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Record W7097828912

Resource Sharing of High-Speed Optical Circuits for File Transfers

2008· article· en· W7097828912 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSynchronous optical networkingElectronic circuitGigabitShared resourceProvisioningResource (disambiguation)EthernetGigabit EthernetMultiplexing
DOInot available

Abstract

fetched live from OpenAlex

A number of efforts are underway to use optical networks to create high-speed end-to-end circuits. These circuits can be used for transfers of large files such as those generated by large-scale scientific applications. At the present time, gigabit and 10-gigabit Ethernet signals from end hosts are typically mapped onto metro-/wide-area SONET circuits using Ethernet-over-SONET (EoS) technologies. While SONET circuits are realized by configuring electronic circuit switches, extension of this concept to all-optical circuits will be straightforward when all-optical switches become commonplace. A few hero demonstrations have shown that 1-Gb / s circuits can be configured from hosts in Europe to hosts in Canada/US to transfer files at high speeds over these circuits. Efforts targeted at these demonstrations have focused on the implementation of two pieces of software: (i) control-plane modules to provision end-to-end circuits (referred to as “user-controlled lightpaths ” in [1]), and (ii) transport protocols, such as SABUL [2], Tsunami [3], and RBUDP [4] suitable for these end-to-end circuits. While the provisioning of such circuits is feasible on experimental research networks such as Starlight, Canarie, UKlight, and SURFnet, we need to address the issue of resource sharing if this concept is to be deployed economically on a wide basis. In a paper presented in PFLDN

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 score0.499
Threshold uncertainty score0.428

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 head0.021
GPT teacher head0.213
Teacher spread0.193 · 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
Published2008
Admission routes1
Has abstractyes

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