Scaling online games with adaptive interest management in the cloud
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
Traditional client-server online games do not scale well in terms of the number of players they can support. Most of this is due to the quadratic growth of bandwidth requirements as the number of players increases, and the limitations on processing power of any single machine. Considering the excitement a first-person shooter (FPS) game can provide by bringing an epic scale online battle to real life, we present a scalable cloud-based architecture able to host hundreds of players in an online FPS game. We host the game in a cloud, rather than on a single machine, and reduce aggregate bandwidth requirements of the game by using a scalable publish-subscribe subsystem. Each player expresses its preferences about other players using an interest set, and receives updates accordingly. Our evaluations, both in a testbed and cloud environment, show our architecture can scale to hundreds of players, an order of magnitude more players than state-of-the-art FPS game servers can currently support.
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 | 0.001 | 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