Iterative traffic engineering in the data plane of multimedia IP communications
Bibliographic record
Abstract
The quality of multimedia communications heavily relies on the end-to-end network condition. Sub-optimal resource allocation in the substrate network may deplete certain links sooner, making the network less resilient to link failures, traffic fluctuation or random traffic spikes. Regulating the traffic can improve the media quality, but is not an easy operation to do in legacy IP networks. SDN technology provides the support for an unprecedented centralized solution in this regard. Thus the biggest challenge is how to allocate resources in the data plane. In this paper, we propose a Two-phase flow embedding approach with an iterative traffic engineering algorithm to address the resource allocation problem lying in the data plane of multimedia IP communication systems. We numerically evaluate it and compare it with (a) Dijkstra algorithm and (b) flow embedding approach with a greedy TE heuristics. We show that the flow embedding approach with the iterative TE excels in all three metrics: session accept rate, throughput, and link utilizations, under a spectrum of scenarios including unexpected network conditions and traffic conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".