A scalable adaptive time synchronization protocol for Large Scale Distributed Collaborative Simulation Environment
Bibliographic record
Abstract
With the emergence of massive multiplayer online games (MMOG) and distributed distant learning/e-learning applications, distributed virtual environments (DVE) have received a good deal of attention. As a highly human-computer interacting environment, DVE provides an ideal platform for real-time message exchanges among virtual objects, human-beings. However, consistency and scalability are becoming indispensable challenges considering the complexities of the existing heterogeneous network architecture as well as the state synchronization requirement of DVEs. In this paper, we made an effort to address these issues by proposing a novel JXTA-based overlay peer to peer architecture for large scale distributed collaborative virtual simulation environments. Compared with our previous implementations based on Department of Defense (DoDpsilas) High Level Architecture (HLA/RTI), this novel architecture is able to provide consistency, scalability and fault tolerance by taking advantages of state-of-the-art of the peer to peer computing paradigm. Our experimental results show that the scalability of DVEs is improved significantly whit our Peer-to-Peer DVE architecture. As a step further, we also propose a time and state synchronization algorithm and evaluate its performance using a series of simulation experiments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
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 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".