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Record W2019339271 · doi:10.1177/1094342003173005

Scalable Bulk Data Transfer in Wide Area Networks

2003· article· en· W2019339271 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Journal of High Performance Computing Applications · 2003
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsnot available
FundersUniversity of TorontoWestern Michigan UniversityHuazhong University of Science and TechnologyUniversity of BahrainNational Science Foundation
KeywordsComputer scienceScalabilityDistributed computingBandwidth (computing)Wide area networkComputer networkInterface (matter)Network interfaceNetwork architectureFlexibility (engineering)Network traffic controlOperating system

Abstract

fetched live from OpenAlex

Bulk data transfer in wide area networks (WAN) requires scalable and high network bandwidth. In this paper, we identify a number of the scalability limitations that affect the full utilization of peak theoretical network bandwidth. In addition, we study and classify different offered approaches to overcome some of the identified limitations and increase network bandwidth among Grid components in WAN. With these limitations in mind, we study and evaluate the scalability and flexibility of a UDP-based multiple-network-interface socket (MuniSocket) model in WAN. The MuniSocket model is a middleware layer between the distributed applications and the multiple networks and system resources available. MuniSocket utilizes existing system resources, network interface cards, and network links to provide a scalable, reliable and high bandwidth network solution for data-intensive distributed applications, thus eliminating most of the identified limitations.

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.

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)0.002
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 score0.835
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0040.000
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 head0.026
GPT teacher head0.261
Teacher spread0.234 · 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