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Record W1801856935 · doi:10.1109/hpsr.2002.1024201

Criticality avoidance routing in dynamic wavelength-routed optical networks

2003· article· en· W1801856935 on OpenAlexaff
Pin-Han Ho, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceComputer networkDistributed computingRouting protocolNode (physics)Asynchronous communicationDynamic Source RoutingWavelength-division multiplexingRouting (electronic design automation)CriticalityRouting and wavelength assignmentStatic routingLink-state routing protocolEngineeringWavelength

Abstract

fetched live from OpenAlex

This paper solves the problem of path selection for WDM mesh networks with a special focus on the implementation in middle-sized networks, such as metropolitan-area networks (MANs). A novel routing and signaling protocol, called asynchronous criticality avoidance (ACA), is proposed to improve the network performance. With the ACA protocol, a specific set of wavelength channels are defined as "critical links" between a node pair according to dynamic link-state. The critical links and the associated link-states are also called criticality information, which are coordinated and disseminated by each source node to every other destination node as an inter-arrival planning. Routing and wavelength assignment is initiated and performed at the corresponding destination node, in which a criticality avoidance mechanism is devised to take the criticality information into consideration. Simulation is conducted in 22- and 30-node networks to examine the proposed approach. The simulation results show that the ACA protocol significantly outperforms the fixed-path least-congested (FPLC) and adaptive dynamic routing (ADR) schemes under the fixed alternate routing architecture. We conclude that the proposed ACA protocol can effectively reduce the resource fragmentation in wavelength-routed WDM networks without wavelength conversion.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.008
GPT teacher head0.230
Teacher spread0.223 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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