automatic derivation of the corresponding performance model changes from the design model changes using coupled transformation; 4) Automatic derivation of transformation directives from the performance model changes and annotation of the performance model with the transformation directives; 5) Automatic refactoring of the performance model by QVT model transformation.iii Systematic and automated pattern exploration techniques and the tools support developed in the thesis are illustrated and evaluated with a Browsing and Shopping SOA case study.A test suite was designed and used to verify all the major functionalities of the proposed approach.Furthermore, several design patterns are applied to the Browsing and Shopping SOA to validate their effectiveness in the process of performance analysis by a system designer.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".