Achieving near-optimal traffic engineering in hybrid Software Defined Networks
Bibliographic record
Abstract
Software Defined Networking (SDN) is an emerging networking paradigm which intends to merge networks into the age of the cloud, providing fine-grained control, simplified configurations, unprecedented flexibility and seamless scalability. However, due to the large set of unresolved challenges as well as the deployment cost, network evolution to fully SDN systems will take a long time. In fact, SDN elements are incrementally deployed in enterprise networks, producing a transitional network form of hybrid SDN (H-SDN). An H-SDN system consists of traditional networking elements and SDN elements, accommodating both conventional traffic and SDN traffic. In this paper, we investigate traffic engineering (TE) in H-SDN, where the SDN controller strategically routes SDN traffic so as to optimize the TE performance over all network links shared with uncontrollable conventional traffic. Two hybrid modes are studied: (1) the barrier mode, where the two forms of traffic are routed in separated capacity spaces; and (2) the hybrid mode, where each link can be fully occupied by either form of traffic. We propose fast algorithms for the TE problems in both scenarios with provable approximation guarantees. Theoretical analysis and computer simulations validate the efficacy of our algorithms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".