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Record W2018195068 · doi:10.1145/1188966.1189007

Hybrid vs. monolithic OS kernels

2006· article· en· W2018195068 on OpenAlexaff
Dylan Griffiths, Dwight Makaroff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMicrokernelComputer scienceBenchmark (surveying)Embedded systemOperating systemKernel (algebra)Software deploymentWorkstationModularity (biology)Process (computing)Code (set theory)Computer architectureSoftware

Abstract

fetched live from OpenAlex

Operating Systems are huge, complex pieces of software that are difficult to design and maintain in both security and functionality. Microkernel-based operating system designs reduce implementation complexity and increase code modularity, but have had serious performance drawbacks. The increase in the speed of both processing and memory access has led some to reconsider the relative advantages and disadvantages in microkernel design [5].In this paper, we investigate the performance of modern, widely-used workstation operating systems using a standard process-based benchmark. The experiments were executed on commodity hardware and the results indicate that hardware characteristics dominate the difference in the kernel architecture, lending support to the idea that microkernels may be poised to gain widespread deployment.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.310

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 science0.0010.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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