MétaCan
Menu
Back to cohort
Record W2121457996 · doi:10.1109/iscc.2006.63

Dynamic Constrained Multicast Routing in WDM Networks: Blocking Probability, QoS and Traffic Engineering

2006· article· en· W2121457996 on OpenAlexaff
Tun Hu, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMulticastComputer scienceComputer networkBlocking (statistics)Quality of serviceBandwidth (computing)Wavelength-division multiplexingXcastRouting (electronic design automation)Traffic groomingProtocol Independent MulticastTraffic engineeringDistributed computingWavelength

Abstract

fetched live from OpenAlex

The development of bandwidth-demanding IP multicast applications has made supporting multicasting in optical layer a favorite option. This article examines the dynamic behavior of optical layer multicasting in sparse splitting WDM networks. We proposed a routing algorithm incorporating the Member-only and shortest-widest approaches to achieve the objectives of Quality of Service (QoS) and traffic engineering in dynamic environment, assuming reasonable blocking probability. A Bottle-neck First-Fit wavelength assignment approach was introduced and applied. The study compared the simulation results of the proposed algorithm to that from the shortest-path based Member-only approach. It showed that the proposed algorithm balances traffic loads well, and accommodates more connection requests in low and medium loads. We further investigated the effects of limiting wavelength usage for each forest on overall blocking probability. Moreover, we formally proved that the algorithm can be easily extended to meet the bandwidth requirement.

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: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.954

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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Optical Network TechnologiesFrench-language works237,207