Towards Peer-to-Peer Based Distributed Simulations on a Grid Infrastructure
Bibliographic record
Abstract
Grid based distributed simulations are becoming more and more important with the increasing number of large-scale modeling and simulation applications. Distributed simulations are evolving with modern distributed computing techniques and are facing new challenges such as interoperability, reusability, scalability, etc. distributed interactive simulation and high level architecture have been the dominant distributed simulation standards for the past few years, and HLA is still the backbone for supporting federate based distributed simulations. However, HLA relies heavily on centralized runtime infrastructure (RTI) and is not easy to scale for large-scale applications. Also, its interoperability is limited since it does not use a fully opened standard such as service oriented architecture (SOA). Therefore, a lot research has been done to promote the next generation of simulation architecture. Such efforts result in the XMSF, which tries to integrate SOA with distributed simulation. In the meantime, peer-to-peer network based distributed simulations are also attracting more researchers to investigate the feasibility of decentralized architecture for large-scale distributed simulations. In this paper, we propose a hierarchical service oriented JXIA-core multi-layered architecture for large scale distributed simulations. Our particular design consideration is dynamic reconfigurable and realtime capable distributed simulation infrastructure, and we also aim to address most of the concerns regarding grid based large-scale distributed simulation. We further verify our design through a formal DEVS simulation based modeling. We believe that a decentralized framework will be dominant in the area of distributed simulations in the near future due to its flexibility, scalability, and the ease of reconfiguring simulation applications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".