iMark: An identity management framework for network virtualization environment
Bibliographic record
Abstract
Network virtualization has been propounded as an open and flexible future internetworking paradigm that allows multiple virtual networks (VNs) to co-exist on a shared physical substrate. Each VN in a network virtualization environment (NVE) is free to implement its own naming, addressing, routing, and transport mechanisms. While such flexibility allows fast and easy deployment of diversified applications and services, ensuring end-to-end communication and universal connectivity poses a daunting challenge. This paper advocates that effective and efficient management of heterogeneous identifier spaces is the key to solving the problem of end-to-end connectivity in an NVE. We propose iMark, an identity management framework based on a global identity space, which enables end hosts to communicate with each other within and outside of their own networks through a set of controllers, adapters, and well-placed mappings without sacrificing the autonomy of the concerned VNs. We describe the procedures that manipulate these mappings between different identifier spaces and provide performance evaluation of the proposed framework.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".