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Record W2160629578 · doi:10.1364/jon.5.001056

Prioritized retransmission in slotted all-optical packet-switched networks

2006· article· en· W2160629578 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Optical Networking · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRetransmissionComputer networkNetwork packetComputer scienceThroughputPacket switchingReal-time computingTelecommunicationsWireless

Abstract

fetched live from OpenAlex

Feature Issue on Photonics in SwitchingWe consider an all-optical slotted packet-switched network interconnected by a number of bufferless all-optical switches with contention-based operation. One approach to reduce the cost of the expensive contention resolution hardware could be retransmission in which each ingress switch keeps a copy of the transmitted traffic in the electronic buffer and retransmits whenever required. The conventional retransmission technique may need a higher number of retransmissions until traffic passes through the network. This in turn may lead to a retransmission at a higher layer and reduce the network throughput. In this paper, we propose and analyze a simple but effective prioritized retransmission technique in which dropped traffic is prioritized when retransmitted from ingress switches so that the core switch can process them with a higher priority. We present the analysis of both techniques in multifiber network architecture and verify it via simulation to demonstrate that our proposed algorithm can limit the number of retransmissions significantly and can improve TCP throughput better than the conventional retransmission technique.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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