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Record W1989110211 · doi:10.1109/mascot.2009.5366174

Scheduling issues in multi-channel wireless networks

2009· article· en· W1989110211 on OpenAlexaff
Mingwei Gong, C. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceWorkloadScheduling (production processes)QueueQueueing theoryPerformance metricDistributed computingComputer networkDynamic priority schedulingWirelessFair-share schedulingRound-robin schedulingReal-time computingMathematical optimizationQuality of serviceTelecommunications

Abstract

fetched live from OpenAlex

Scheduling decisions can have a pronounced impact on the performance of multi-radio wireless systems. In this paper, we study the effects of dispatch policies and queue scheduling strategies on the user-perceived performance for Internet traffic flows in a multi-channel WLAN. Our work is carried out using simulation and an empirical Web workload trace, with mean response time as the primary performance metric. The simulation results demonstrate the good/bad combination of the dispatch policy with queue scheduling strategy, the advantages of deferred dispatch over immediate dispatch, and the sensitivity of dispatch policies to heavy-tailed workload characteristics. The results also highlight the pros and cons of a simple lookahead scheduling policy, particularly in the presence of high variability workloads on a heterogeneous multi-channel system with random losses. Our results provide insights into efficient and robust scheduling policies for multi-channel WLANs.

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)0.003
metaresearch head score (Gemma)0.015
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.031
GPT teacher head0.300
Teacher spread0.269 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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