Approach for generating performance models from UML models of SOA systems
Bibliographic record
Abstract
Model-Driven SOA is an emerging approach for developing service-oriented systems using models at different levels of abstractions and applying model transformations to generate either code or other models for the analysis of non-functional properties, such as performance. The paper proposes an approach for deriving layered queueing performance models for the evaluation of the runtime performance characteristics of such systems in the early development phases, before the entire system is built and can be deployed and measured. Early performance evaluation helps to choose an appropriate architecture, design and configuration alternatives, so that the final system meets its performance requirements. The starting point for derivation is a platform independent UML model of a SOA system representing the workflows, architecture of the underlying components offering services, and behavior of the corresponding runtime scenarios. A platform dependent model, obtained by weaving platform services into the platform-independent model through aspect-oriented modeling techniques, represents the source model for the transformation into a performance model. The deployment of the software on hardware resources is also part of the source model. The UML model is annotated with performance information by using the standard UML profile MARTE. The proposed approach is illustrated with a healthcare application.
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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 | 0.001 | 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".