QoS-aware Service Composition over Graphplan through Graph Reachability
Bibliographic record
Abstract
QoS-aware service composition is a bi-objectivetask for the generation of a business process: to fulfill functional goals and to optimize the QoS criteria. Planning algorithms are frequently used for the generation of a business process to achieve functional goals. In this paper, we use a planning algorithm, GraphPlan, and a graph search algorithm, Dijkstra's algorithm, to achieve both functional goals and QoS optimization at the same time. Firstly, we analyze graph reachability in the planning graph built by Graphplan algorithm.Taking advantage of graph reachability, we propose an approach of using Graphplan technique combined with Dijkstra's algorithm to solve QoS-aware service composition problem. The experiments show our approach is able to findthe optimal solution for different QoS criteria. Moreover, our approach reduces the possibilities of combinatorial explosion to a large degree when exploring the graph for the optimal path.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".