Non-Real-Time Network Traffic in Software-Defined Networking: A Link Bandwidth Prediction-Based Algorithm
Bibliographic record
Abstract
Network traffic control is the process of managing, prioritizing, controlling or reducing the network traffic by the network scheduler. High utilization of link bandwidth is very significant for network control and maintenance in Software-Defined Networking (SDN). When we get the accurate link bandwidth predictions for T time periods of the future in a specific network topology, the residual link bandwidth could be determined by the link bandwidth capacity and corresponding prediction values. Given the non-real-time request pairs, this process can be transformed into a multi-commodity flow model. But the traditional multi-commodity model has not introduced the time dimension. In this paper, the model associated with the time dimension is to complete the transmission of the non-real-time network traffic. However, in consideration of the large scale of the problem, a heuristic algorithm on the basis of greedy strategy is proposed to schedule the non-real-time network traffic properly. The experiments show that the heuristic algorithm is superior to global optimization in computing speed and the single path resulting from heuristic algorithm occupies fewer links in the network topology for the non-real-time network traffic.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".