A Framework for Verifying SLA Compliance in Composed Services
Bibliographic record
Abstract
Service level agreements (SLAs) impose many non-functional requirements on services. Business analysts specify and check these requirements in business process models using tools such as IBM WebSphere Business Modeler. System integrators on the other hand use service composition tools such as IBM WebSphere Integration Developer to create service composition models, which specify the integration of services. However, system integrators rarely verify SLA compliance in their proposed composition designs. Instead, SLA compliance is verified after the composed services are deployed in the field. To improve the quality of the composed services, we propose a framework to verify SLA compliance in composed services at design time. The framework re-uses information in business process models to simulate services and verify the non-functional requirements before the service deployment. To demonstrate our framework, we built a prototype using an industrial process simulation engine from IBM WebSphere Business Modeler and integrate it into an industrial service composition tool. Through a case study, we demonstrate that our framework and the prototype assist system integrators in composing services while considering the non-functional requirements.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".