Enabling HLA-based Simulations on the Cloud
Bibliographic record
Abstract
The HLA framework is widely used to formalize simulations and achieve reusability and interoperability of simulation components. In order to manage the underlying system of HLA-based simulations, Grid Computing and Cloud Computing are employed to tackle the details of operation, configuration, and maintenance of simulation platforms that simulation applications run on. However, to make a simulation-run-ready environment among different types of computing resources and network environments is challenging, especially for modelers who may not be familiar with the management of distributed systems. In this article, we propose a new cloud-based scheme for HLA based simulations, aiming to ease the management of underlying resources, particularly for those located on geographically distributed locations, and to achieve rapid elasticity that can provide adequate computing capability to end users. An approach for handling diverse network environments is given, by adopting it, idle public resources can be easily configured as additional computing resources for the local cloud infrastructure. In the experiments, compared with its corresponding Grid Computing platform, this Cloud Computing platform achieves a similar performance but with many advantages that Cloud can provide, such as energy consumption, security, and multi-user availability.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".