Optimizing Web Service Composition with Graphplan and Fuzzy Control
Bibliographic record
Abstract
With the development of cloud computing, more and more applications are posted online to provide services for users.Since user needs can be complex, an individual service will not able to meet the requirement.Web Service Composition composes multiple web services together to fulfil the complicated user requirement.While searching an optimal composition with both functional and non-functional requirements still is a challenging problem that needs to be addressed.QoS-aware web service composition is an NP-hard problem.To solve this problem, we design a system which combines GraphPlan with Fuzzy Control algorithm.Fuzzy Control is employed to generate overall QoS according to user preferences.In the forward phase of Graphplan, less competitive services are pruned according to the overall QoS.In the backward phase, services are selected according to functional goals and their overall QoS.Furthermore, case study and are performed, and the experimental results show that our approach improves the quality of service composition significantly compared with ordinary and Skyline approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".