Towards Peer-to-Peer Based Distributed Simulations on a Grid Infrastructure
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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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