An Efficient Time Management Scheme for Large-Scale Distributed Simulation Based on JXTA Peer-to-Peer Network
Bibliographic record
Abstract
As an emergence technology, P2P is spreading to distributed simulation area, and many distributed simulation frameworks have used P2P as the middleware to interconnect their existing single processor's simulators to form distributed environments for simulation execution. In terms of simulation time management, most existing tools use a middleware layer to implement and support time management in a heterogeneous networking environment, and therefore, it is generally not easy to migrate a single processor's simulation to multi-processors in these frameworks. In this paper, we present a P2P based distributed simulation time management based upon JXTA API and Service Oriented Architecture (SOA), and we focus our discussion on how we implement the time management as a JXTA peer and a JXTA group service. Our time management is actually a native distributed message passing management framework, and does not rely on any middleware layer. Furthermore, we evaluate the performance of our implementations using a local Linux cluster. This work will establish a solid foundation for the more advanced distributed simulation services that have been proposed in our project [1].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".