Modeling and simulation as a service architecture for deploying resources in the Cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, Cloud Computing has become popular to facilitate the use of Modeling and Simulation (M&S) resources. Nevertheless, there are still various issues to solve, including the structure constrain of chosen web service frameworks, the sharing of varied resources in the Cloud, and the difficulties in reproducing experiments. We show a new architecture based on Cloud Computing and new modeling methods to deal with these issues. This layered architecture, called Cloud Architecture for Modeling and Simulation as a Service (CAMSaaS), simplifies the deployment of M&S resources as services in the Cloud. CAMSaaS supports hierarchical resource services, experimental frameworks, scalable infrastructure and makes everything as a service. We deploy varied M&S resources as services in the Cloud, and build a Modeling and Simulation as a Service (MSaaS) middleware called CloudRISE to manage a variety of M&S resources. We also use the experimental framework concept to simplify the management of experiment environments. We present a case study for crowd evacuation application using the architecture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.000 | 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