A Dynamic Networking Substrate for Distributed MMOGs
Bibliographic record
Abstract
This paper proposes a distributed and dynamic networking architecture for massively multiplayer online games (MMOGs). The MMOG networks deal with the challenge of update message exchange among a large number of players that are subject to both mobility (constant change of virtual location) and churn (joining and leaving the physical network at will). Ideally, a player’s update messages should be multicasted to the player’s area of effect, which is a neighborhood around the player. But, this requires the system to have a centralized indexing service that keeps record of players residing in each region, making the system less scalable as the number of players increases. The use of geometric routing helps alleviate this requirement by exploiting location addressing and thus eliminating the need for IP-search queries. However, geometric routing comes with a number of convergence and performance issues that require solutions for reducing hop-count and minimizing overall delay while providing guaranteed message delivery. In this paper, we propose a geometric routing overlay for message exchange among a large number of MMOG players that not only provides reduced delay and guaranteed delivery, but also supports player mobility and churn. In addition, we enhance our greedy routing method to more efficiently support long distance messages. We demonstrate the effectiveness of our proposed scheme using both theory and simulations.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".