Comparing OpenFlow and NETCONF when interconnecting data centers
Bibliographic record
Abstract
SDN technology has been applied to a range of different networks, ranging from Ethernet services to large cloud environments. More recently, interest has turned towards extending programmability of Optical Transport Networks (OTN). In the SDN architecture, SBIs are used to communicate between the SDN controller and the switches or routers in the network. In this paper, we deploy OpenFlow and NETCONF as SBIs in managing BoD across interconnected data centers over OTN. More specifically, we use these protocols to communicate between an OpenDayLight controller and two BTI7800 network elements that interconnect the data centers. We present experimental results for both BTI's YANG-based NETCONF implementation and our port of OpenFlow for a number of use cases. Our results show that NETCONF is faster and requires fewer control message. However, OpenFlow offers better bandwidth utilization over the interconnecting link.
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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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".