A Distributed Topology Control Algorithm for P2P Based Simulations
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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