GPU/QoE-Aware Server Selection Using Metaheuristic Algorithms in Multiplayer Cloud Gaming
Bibliographic record
Abstract
The combination of cloud computing and advancements in GPU have made many real time services possible, including Cloud Gaming (CG). Doing all the process-intensive tasks in the cloud frees players from upgrading their heterogeneous devices and installing new software, and lets them play wherever and whenever. However, higher quality is always demanded by players. For instance, as frame rate has major impact on the player's gaming performance, demand of higher frame rate is increasing. On the other hand, service providers aim to offer cost effective services. Management of the graphic-intensive CG service demand and maximizing the service providers' benefits is an issue that must be addressed properly. As remote GPU plays the main role of rendering and is the most expensive infrastructure rented by the service provider, its appropriate management is vital to address the above issue. To do so, we formulate the problem in an efficient manner and propose two methods to maximize both GPU utilization and the users' quality of experience (QoE) at the same time, subject to the constraints of the servers. Our methods are based on two metaheuristic algorithms to solve an NP-Hard optimization problem for GPU-based server selection. Our simulation results shows that by increasing the number of players, both algorithms have increasing performance in terms of GPU utilization, reduced capacity wastage, and QoE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".