A multi-service multi-role integrated information model for dynamic resource discovery in virtual networks
Bibliographic record
Abstract
Network virtualization is considered as a promising way to overcome the limitations and fight the gradual ossification of the current Internet infrastructure. The network virtualization concept consists in the dynamic creation of several co-existing logical network instances (or virtual networks) over a shared physical network infrastructure. One of the challenges associated with this concept is the dynamic discovery and selection of virtual resources that can be composed to form virtual networks. To achieve that task, there is a need for a formal and expressive information model facilitating information representation and sharing between the various roles/entities involved. We have previously proposed a service-oriented hierarchical business model for virtual networking environments, as well as an architecture enabling its realization. In this paper, we build on this business model and architecture by proposing a multiservice, multi-role hierarchical information model, for virtual networking environments. Furthermore, we demonstrate the usage of this information model using a secure content distribution scenario that is realized using REST interfaces. Unlike other proposals, our integrated information model enables the fine-grained description of virtual networks and virtual networking resources, in addition to the modeling of network services and roles, and their relationships and hierarchy.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".