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Record W4393261338 · doi:10.18280/mmep.110301

Unreliable Multi Server Retrial Queueing System with Reneging and Diverse Outgoing Services

2024· article· en· W4393261338 on OpenAlexvenueno aff
Saravanan Vadivel, Poongothai Venugopal, Godhandaraman Pakkirisamy

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAdvanced Queuing Theory Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsQueueing theoryComputer scienceQueueing systemComputer networkReal-time computingDistributed computing

Abstract

fetched live from OpenAlex

This article explores a M/M/m unreliable retrial queue with reneging and diverse outgoing services.Incoming calls that arrive and discover all servers occupied join the orbit.The buffering incomings from the orbit retry their request after a while or leave the system without receiving service.When the orbit becomes empty, the idle server provides outgoing services.It is assumed that there are two categories of outgoing services.Due to unexpected circumstances, the server may breakdown.When a server undergoes breakdown, immediate repair process begins.Post-server breakdown, incoming calls go into orbit and retry service randomly, whereas the two variants of outgoing calls leave the system.The study utilizes a quasi-birth-death (QBD) process to analyse the stationary system size distribution.The steady state probabilities and the rate matrix are obtained through the matrix geometric method (MGM).Various performance metrics are evaluated for the proposed model.The study examines the impact of various system-based parameters on efficiency metrics with the help of numerical results.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.017
GPT teacher head0.195
Teacher spread0.178 · 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
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
Published2024
Admission routes1
Has abstractyes

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