SDN-enabled Game-Aware Network Management for Residential Gateways
Bibliographic record
Abstract
Residential gateways play a key role in providing internet access to home consumers. Nowadays, users in the same home with heterogeneous applications share a common gateway. As such, the gateway becomes the bandwidth bottleneck, leading to impairments and negatively affecting users' Quality of Experience (QoE). In the case of delay sensitive applications like video streaming and online gaming, this impairment becomes more crucial. In this paper, we present an SDN-enabled optimization-based scheme for optimally sharing the bandwidth among network flows within a residential gateway. We target online game flows and try to provide them with a higher QoE while not starving other traffic flows. Our optimization model considers the nature of network flows, and aims to maximize the bandwidth utilization. Our experimental results show improvements in bandwidth efficiency by almost 15% and in fairness by almost 11.5% among active network flows compared to conventional methods. In addition, the proposed method minimizes the overall delay experienced by players by almost half, outperforming the commonly-used max-min fairness and Round Robin schemes.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".