Network Latency Classification for Computer Games
Bibliographic record
Abstract
WTFast’s Gamers Private Network (GPN) technology improves and stabilizes network latency of communication between players and servers in online video games, especially when players are distributed worldwide. Latency is known to be the most critical factor in gaming quality of experience. We investigate the classification of game sessions based on their features to five “playability” levels of latency using a large data set collected from the GPN network in 2019–2020. Various machine learning models were developed using this data set and evaluated using conventional and new performance metrics. The results confirm that such a classifier could be developed with reasonable average accuracy. The correct classification and hence “playability” of a game session based on its environmental, sizing, game type, and physical features is important for the operation and continuous management of a game-focused network such as the GPN. The use of an effective machine learning classifier will pave the way for building an effective and productive pipeline for game traffic routing and dynamic network reconfiguration. While the proposed measure of classifier performance shows promise, more research work would be required to understand its mathematical and statistical properties and its relationships to existing metrics: (a) because it’s a very specific type of application area (gaming+networking) for which there is relatively little ML research; (b) what very-high quality classifiers could mean for a real-time GPN management system that would provide QoE feedback to the GPN routing heuristics.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".