Resource optimization through hierarchical SDN-enabled inter data center network for cloud gaming
Bibliographic record
Abstract
Gaming on demand is an emerging service that combines techniques from Cloud Computing and Online Gaming. This new paradigm is garnering prominence in the gaming industry and leading to a new "anywhere and anytime" online gaming model. Despite its advantages, cloud gaming's Quality of Experience (QoE) is challenged by high and varying end-to-end communication delay. Since the significant part of the computational processing, including game rendering and video compression, is performed on the cloud, properly allocating game requests to the geographically distributed data centers (DCs) can lead to QoE improvements resulting from lower delays. In this paper, we propose a hierarchical Software Defined Network (SDN) controller architecture to near-optimally allocate a gaming session to a DC while minimizing network delay and maximizing bandwidth utilization. To do so, we formulate an optimization problem, and propose the Online Convex Optimization (OCO) as a practical solution. Simulation results indicate that the proposed method can provide close-to-optimal solutions, and outperforms classic offline techniques e.g. Lagrangean relaxation. In addition, the proposed model improves the bandwidth utilization of DCs, and reduces end-to-end delay and delay variation by gamers. As a byproduct, our proposed method also achieves better fairness among multiple competing players in comparison with existing methods.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".