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Record W2122044701 · doi:10.1109/jsac.2004.839383

A novel signaling nested reservation protocol for all-optical networks

2005· article· en· W2122044701 on OpenAlexaff
A.V. Sichani, Hussein T. Mouftah

Bibliographic record

VenueIEEE Journal on Selected Areas in Communications · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer networkComputer scienceReservationNetwork packetResource Reservation ProtocolWavelength-division multiplexingBandwidth (computing)Protocol (science)ThroughputDistributed computingWirelessTelecommunicationsInternet protocol suiteThe InternetWavelength

Abstract

fetched live from OpenAlex

This work proposes a new reservation protocol for enhancing the performance of wavelength-routed networks. To be more robust and reliable, the proposed approach employs distributed control mechanisms. The new method particularly focuses on wavelength-division multiplexed (WDM) core networks with distant end-nodes. It takes into account the considerable amount of data that can be transferred by high-speed WDM networks within limited reservation periods. To increase the throughput, the protocol consumes the unoccupied bandwidth of reservation phases by transferring nonreal-time data packets during these intervals. This scheme is implemented by applying a modified form of backward reservation protocol. To initiate a multihop reservation call, this protocol labels a path as reserved instead of locking it. Meanwhile, labeled nodes with single-hop requests will receive permission signals to send predetermined packet sizes. The length of packets transmitted is defined by the round-trip propagation delay between the current and the upcoming nodes along the path. In case a reservation fails, already labeled nodes will be notified by receiving a prevention signal, which will block them from transferring data packets.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.350
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.343
Teacher spread0.275 · 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
GenreMethods

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

Citations3
Published2005
Admission routes1
Has abstractyes

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