Ad-Hoc Cloudlet Based Cooperative Cloud Gaming
Bibliographic record
Abstract
As the game industry matures, processing complex game logics in a timely manner is no longer an insurmountable problem. However, current cloud-based mobile gaming solutions are limited by their relatively high requirements on Internet resources. Also, they typically do not consider the geographical locations of nearby mobile users and thus ignore the potential cooperation among them. Therefore, inspired by existing cloud computing techniques, we propose an ad hoc mobile-cloudlet-cloud based approach to implement cooperative gaming architecture. In this paper, two modules of the architecture are introduced: (1) progressive game resources download, by which mobile users can adaptively download gaming resources from cloud servers or nearby mobile users, (2) ad-hoc mobile based cooperative task allocation, by which gaming components can be executed dynamically on local devices, nearby devices, stationary cloudlet(s), or cloud servers. The mechanisms of both modules are formulated as optimization problems and algorithms are proposed to solve them. Simulations results based on real mobility traces show that our system's performance depends highly on the ad-hoc network environment. Our scheme has lower system resource usage while utilizing resources of nearby devices, compared to the cloud-based gaming architecture; and performs better with short on-device task duration compared to code-offloading based architecture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".