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Record W2145150838 · doi:10.1109/dsn.2013.6575312

Seamless kernel updates

2013· article· en· W2145150838 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
TopicSecurity and Verification in Computing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRebootComputer scienceKernel (algebra)Overhead (engineering)Operating systemLinux kernelState (computer science)InstallationSystem callDistributed computingRootkitMalwareProgramming language

Abstract

fetched live from OpenAlex

Kernel patches are released frequently to fix bugs and security vulnerabilities. However, users and system administrators often delay installing these updates because they require a system reboot, which results in disruption of service and the loss of application state. Unfortunately, the longer a system remains out-of-date, the higher is the likelihood of system failure or a successful attack. Approaches, such as dynamic patching and hot swapping, have been proposed for updating the kernel. All of them either limit the types of updates that are supported, or require significant programming effort to manage. We have designed a system that checkpoints application-visible state, updates the kernel, and restores the application state thus minimizing disruption of service. By checkpointing high-level state, our system no longer depends on the precise implementation of a patch and can apply all backward compatible patches. Our results show that updates to major releases of the Linux kernel can be applied with minimal effort and no observable overhead.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.999

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)0.0000.002

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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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

Quick stats

Citations14
Published2013
Admission routes1
Has abstractyes

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