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. Many researchers are now trying to find ways to optimize the gaming system regarding the network usage, local resource utilization, and energy consumption. 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 users. Therefore, inspired by existing cloud computing techniques and the concept of ad-hoc cloudlet computing, in this paper, we propose an ad-hoc cloudlet based gaming architecture. 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 according to the gaming progress, and 2) ad-hoc cloudlet-based cooperative task allocation, by which gaming components can be executed dynamically over local devices, nearby devices, or cloud servers. We also formulate the mechanism for both modules as an optimization problem and propose several algorithms for both modules, which are later used for evaluation purposes. We carry out simulations based on real mobility traces, and the results show that our system's performance depends highly on the ad-hoc network environment (the more concurrent and balanced connections within the ad-hoc network, the lower the energy costs). Also, regardless of the network environment, our system has lower energy costs while utilizing resources of nearby devices, compared to the cloud-based gaming architecture.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".