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Interrupt Related Interference Bounding in Aerospace Multicore Systems

2025· article· W4416924323 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
Language
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsCégep de Saint-LaurentPolytechnique Montréal
Fundersnot available
KeywordsInterruptInterrupt handlerMulti-core processorBounding overwatchOverhead (engineering)Interference (communication)

Abstract

fetched live from OpenAlex

The transition to multicore architectures in safetycritical systems brings many challenges. The contention on shared components generates unpredictable timing hazards called interference. Interrupts are inherently unpredictable and add even more timing uncertainty to the system. In the case of interrupt storms, the core handling the interrupts will significantly affect the execution of other cores. In this paper, we study and quantify interrupt interference and propose a novel interrupt manager to bound and monitor interrupt-related interference. Our approach, implemented on a commercial RTOS, offers a low worst-case execution time overhead of 1.01 % while increasing applications' predictability by 84.1 % and reducing the measured worst-case execution times by 5.9 % in presence of interrupt storms.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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