Dynamic Routing with Online Traffic Estimation for Video Streaming over Software Defined Networks
Bibliographic record
Abstract
The traffic generated by video streaming applications constitutes a large portion of the Internet traffic carried over today’s networks. Video streaming demands low latency and high bandwidth. In particular, the transmission of high-quality (high-resolution) streaming video may put the network under pressure. Therefore, high-quality video traffic requires network managers to implement smart and fast routing decisions. Software Defined Networking (SDN) provides a global view and centralized control for the whole network which gives opportunities to dynamically manage networks. In this paper, we use an OpenFlow-based SDN environment and propose a dynamic routing scheme with online traffic estimation to increase the quality of high-quality video streaming and the throughput of the network. The traffic is clustered using an unsupervised machine learning algorithm, high-quality video flows are identified and routed over less congested paths. The whole design is tested in the Mininet simulator. Simulation results show that the proposed scheme improves the link utilization and reduces the amount of dropped frames as a result of excessive delay.
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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 | 0.000 | 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".