A Novel Game Map Preloading and Resource Provisioning Scheme in Cooperative Cloud Networks
Bibliographic record
Abstract
With the popularization of mobile smart devices (e.g., smartphones, tablets), the number of mobile game players increases significantly. This makes mobile gaming industry rise and take up a large piece of global game market. Different from the traditional players playing games on their personal computers with wired local area network or Wi-Fi access, mobile game players nowadays play online games on their smart devices that communicate to cloud servers via wireless cellular networks. Therefore, the monetary cost for game content downloading and updating via cellular networks may increase the burden of gamers. Meanwhile, an unpredictable latency may be introduced by communication links of wireless cellular networks. This may negatively affect player's gaming experience. In fact, we can predict the next movement of the player in the game and preload the maps that have high probability to go. Meanwhile, if any of these maps have been downloaded and cached by other players nearby, with the Device-to-Device (D2D) communication networks, people may preload these maps freely. In this paper, we focus on the problem that how to select maps to preload from either game server or neighborhood with the consideration of limited storage space on smart devices. We first formulate an optimization problem. Since the formulated problem is NP-hard, we decouple the problem into two subproblems and solve them iteratively to get a suboptimal solution. Simulation results show that our proposed scheme can significantly increase the utility received by mobile players.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".