A Statistical Network Traffic Model for First-Person Shooter Games
Bibliographic record
Abstract
Online games have now become significant contributors to Internet network traffic.As such, research effort is now devoted to the analysis and modeling of this network traffic to aid network designers to provision for gaming traffic in the networks they design.While many research studies have performed measurements on gaming network traffic, the results are game-specific and have not been unified into a framework.In general, network traffic can be represented by packet size and packet interarrival time parameters; therefore, a synthetic model can be built to deal with these two parameters as traffic features.In this paper, we present a technique for constructing a Hierarchical Hidden Markov Model that provides a packet level statistical model for First Person Shooter (FPS) gaming network traffic, which allows generation of traffic for various numbers of users through different game states.The proposed solution has been implemented for Counter-strike and Quake as two of the most popular online FPS games.The results derived from the models have then been used to successfully predict certain game related statistics. Index Terms-Network traffic generation
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".