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Record W2160352517 · doi:10.1109/saconet.2014.6867766

A new mechanism for SDN network virtualization service

2014· article· en· W2160352517 on OpenAlexaff
Changcheng Huang, Jiafeng Zhu, Min Luo, Wu Chou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsCarleton University
Fundersnot available
KeywordsNetwork virtualizationService providerComputer scienceComputer networkVirtualizationSoftware-defined networkingApplication service providerService (business)Cloud computingService designBusinessOperating system

Abstract

fetched live from OpenAlex

Software Defined Network (SDN) is becoming a new paradigm for both WAN and enterprise networks. By separating control and data planes, SDN allows network providers to sell new services without changing their physical switches. One of the key services SDN can enable is network virtualization which allows a service provider to have a virtual slice of a network provider's physical network. While this feature empowers service providers to have dynamic networks, it is becoming more difficult for a network provider to statistically share its resource usage across multiple service providers due to the fact that service providers are reluctant to share their user information. This paper presents a new scheme that empowers network provider to maximize statistical resource sharing with service guarantee while minimizing information shared.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.646
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.222
Teacher spread0.208 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations4
Published2014
Admission routes1
Has abstractyes

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