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Record W2160155126 · doi:10.1109/jlt.2012.2183576

Green Packet Optical Transport Networks (P-OTNs) Based on Photonic PBB-TE Switches and Minimized EEE Overhead

2012· article· en· W2160155126 on OpenAlexaff
Mohammad Nurujjaman, Mehdi Sharifi-Rayeni, Chadi Assi, Martin Maier

Bibliographic record

VenueJournal of Lightwave Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsInstitut National de la Recherche ScientifiqueConcordia University
Fundersnot available
KeywordsCarrier EthernetComputer networkConnection-oriented EthernetComputer scienceEthernetMultiprotocol Label SwitchingEthernet over SDHNetwork packetQuality of serviceEthernet flow control

Abstract

fetched live from OpenAlex

Carrier Ethernet exhibits an enormous potential to be a cost-effective and less complex replacement of SONET/SDH especially after the ratification of IEEE standard 802.1Qay Provider Backbone Bridge-Traffic Engineering (PBB-TE). The recent IEEE standard 802.3az Energy Efficient Ethernet (EEE) presents another opportunity for service providers to select Ethernet as a technology of choice in the backbone while leveraging on its promise for achieving green transport network. In this paper, we propose two novel architectures of photonic PBB-TE core and edge switches, which enhance the usability of PBB-TE networks by reducing power consumption in individual switches in conjunction with passive optical bypassing and EEE. We also formulate the problem of energy-aware scheduling as an optimization problem whose objective is to minimize the overall energy consumption for transmitting Ethernet frames while satisfying their delay requirements. This model will be used as a benchmark while evaluating the performance of packet coalescing, a promising and recently proposed approach, as well as the performance of EEE.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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.

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
Published2012
Admission routes1
Has abstractyes

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