HyperFlow: a distributed control plane for OpenFlow
Bibliographic record
Abstract
OpenFlow assumes a logically centralized controller, which ideally can be physically distributed. However, current deployments rely on a single controller which has major drawbacks including lack of scalability. We present HyperFlow, a distributed event-based control plane for OpenFlow. HyperFlow is logically centralized but physically distributed: it provides scalability while keeping the benefits of network control centralization. By passively synchronizing network-wide views of OpenFlow controllers, HyperFlow localizes decision making to individual controllers, thus minimizing the control plane response time to data plane requests. HyperFlow is resilient to network partitioning and component failures. It also enables interconnecting independently managed OpenFlow networks, an essential feature missing in current OpenFlow deployments. We have implemented HyperFlow as an application for NOX. Our implementation requires minimal changes to NOX, and allows reuse of existing NOX applications with minor modifications. Our preliminary evaluation shows that, assuming sufficient control bandwidth, to bound the window of inconsistency among controllers by a factor of the delay between the farthest controllers, the network changes must occur at a rate lower than 1000 events per second across the network. 1.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".