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Record W2071864257 · doi:10.1080/15326349.2011.614187

Goodput Analysis Using Terminating MAP for a Class of Discrete-Time Queueing Models

2011· article· en· W2071864257 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

VenueStochastic Models · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAdvanced Queuing Theory Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGoodputQueueing theoryPlatoonMarkovian arrival processNetwork packetLayered queueing networkComputer scienceQueueThroughputMarkov processBulk queueComputer networkSynchronizingReal-time computingDistributed computingMathematicsStatisticsControl (management)Transmission (telecommunications)TelecommunicationsWirelessArtificial intelligence

Abstract

fetched live from OpenAlex

We develop an approach for computing the goodput using the idea of terminating Markovian Arrival Process (tMAP) for a finite queueing system in discrete-time with Platoon Arrival Process (PAP). A platoon is said to have been successfully transmitted if all of its packets have been admitted into the queue without any loss. Otherwise, the transmitted packets from the unsuccessful platoon would constitute wasted work. The throughput rate of the non-wasted work is called “goodput.” We apply the idea to modeling a queueing system with packet discarding policies in high speed networks—a system of considerable interest in networking.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.048
GPT teacher head0.261
Teacher spread0.214 · 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