QoS-Based Composition of Service Specific Overlay Networks
Why this work is in the frame
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Bibliographic record
Abstract
Optimization of service compositions represents a challenging area of research in mobile networks. The use of formal semantic descriptions of service interfaces and functionalities enable automated reasoning over service compositions. The growing challenge of finding effective methods to satisfy non-functional QoS requirements of customers exacerbates the composition problem. Fuzzy reasoning provides an effective means to represent QoS concepts that are vague or approximate thus providing a flexible best-match service query scheme. This paper presents a new fuzzy-induced, semantic-similarity-based service-specific overlay network composition over a hybrid service overlay network. The presented composition method allows for efficient, accurate, and QoS-aware component service discovery, composition, and execution. We demonstrate that our approach provides better performance and reduced composition delay when compared to other service-composition solutions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it