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Record W1745487936 · doi:10.1109/icii.2001.983555

Control architecture of multiple-hop connections in IP over WDM networks

2002· article· en· W1745487936 on OpenAlexaff
Jing Wu, Hussein T. Mouftah, Delfin Y. Montuno

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsQueen's UniversityNortel (Canada)
Fundersnot available
KeywordsComputer networkComputer scienceMultiprotocol Label SwitchingNetwork traffic controlOptical IP SwitchingQuality of serviceTraffic engineeringNetwork packetLabel switchingDistributed computingNetwork architectureTraffic groomingWavelength-division multiplexingInternet ProtocolThe Internet

Abstract

fetched live from OpenAlex

In the existing functional definition of the optical network, the optical network primarily offers high bandwidth connectivity in the form of optical layer connections, where a connection is defined to be a fixed bandwidth circuit between two user network elements (also called clients). We propose to add IP routers packet processing function into the data plane of optical core networks, in order that transport service providers can provide multiple-hop connections between ISPs in addition to single-hop-only point-to-point lightpaths. Their benefits include optimized utilization of the bandwidth of lightpaths, more means to add and drop IP traffic, and flexibility of traffic engineering operations. We extend the functional models for IP-optical network interaction in the context of multiple-hop-enabled IP over WDM networks. The unified control and management supported by generalized MPLS is one of the underpinnings of the evolution from single-hop-only optical networks to multiple-hop-enabled IP over WDM networks. In order to support multiple-hop connections, we propose that the requests of connection establishment in the UNI should be extended to include the bandwidth and IP layer QoS requirements. Finally, the enhanced traffic engineering for transport service providers is discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.444

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 head0.008
GPT teacher head0.196
Teacher spread0.189 · 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
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

Citations0
Published2002
Admission routes1
Has abstractyes

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