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Record W1497806656 · doi:10.1109/inm.2015.7140341

Introducing wireless access programmability using software-defined infrastructure

2015· article· en· W1497806656 on OpenAlexaff
Thomas Lin, Hadi Bannazadeh, Alberto Leon‐Garcia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkOpenFlowTestbedWireless networkFlexibility (engineering)WirelessMunicipal wireless networkNode (physics)Access networkWireless distribution systemHeterogeneous networkSoftware-defined networkingDistributed computingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Programmability in wireless access networks can provide unprecedented flexibility in meeting the communications needs of a diverse set of wireless devices under changing demand and network conditions. Programmability also holds the promise of enabling multiple simultaneous virtual operators providing a variety of access networks that offer different services using different business models. In this paper we present our work on enabling wireless access network programmability in the SAVI testbed. We introduce an architecture for Software-Defined Infrastructure that offers virtualized heterogeneous resources in support of services and applications. Central to this architecture is the Janus SDI resource manager that can coordinate the actions of a set of controllers, e.g. OpenStack, OpenFlow, and other controllers such as a wireless access controller. We describe a design where Janus is used to integrate the wireless access network into a Smart Edge node. We introduce use cases that exploit the flexibility offered by this design, and we present experimental results from our implementation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.040
GPT teacher head0.290
Teacher spread0.250 · 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 designSimulation or modeling
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

Citations2
Published2015
Admission routes1
Has abstractyes

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