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Record W2162968606 · doi:10.1109/comst.2000.5340798

An overview of pricing concepts for broadband IP networks

2000· article· en· W2162968606 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

VenueIEEE Communications Surveys & Tutorials · 2000
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceQuality of servicePricing strategiesAdmission controlNetwork congestionVariable pricingComputer networkBroadbandDynamic pricingOperations researchTelecommunicationsMicroeconomicsEconomics

Abstract

fetched live from OpenAlex

In this article we provide an overview of pricing concepts for broadband multiservice networks. We review the notions of flat pricing, priority pricing, Paris-Metro pricing, smart-market pricing, responsive pricing, expected capacity pricing, edge pricing, and effective bandwidth pricing. We use numerous evaluation criteria, including network, economic, and social efficiency, as well as their suitability in using pricing as a means for congestion control. Some of the schemes are based on best-effort networks, and are thus unable to provide the user with quality of service (QoS) guarantees. Others build on networks with connection admission control functions and are thus able to provide individual QoS guarantees. We particularly investigate the relevant time frame over which pricing schemes are assumed to operate. The majority of the schemes work on short time frames (on the order of minutes), which makes them applicable to use pricing as an additional means for controlling congestion. We also consider technical aspects such as compliance with existing networking technologies or computational overheads associated with billing and accounting.

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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0030.000
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 head0.088
GPT teacher head0.365
Teacher spread0.277 · 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