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Record W2154484410 · doi:10.1109/glocom.2005.1577759

End-to-end delay satisfaction balancing routing

2005· article· en· W2154484410 on OpenAlex
Mohamed Ashour, Tho Le‐Ngoc

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

VenueGLOBECOM '05. IEEE Global Telecommunications Conference, 2005. · 2005
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceNetwork delayRouting (electronic design automation)Static routingLoad balancing (electrical power)Equal-cost multi-path routingDestination-Sequenced Distance Vector routingEnd-to-end delayComputationMathematical optimizationDistributed computingLink-state routing protocolComputer networkAlgorithmRouting protocolMathematics

Abstract

fetched live from OpenAlex

This paper presents QoS-based routing algorithms using the end-to-end delay satisfaction balancing concept. The algorithms are suitable for networks that do not use per-path reservation. A nonlinear optimization problem and a gradient-based solution are formulated for off-line computation of the optimal route configuration. An approximation of the optimization problem is developed for on-line distributed processing. Using the approximation, vector routing tables can be used to set-up paths for arriving calls. Performance of the proposed schemes is evaluated and compared with that of minimum-delay, minimum-hop, and min-interference routing algorithms. Results show that using an objective function based on delay satisfaction balancing enables the network to accommodate more users of varying end-to-end delay requirements.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0080.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.033
GPT teacher head0.307
Teacher spread0.274 · 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