The Self-Managing Future Internet powered by the current IPv6 and extensions to IPv6 towards “IPv6++” — A viable roadmap Scenario for the Internet Evolution Path
Bibliographic record
Abstract
In this paper, we report on some perspectives we give on how to create a viable Evolution Path towards Self-Managing Future Internet via a “standardizable" and commonly-shared architectural Reference Model for Autonomic Network Engineering and Self-Management. We present a Scenario on how the Self-Managing Future Internet can be developed via a viable Evolution Path that starts with today's network models, architectures, protocols such as IPv6 (in particular) and paradigms. The scenario then goes on to define the incremental changes and concepts necessitated and guided by a unified, holistic, commonly-shared, architectural Reference Model for Autonomic Network Engineering and Self-Management that needs to be developed and standardized first, as a starting point to creating the Evolution Path towards the Self-Managing Future Internet. This evolution of today's network models, architectures, networking paradigms and protocols such as IPv6 (towards IPv6++) must be guided and necessitated by the architectural Reference Model. The Scenario is a “what-if” type of Scenario that presents solid and realistic steps that define an evolutionary roadmap to achieving a very advanced feature-rich Self-Managing Future Internet by 2015, which can continue to evolve beyond that time frame. The ongoing activities of the EC funded FP7-EFIPSANS Project (http://www.efipsans.org/) are geared towards this goal.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".