QoS-Aware Service Selection for Abstract Workflows using Provenance Data and Fuzzy Constraint Satisfaction Modeling.
Bibliographic record
Abstract
Service-oriented applications are using services that most accurately meet their requirements; as a result Quality of Service (QoS)-based service selection mechanisms play an essential role in service-oriented architectures. In service-oriented environments such as the Grid, we usually define abstract workflows to enable the binding of services at runtime. Using service discovery techniques to discover services that functionally match tasks of the abstract workflow, enables to differentiate services in order to select the ones that better satisfy user QoS requirements. In this paper, we model the QoS-aware service selection for abstract workflows as a Constraint Satisfaction Problem (CSP) and demonstrate that soft CSPs like fuzzy CSP are an appropriate approach for these purposes. We provide evaluations of the model and discuss how effective the process of abstract workflow service selection can be when merging it with the degree of satisfaction. Furthermore, we exploit the hill climbing strategy using special heuristics to achieve a speedup in order to make the search process more effective and scalable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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".