Cloud Scaling Policies Verifier System driven by STORM Model Checker
Bibliographic record
Abstract
Abstract Interpreting the stochastic behavior of cloud scaling policies formalized and verified with a model checker, using an independent visualization tool can be time-consuming as several manual steps need to be taken to visualize the behavior. This is because the existing model checker presents verification results through text-based descriptions and line charts depicting probabilities or rewards against investigated variables. However, with the line charts, the user can only analyze the probabilities or rewards instead of the behavior. To address this, we propose a cloud scaling policies verifier system that utilizes the extensible STORM model checker to automatically visualize the behavior of the verified model and the verification results. Additionally, the system architecture integrates the desktop local environment with the Docker container environment using open-source technologies including StormPy, PyQt, Networkx, and Matplotlib to enable replication and innovation for future research. Moreover, the system has undergone evaluation through functional and integration testing, demonstrating its effectiveness. Finally, it has been demonstrated that the system can be utilized not only for checking and verifying various cloud scaling policies but also for other decision-making policies beyond cloud scaling.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".