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Record W2152428001 · doi:10.1109/qest.2006.23

Layered Bottlenecks and Their Mitigation

2006· article· en· W2152428001 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsCarleton University
Fundersnot available
KeywordsBottleneckComputer scienceDistributed computingQueueing theoryThread (computing)Scheduling (production processes)Resource (disambiguation)Computer networkEmbedded systemOperating systemEngineeringOperations management

Abstract

fetched live from OpenAlex

Bottlenecks are a simple and well-understood phenomenon in service systems and queueing models. However in systems with layered resources bottlenecks are more complicated, because of simultaneous resource possession. Thus, the holding time of a higher-layer resource, such as a process thread, may include a small execution demand, but a large time to use other resources at a lower layer (such as a disk). A single saturation point may in fact saturate many other resources by push-back, making diagnosis of the problem difficult. This paper gives a new corrected definition of a layered bottleneck, and develops a framework for systematic detection of the source of a bottleneck, for applying improvements and for estimating their effectiveness. Many of the techniques are specific to layered bottlenecks

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.136

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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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