Load management in model-aware execution of composite web services
Bibliographic record
Abstract
In the Service Oriented Architecture services are computational units that can be published, discovered, consumed and aggregated in the platform and organization independent manner. The most widely accepted way to achieve Service Orientation (SO) is with Web Services (WSs), due to the standardization efforts and the wide range of available infrastructure. One of the most interesting aspects of WSs is the ease with which they can be combined into Composite Web Services (CWSs). The most popular language to specify and implement CWSs is BPEL. While being easy to use, it also introduces difficulties to monitor and optimize CWSs, specifically in the selection of optimal WSs. This paper investigates the possibility to support this selection with dynamic load management, based on the alternative, model-aware, approach to orchestrate WSs with the Coloured Petri Nets (CPN) formalism. The use of the mathematically grounded formalism allows to model and verify properties of CWSs and enables at runtime guidance of the execution of the CWS. This paper presents how, during a model-aware execution of a CWS, to predict and avoid some of the undesirable behaviors of WSs. Compared to BPEL, the model-aware approach significantly improves the performance and manageability of CWSs and thus opens up new deployment scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".