A generic framework to support application-level flow management in software-defined networks
Bibliographic record
Abstract
Software-Defined Networking (SDN) provides a highly flexible flow management platform through a logically centralized controller that exposes network capabilities to the applications. However, most applications do not natively use SDN. An external entity is thus responsible for defining the corresponding flow management policies. Usually network operators prefer to control the flow management policies, rather than granting full control to the applications. Although IP addresses and port numbers can suffice to identify users and applications in ISP networks and determine the policies applicable to their flows, such an assumption does not hold strongly in cloud environments. IP addresses are allocated dynamically to the users, while port numbers can be freely chosen by users or cloud-based applications. These applications, like computing or storage framework, use diverse port numbers which amplifies this phenomenon. This paper introduces higher-level abstractions for defining user- and application-specific policies. These policies are then automatically mapped to OpenFlow rules by retrieving flow-based information of active users and applications in real-time. We implemented this framework and evaluated its practicality by measuring the underlying overhead.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".