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Record W2060137540 · doi:10.1109/atc.2008.4760501

The state-of-the-art of on-demand P2P media streaming over the Internet

2008· article· en· W2060137540 on OpenAlexaff
Son T. Vuong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceScalabilityServerReal Time Streaming ProtocolThe InternetScheduling (production processes)Computer networkOverlayOn demandBandwidth (computing)Internet trafficMultimediaWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

On-demand media streaming has gained considerable attention in recent years owing to its promising usage in a rich set of Internet-based services such as movies on demand and distance learning. However, the current solutions for on-demand media streaming, primarily based on the client-server model, are not economical and not scalable due to the high cost in deploying the servers and the heavy media traffic overload on the servers. We propose a scalable cost-effective P2P solution, called Bit-Vampire, that aggregates the peerspsila storage and bandwidth to facilitate on-demand media streaming. In this talk, we will discuss the state-of-the-art of P2P on-demand video streaming and in particular present a novel category overlay P2P architecture that enables efficient content search, called COOL (CategOry OverLay) search, and the concept of Bit-Vampire that makes use of COOL search and a scheduling algorithm for efficient concurrent media streaming from multiple peers. Other related practical issues such as efficient pre-release distribution for flash crowd congestion avoidance are also discussed. A movie streaming demo is also presented to illustrate the application of Bit-Vampire that will enable the ldquoInternet DVD playerrdquo technology in the future.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.483

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 science0.0030.001
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 head0.015
GPT teacher head0.225
Teacher spread0.209 · 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 designNot applicable
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
Published2008
Admission routes1
Has abstractyes

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