A Distributed Topology Control Algorithm for P2P Based Simulations
Bibliographic record
Abstract
Although collaborative distributed simulations and virtual environments (VE) have been an active area of research in the past few years, they have recently gained even more attention due to the emergence of online gaming, emergency simulation and planning systems, and disaster management applications. Such environments combine graphics, haptics, animations and networking to create interactive multimodal worlds that allows participants to collaborate in realtime. Massively Multiplayer Online Gaming (MMOG), perhaps the most widely deployed practical application of distributed virtual environments, allows players to act together concurrently in a virtual world over the Internet. IP Multicasting would be an optimal solution for the dissemination of updates among participants, but IP multicasting is not available to home users on the Internet, due to a number of technological, practical, and business reasons. In light of the lack availability of IP Multicasting on the global Internet, researchers have recently tended to shift multicasting from the networking layer to the application layer, known as Application Layer Multicasting, effectively constructing an overlay network among participants of the distributed simulation where end hosts themselves participate in the dissemination of update messages. In this paper, we propose a topology control architecture to support P2P based collaborative distributed simulations over the Internet by using AIM. We present our networking model and its rationale, theoretical proof, and simulation measurements in comparison with other methods as proof of concept.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".