A Multi-Layered Scheme for Distributed Simulations on the Cloud Environment
Bibliographic record
Abstract
In order to improve simulation performance and to integrate simulation resources among geographically distributed locations, the concept of distributed simulation is proposed. Several types of distributed simulation standards, such as DIS and HLA, are established to formalize simulations and achieve reusability and interoperability of simulation components. To implement these distributed simulation standards and to manage the underlying system of distributed simulation applications, we employ grid computing and cloud computing technologies. These tackle the details of operation, configuration, and maintenance of simulation platforms in which simulation applications are deployed. However, for modelers who may not be familiar with the management of distributed systems, it is challenging to make a simulation-run-ready environment among different types of computing resources and network environments. In this article, a new multi-layered cloud-based scheme is proposed for enabling modeling and simulation based on different distributed simulation standards. This scheme is designed to ease the management of underlying resources and to achieve rapid elasticity that can provide unlimited computing capability to end users; it considers energy consumption, security, multi-user availability, scalability, and deployment issues. A mechanism for handling diverse network environments is described; by adopting it, idle public resources can be easily configured as additional computing capabilities for the local resource pool. A fast deployment model is built to relieve the migration and installation process of this platform. An energy-saving strategy is utilized to reduce the consumption of computing resources. Security components are implemented to protect sensitive information and block malicious attacks in the cloud. In the experiments, the proposed scheme is compared with its corresponding grid computing platform; the cloud computing platform achieves similar performance, but incorporates many advantages that the Cloud can provide.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".