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Record W2000475884 · doi:10.1109/iccnc.2012.6167539

P2P streaming; Impact of bandwidth throttling on QoS

2012· article· en· W2000475884 on OpenAlexaff
Jalal Almhana, Zikuan Liu

Bibliographic record

Venue2012 International Conference on Computing, Networking and Communications (ICNC) · 2012
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsBandwidth throttlingComputer scienceComputer networkBandwidth (computing)Quality of serviceThe InternetInternet trafficProvisioningMobile QoSBandwidth allocationInternet accessService (business)Service providerWorld Wide WebEngineeringBusiness

Abstract

fetched live from OpenAlex

Internet subscribers and services have grown even during the recent recession. As a result, Internet traffic volume and content are changing constantly. These changes increase pressure on Internet Service Providers (ISPs) by making bandwidth provisioning and management difficult, especially to maintain required Quality of Service (QoS) levels. Peer to Peer (P2P) traffic associated with File Sharing Applications (FSA) is the most influential factor in changing Internet traffic content and volume. Traffic data for 900 subscribers to an ISP was collected over two months in a period before the rapid growth of P2P. In this paper we study the potential effect of P2P video streaming, under both restricted (Bandwidth Throttling) and unrestricted conditions, on the QoS experienced by subscribers without high P2P usage. Under unrestricted conditions, our results show that QoS for these subscribers will be substantially degraded by this kind of P2P traffic; however, when ISPs exercise bandwidth throttling to shape traffic, the QoS can be considerably improved.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.359
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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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