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Record W2133740904 · doi:10.1109/wcnc.2003.1200662

Performance evaluations of SRMTP for reliable multicasting over satellite networks

2004· article· en· W2133740904 on OpenAlexaff
Chen Wang, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMulticastComputer scienceComputer networkReliable multicastProtocol Independent MulticastProtocol (science)SatelliteDisseminationDistributed computingThe InternetCommunications satelliteData transmissionBroadbandIP multicastTransmission (telecommunications)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A wide variety of network applications require the use of reliable multicast protocols to disseminate data from one source to a potentially large number of receivers simultaneously. Broadband satellite is an ideal transmission medium to support such applications. Although several reliable multicast protocols have been proposed for the Internet, they are not optimized for satellite networks, and the only known protocol developed specifically for reliable multicasting over satellite networks is MFTP. In this paper, we propose a novel set of window-based satellite reliable multicast transport protocols (SRMTPs) for bulk data transfer over broadband satellite networks, with and without the aid of satellite onboard processing (OBP) and buffering (OBB). These protocols employ a judicious combination of negative and positive acknowledgment sent over the shared return satellite channel in a round-robin fashion. Performance evaluation by simulations shows that SRMTP generally outperforms MFTP, especially when it is enhanced by OBP and OBB.

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 score0.881
Threshold uncertainty score0.293

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 science0.0000.000
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 head0.024
GPT teacher head0.286
Teacher spread0.262 · 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

Citations6
Published2004
Admission routes1
Has abstractyes

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