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Record W2546673654 · doi:10.1109/ccece.2016.7726826

Towards virtualisation and secured software defined networking for wireless and cellular networks

2016· article· en· W2546673654 on OpenAlexaff
Chukwudi A. Ezefibe, Yousef R. Shayan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceSoftware-defined networkingComputer networkForwarding planeInteroperationNetwork virtualizationOpenFlowVirtualizationWireless networkCellular networkScalabilityDistributed computingNetwork architectureWirelessTelecommunicationsInteroperabilityCloud computingOperating system

Abstract

fetched live from OpenAlex

This paper centres on the study of concepts and applications of software defined networking (SDN) which is a network architecture that decouples the network control logic from the underlying network infrastructure elements. Separation of the control plane from the data plane is the basic principle of SDN. This gives a separation of functions which leads to better scalability, programmability, interoperation, performance and ease of management. SDN helps to keep up with the increasing demand for connectivity and bandwidth. This paper presents a review on how SDN can help drive development of network function virtualisation. Application of SDN to wireless and cellular data networks is also studied. Finally, vulnerabilities associated with SDN are discussed and recommendations are offered on how to make SDN more secure and reliable. A detailed architecture for a network function virtualised mobile cellular system is proposed as well as recommendations to improve security of these systems.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.590

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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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 designOther design
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

Citations3
Published2016
Admission routes1
Has abstractyes

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