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Record W2009995450 · doi:10.1109/ccece.2012.6334815

Load balancing for QoS in Agile All-Photonic Networks

2012· article· en· W2009995450 on OpenAlexaff
Imad Khazali, Anjali Agarwal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer networkComputer scienceQuality of serviceStatic routingEqual-cost multi-path routingLoad balancing (electrical power)Distributed computingAdaptive quality of service multi-hop routingLink-state routing protocolTraverseNetwork packetRouting protocol

Abstract

fetched live from OpenAlex

A Quality of Service (QoS) routing architecture that balances Internet flow traffic with different QoS requirements in Agile All-Photonic Networks (AAPN) is presented. The architecture is based on the static and adaptive routing methods proposed previously for the AAPN network. The static routing method is based on the static Highest Random Weight (static HRW) used for designing load balanced web caches while the adaptive routing method is based on the adaptive Highest Random Weight (adaptive HRW) used for designing load balanced Internet routers. The architecture performance is investigated using traffic that belongs to two DiffServ traffic classes, namely: Expedite Forwarding (EF) that is sensitive to variations in end-to-end delay & traffic drop rate and Best Effort (BE) that can tolerate variations in end-to-end delay. Different routing methods are used to handle the two traffic classes: while the static routing method is used to route the EF traffic, the adaptive routing method is used route the BE traffic. The objective is to have EF packets that belong to the same flow traverse the same path and to preserve load balancing by remapping the BE flows when needed.

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: none
Teacher disagreement score0.805
Threshold uncertainty score0.453

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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