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Record W2029910757 · doi:10.1109/glocomw.2010.5700381

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

2010· article· en· W2029910757 on OpenAlexaff
Ranganai Chaparadza, Symeon Papavassiliou, Said Soulhi, Jianguo Ding

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsEricsson (Canada)
FundersNational Technical University of Athens
KeywordsIPv6The InternetComputer scienceIPv6 addressNetwork managementDistributed computingWorld Wide WebComputer network

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.232
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2010
Admission routes1
Has abstractyes

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