Network Performance Measurement Framework for Real-Time Multiplayer Mobile Games
Bibliographic record
Abstract
Player satisfaction with real-time multiplayer mobile games is correlated with performance of the communications network. The network is the most dynamic component of such games; congestion and channel loss figure prominently in achieving bounds required for real-time response. If messages cannot be delivered on-time, game developers must use predictive techniques to maintain the game experience. Additional complexity in game play requiring more bandwidth and/or processing could be possible under favourable network conditions. In this paper, we provide a light weight, embedded measurement framework that obtains frame rate, one-way latency, and frame duration within a game session. The captured data can be used by game designers to tune game complexity and to manage predictive algorithm parameters. Game designers use these predictive algorithms to maintain an approximation of what occurs in real-time, despite delays from network transmission. Our initial test results show that we are able to obtain the necessary information efficiently and note that the game deployed in our case study experiences occasional large delays.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".