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Record W2005595899 · doi:10.1109/ccece.2006.277658

Service Overlay Network Resource Adaptations Based on an Economic Model

2006· article· en· W2005595899 on OpenAlexafffund
Côn Tran, Zbigniew Dziong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceComputer networkQuality of serviceOverlay networkAdmission controlThe InternetProfit (economics)Voice over IPOverlayBandwidth (computing)RevenueDistributed computingWorld Wide Web

Abstract

fetched live from OpenAlex

In the current Internet, service overlay networks (SON) can provide a means for offering end-to-end quality of service (QoS) required by real time services such as VoIP, streaming multimedia and interactive games. We consider the approach where the SON operator leases bandwidth with QoS guarantees for the overlay links from Internet autonomous systems. Available bandwidth is managed by the SON admission and routing policy to provide end-to-end QoS connections to the service user. Maximizing profit is a key objective for the SON operator. In this paper, we propose a novel resource management approach which uses an economic model, including costs and revenues, to drive resource adaptations to changing network conditions, so that network profit can continuously be optimized. The approach integrates link capacity adaptations with connection admission control and routing policies based on Markov decision process theory. Numerical analysis results on a network example shows that the approach is effective at attaining maximized profit under varying network conditions

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: Methods · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.490

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 science0.0010.000
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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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
GenreMethods

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
Published2006
Admission routes2
Has abstractyes

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