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Record W2103375672 · doi:10.1109/glocom.2006.320

NXG01-3: Rate Allocation under Network End-to-End Quality-of-Service Requirements

2006· article· en· W2103375672 on OpenAlex

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 · 2006
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceQuality of serviceEnd-to-end principleComputer networkProbabilistic logicEnd-to-end delayBandwidth (computing)Bandwidth allocationQueueNetwork delayQueuing delayMathematical optimizationNetwork packetDistributed computing

Abstract

fetched live from OpenAlex

We address the problem of allocating transmission rates to a set of network sessions with end-to-end bandwidth and delay requirements. We give a unified convex programming formulation that captures both average and probabilistic delay requirements. Moreover, we present a distributed algorithm and establish its convergence to the global optimum of the overall rate allocation problem. In our algorithm, session sources update their rates as to maximize their individual benefit (utility minus bandwidth cost), the network partitions end-to-end delay requirements into local per-link delays, and the links adjust their prices to coordinate the sources' and network's decisions, respectively. This algorithm relies on a network utility maximization approach, and can be viewed as a generalization of TCP and queue management algorithms to handle end-to-end QoS. We also extend our results to deterministic delay requirements when nodes employ packet- level generalized processor sharing (PGPS) schedulers.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.030
GPT teacher head0.279
Teacher spread0.249 · 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